MuHBoost: Multi-Label Boosting For Practical Longitudinal Human Behavior Modeling
Authors: Nguyen T Thach
Patrick Habecker
Anika R. Eisenbraun
W. Alex Mason
Kimberly A. Tyler
Bilal Khan
Hau Chan
Venue: ICLR 2025 Poster (2025)
Keywords: AI for Public Health
large language models
heterogeneous time-series classification
multi-label classification
Primary area: other topics in machine learning (i.e., none of the above)
TL;DR: We develop a novel method for longitudinal human behavior modeling that leverages LLMs in a boosting framework to perform multi-label classification of multiple health or well-being outcomes.

Abstract: Longitudinal human behavior modeling has received increasing attention over the years due to its widespread applications to patient monitoring, dietary and lifestyle recommendations, and just-in-time intervention for at-risk individuals (e.g., problematic drug users and struggling students), to name a few. Using in-the-moment health data collected via ubiquitous devices (e.g., smartphones and smartwatches), this multidisciplinary field focuses on developing predictive models for certain health or well-being outcomes (e.g., depression and stress) in the short future given the time series of individual behaviors (e.g., resting heart rate, sleep quality, and current feelings). Yet, most existing models on these data, which we refer to as ubiquitous health data, do not achieve adequate accuracy. The latest works that yielded promising results have yet to consider realistic aspects of ubiquitous health data (e.g., containing features of different types and high rate of missing values) and the consumption of various resources (e.g., computing power, time, and cost). Given these two shortcomings, it is dubious whether these studies could translate to realistic settings. In this paper, we propose MuHBoost, a multi-label boosting method for addressing these shortcomings, by leveraging advanced methods in large language model (LLM) prompting and multi-label classification (MLC) to jointly predict multiple health or well-being outcomes. Because LLMs can hallucinate when tasked with answering multiple questions simultaneously, we also develop two variants of MuHBoost that alleviate this issue and thereby enhance its predictive performance. We conduct extensive experiments to evaluate MuHBoost and its variants on 13 health and well-being prediction tasks defined from four realistic ubiquitous health datasets. Our results show that our three developed methods outperform all considered baselines across three standard MLC metrics, demonstrating their effectiveness while ensuring resource efficiency.

Published as a conference paper at ICLR 2025
MUHBooST: MULTI-LABEL BOOSTING FOR PRACTI- CAL LONGITUDINAL HUMAN BEHAVIOR MODELING
Nguyen Thach", Patrick Habeckerl, Anika Eisenbraun", Alex Mason
Kimberly Tyler?, Bilal Khan & Hau Chan
ABSTRACT
Longitudinal human behavior modeling has received increasing attention over the years due to its widespread applications to patient monitoring, dietary and lifestyle recommendations, and just-in-time intervention for at-risk individuals (e.g.. prob- lematic drug users and struggling students), name a few. Using in-the-moment health data collected via ubiquitous devices (e.g., smartphones and smartwatches), this multidisciplinary field focuses developing predictive models for certain health or well-being outcomes (e.g., depression and stress) in the short future given the time series of individual behaviors (e.g., resting heart rate, sleep quality, and current feelings). Yet, most existing models on these data, which we refer to as ubiquitous health data, do not achieve adequate accuracy. The latest works that yielded promising results have yet to consider realistic aspects of ubiquitous health data (e.g., containing features of different types and high rate of missing values) and the consumption of various resources (e.g., computing power, time, and cost). dubious whether these studies could translate to realistic settings. In this paper, propose MuHBoost, a multi-label boosting method for addressing these shortcomings, by leveraging advanced methods large language model (LLM) prompting and multi-label classification (MLC) jointly predict multiple health or well-being outcomes. Because LLMs can hal- lucinate when tasked with answering multiple questions simultaneouslyy, develop two variants MuHBoost that alleviate this issue and thereby enhance its predictive performance. We conduct extensive experiments evaluate MuH- Boost and its variants on 13 health and well-being prediction tasks defined from four realistic ubiquitous health datasets. Our results show that our three developed methods outperform all considered baselines across three standard MLC metrics, demonstrating their effectiveness while ensuring resource efficiency.
Given these two shortcomings,
also
INTRODUCTION
Longitudinal human behavior modeling is an emerging field of study that spans psychology, human- computer interaction, ubiquitous computing, and machine learning (ML). The first three disciplines enable the collection of longitudinal health data (e.g., resting heart rate, sleep quality, and current feelings) from individuals over an extended period of time in their natural environment. Then, ML techniques are utilized to develop predictive models for some relevant health or well-being outcomes (e.g., depression (Chikersal al. 2021) and atrial fibrillation (Chen et al. 2022 ) in the short future. With the advances in mobile and wearable technologies (c.g.. smartphones and smartwatches) that facilitate ubiquitous data collection, this multidisciplinary field has widespread implications: from delivering various healthcare services for patients such as chronic disease monitoring, fitness and gait analysis, and obesity management (Saleemi et al. 2023), to developing just-in-time intervention strategies for vulnerable persons who use drugs (PWUDs. (Bae et al. 2018 Tyler et al. 2024).
Data pertaining to longitudinal human behavior modeling, or ubiquitous health daid i contain vari- ables that are collected repeatedly over time and hence multivariate times series in nature (Xu
"Correspondence to nate,thach @ hwwkers.unl.comm und.ecu. UUniversity of Nebraska- Lincoln. Lehigh University. Due their ubiquitous nature and their intended applications human health (and well-being).

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et al., 2022). Some of these variables are obtained passively (e.g., resting heart rate via smart- watches); others are self-reported by participants (c.g., mood measures via regular smartphone- based questionnaires). The modeling targets for prediction (e.g., depression) can be defined based on either these self-reports or clinical diagnoses from domain experts. Therefore, longitudinal hu- man behavior modeling is often associated with time-series classification (TSC), where the goal is to build models that predict a label for an individual given their longitudinal data across a time period.
While ubiquitous health data are highly informative, they pose unique challenges standard ML approaches for TSC (Xu et al. 2022). First, they can span a long time period, resulting in long time series. Second, given the in situ nature of data collection in order to capture authentic human behaviors, there is high rate of missing values due to various sources, from participants low com- pliance (caused by e.g., privacy concerns or inherently disorganized lifestyle and underlying social pathology) to device failures (e.g., low or dead battery and data transfer loss) (Shiffman 2009 [Intille]] jt al. 2016). Third, they have small sample sizes given that, in order to ensure managea during data collection, self-reports and clinical diagnoses required to form the labels are administered either infrequently or only at the end of the study (and are occasionally missing as well).
Because of these challenges, Xu et al. 2022) show that traditional ML algorithms (e.g., support vector machine (SVM) (Cortes & Vapnik| 1995), random forest (Ho 1995). and boosting (Chen & Guestrin 2 and deep learning models (c.g., convolutional neural networks (LeCun et al. 2015)
and residual networks (He et al. 2016)) for depression detection struggle to achieve accuracy scores above 50%. (Please refer to the Related Work section for further background.) Latest works on longitudinal human behavior modeling (Liu et al.) 2023] Englhardt et al. 2024| Kim et al., (2024) attempt to improve the accuracy by employing large language models (LLMs), which demonstrate their strong performance over existing approaches across a wide variety of health prediction tasks at the individual level such as depression and sleep disorder detection with little to no parameter tuning. LLMs also provide a straightforward and effective means to incorporate raw data from different modalities, namely text. (from, e.g., domain knowledge and individual auxiliary information), into the model, further improving its predictive performance as shown in all cited works.
Limitations of Prior Works. Despite showing promising results, the three aforementioned studies have the two following major shortcomings in terms of practicality. First, they were only validated on health prediction tasks involving numerical time series from mobile and wearable sensing date This setting is unrealistic given that innovative data collection protocols, such as ecological mo- mentary assessment (EMA) (Shillimnn et al. 2008). may result in heterogencous time series (c.g., categorical and mixed-type data) with even higher rate of missing values. Second, these works do not consider the nontrivial consumption of computing, time, and monetary resources when employ- ing LLMs, which may quickly accumulate given the typical need for predicting multiple related health or well-being outcomes (e.g., comorbidity (Wosiak et al. 2018) and simultaneous usage of multiple drugs (Lorvick et al. 2023)). Please refer to Appendix A for full details.
Our Contributions. In this paper, we address these two shortcomings of prior LLM-based methodologies for longitudinal human behavior modeling (i.e., TSC problems concerning ubiq- uitous health data) by developing a novel method that (i) accommodates more general forms of ubiquitous health data and (ii) minimizes computing, time, and monetary resource consumption without compromising predictive performance. Toward achieving (ii) and motivated by the fact that there often exists some form of interrelationship between different health or well-being outcomes (e.g., anxiety and depression (Kroenke et al.) 2009)), we consider the problem of multi-label classi- fication (MLC) where multiple tasks associated with these outcomes are simultaneously learned by a shared model. We summarize our contributions as follows:
I. We propose the Multi-abiquiioos--eeen Health Boosting (MuHBoost) method that combines state-of-the-art LLM prompting techniques with advanced MLC approaches. In particu- lar, to attain strong predictive performance under various challenges inherent to ubiquitous health data as mentioned earlier, we leverage SummaryBoost (Manikandan et al. (023), a methodology for integrating a collection of weak learners generated from LLMs into a boosting pipeline. Given its proven promising efficacy in modeling tabular data with high heterogeneity (including missing values) and small sample size, outperforming zero-shot
This could be partly attributed the current lack of public ubiquitous health datasets.
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and few-shot approaches by substantial margins without any retraining or finetuning of the employed LLM, we adapt SummaryBoost to (i) work with ubiquitous health data and, more importantly, (ii) enable efficient MLC. By simultaneously inferring multiple health or well-being outcomes, MuHBoost significantly reduces the stated resources needed to employ cutring-edge I.MSs for longitudinal human behavior modeling in practice.
II. To mitigate hallucinations from LLMS (Huang et al.
2023 when predicting multiple lahels, we develop two variants of MuHBoost by leveraging novel algorithms from ML. literature (Nam al. 2017, Li et al. 2023). As shown in our experiments, the first variant enhances MuHBoost's s predictive performance (see (III) shortly) with simple modifications, and the other generally yields even better performance, albeit with extra costs.
III. We evaluate the effectiveness of MuHBoost and its variants on two publicly available ubiq- uitous health datasets and two novel datasets concerning the well-being of PWUDs and college students. For each dataset, we define several tasks of interest (2 6, for a total of 13 tasks across four datasets) to formulate a single MLC problem. Our results show that MuHlBoost and its variants achieve the overall best predictive performance, surpassing all considered baselines, including the three aforementioned state of the art in longitudinal human behavior modeling, across three standard metrics in MLC (Hamming accuracy and micro- and maaro-averaged F)). We also provide a time complexity analysis and cost esti- mation of API calls to demonstrate the resource efficiency of our three developed methods.
Outline. In Section 2
we review existing works on longitudinal human behavior modeling and introduce relevant approaches in MLC. In Section D we elaborate MuHBoost and its two variants. In Section 4 we evaluate the efficacy of our proposed methods. We conclude our work in Section
2 RELATED WORK
Longitudinal human behavior modeling is challenging for existing TSC methods applying to ubiq- uitous health data due to, e.g., the long time series, the high rate of missing values, the small sample size, and the nature of human behaviors (Xu et al. 2022). Therefore, we first focus on works that di- rectly consider problem of interest. Then, we discuss MI.C approaches upon which MuHBoost and its variants are built. We refer readers to Appendix B for a more comprehensive review.
Prior Efforts for Longitudinal Human Behavior Modeling. There has been an increasing inter- est in building ML models using ubiquitoos health data, from detecting mental health disorders (c.g., depression (Burss et al. 201T Chikersal et al., 202T) and stress (Vos et al.] 2023)) to predicting di- verse health and well-being outcomes (e.g., atrial fibrillation (Chen al. (022), binge drinking (Bae et al.]] 2018), and glycemic response for dietary recommendations (Zeevi et al. 2015)). However, the
generalizability of these models are dubious (Mohr et al. 2017: Saeb et al. 2016 Xu et al. 2022).
which prompted the release of several public datasets for the first time, notably GLOBEM (Xu et al.
2022) and LifeSnaps (YTantidou
al. (002).. Preliminary benchmark results with GLOBEM on
depression detection task (Xu et al., (022)) (same work that released the dataset) show the subpar predictive performance of all 18 existing ML algorithms for the considered problem, calling for methodological advances in longitudinal human behavior modeling. At the same time, research on integrating I.LMs in health applications (e.g., generic medical question answering (Singhal et al, 2023) and mental health prediction (Xu et al. )2244) has flourished over the past years. For this reason, latest works (Liu et al. 2023 Englhardt et al. 2024; Kim et al. 2024) explored the efficacy of LLMs in this area using standard in-context learning (e.g., zero-shot and few-shot prompting) and finetuning techniques, showing they are indeed the state of the art in longitudinal human be- havior modeling. Nevertheless, they share the same nontrivial shortcomings explained in our introduction, which severely undermine their implications for real-world health applications.
Multi-Label Classification (MLC). Approaches to MLC are mainly categorized into algorithm adaptation and problem transformation (Gibaja & Ventura 2015 Herrera et al.) 2016). The lat- ter approach, which transforms MLC problems to well- establssal single-label problems, has seen wider applications. Three most popular transformation methods are binary relevance (BR), classifier chain (CC), and label powerset (LP). BR and CC convert to multiple binary classification problems, whereas I.P converts to a multiclass classification problem. BR methods often employed across
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Data conversion prompt
(System)
(metadltta)
You fiihfully
Hore example from dataect, which disided into mubrise [TOPIS]]
Goal: Describe given dats words.
(sertallzed data in ISON fermat]
description
stoeed a ISON
Use your creptivity to deseribe gith [TOPIC]] sccurately and concisely. Do adé my
format
adtitional
Extraction prompt
(System)
Pleasz pxtmet sentences the spmppry: The should contain than 200 words Do not acd any addinonal infortrotiin.
extracced data
sammarizer that follows the output partem.
an eurractive
Decument: (data description)
Abstraction prompt
(System) You are at abstractve summarieer that
Please sov the extracied summurs Iased on the document. revisal the
summary description)
summary.
Killows le
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Figure 1: Template for the conversion of a record. Each call to the LLM is denoted by red arrow.
various fields due to their simplicity and interpretability. However, unlike CC's, they omit the de- pendencies between labels altogether, which are fact relevant in health and wellness applications (Wosiak et al. 2018 Blanco et al., 2020) Ge et al., 2020). On the other hand, CC methods are computationally expensive (Read et 2021) whereas LP's are prone to suffer from class-wise data scarcity ( (Tsoumakas & Vlahavas (007). Given the discussed underperformance of traditional MI. methods and the promise of LLMs longitudinal human behavior modeling, we approach MLC using ideas from the NLP literature (to be elaborated in Sections 3.2 and 3.3) where the problem is
better known as multi-label text classification (MLTC) (Chen & Ren 2021
3 MUHBoOST
We now describe the proposed MuHBBoost method. Section 3.1 details our data conversion proce- dure for adapting SummaryBoost (Manikandan et al.) 2023) (originally devised for tabular data) to ubiquitous health data. Section 3.2 claborates how we construct MuHBoost via extending Summa- ryBoost MLC. The two variants of MuHBoost are developed in Section 3.3
3.1 FLEXIBLE DATA CONVERSION
SummaryBoov Boost converts each tabular record into natural language representation called data de- scription by zero-shot prompting the LLM to describe the record as faithfully and concisely as possible. We first devise similar data conversion procedure for ubiquitous health data, which highly heterogeneous in practice as mentioned in Section We discuss two main challenges with this adaptation. First, when the record contains either rich contextual informatior or has long and high-ddmeensionl time series (i.e., large number of features recorded over time), fitting it into the prompt without exceeding the allowed context length L.LMs often turns infeasible or results in a decline in performance (Liu et al. 2024). Second, HummaryBoost's data conversion step does not actively aim to mitigate hallucinations from LLM which could be a major issue for time series.
To simultancously address these two concerns, propose two-step data conversion procedure built on top of the one from SummaryBoost, which is fully automated and requires minimal prompt engineering. As illustrated in Figure I the first step (first panel) aims to generate data description for each record similarly as in SummaryBoost The metadata contains high-level contexts about the present prediction task, and the serialized data contain individual-level details of record, which categorize into time series and auxiliary data. The latter type of data provides personal contextual information. When data pertaining small chunks or "topics", each of which contains temporally consistent or thematically related in-
record from either type is rich, we divide it into multiple
It has heen universally shown that the addition of task-speeific (e.g., domain knowledge about the health outcomes to predict) and individual-level (e.g., participants' background) contexts the prompt can signifi- cantly improve the performance of L.MMs (Liu et al.) 2023 Englhardt et al. 2024 Kim et al. 2024).
With simple directives the prompt for every API call (e.g., "do add any additional information").
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formation. That is, for time-series data, each topic is sensible portion of the entire time period e.g., 30-day data comprise "first 10-day", "second 10-day", and "third 10-day" data. For auxiliary data, each topic can cover responses to block of questions asking for e.g., demographics in some background survey. (Please refer to Appendix C.T for further details.)
In the second step of data conversion, we combine (if there are multiple topics) and refine the ob- tained data description(s) of each record into a single final data description. To achieve so, we apply the two-stage pipeline called cestract-then-gnneet yield significant performance improvement in terms of summary faithfulness (i.e., alleviating hal- lucination). More specifically, in the first stage (second panel of Figure D.. we prompt the LLM to extract salient information of the converted record. Albeit faithful, extractive summarization meth- ods tend to produce rigid and redundant summaries. Therefore, in the second stage of the pipeline (Figure Tss last panel), we prompt the LLM again to revise the extractive summary in order to obtain a more concise and natural-sounding data description, which is known as abstractive summarization (Gupta& & Gupta 2019). Finally, in accordance with SummaryBoost we append the associated label information at the end of each refined data description. In the case of binary MLC (i.e., all labels are binary), to reduce hallucinations from negation (Truong et al. 2023) in records having mostly nega- tive labels (e.g. from healthy participants), we only concatenate a binary label of the corresponding multi-labed record if it is positive If record does not have any positive label, instead append an explicitly negative sentence such as "Hence, this participant does not have any [disorders]."
from Zhang et al. (2023), which was proven to
3.2 EFFICIENT MULTI-LABEL BOOSTING
At its core, Summary Boost leverages LLMs to generate summaries from a representative subset of the dataset (given the limited context length of LLMs). These summaries then serve as weak learners in a traditional boosting algorithm, particularly AdaBoost (Freund & Schapire 1997), for binary or multiclass classification. This concept can be neatly extended to MLC via the LP approach.
Recall that LP reduces a Q-label classification problem to a multiclass classification problem. where each class corresponds to one of the 2 unique label combinations. Notice that the actual number of classes is min N, NN229) where N is the sample size. Motivated by (Song et al. 2023), which converts MLTC into a prompt learning task, we can design a generalizable prompt template in order to help LLMs capture the potential semantic relationships between multiclass labels and text from given dataset. Concretely, given set of data descriptions, we first ask the LLM to generate multiple (i.e., Q) summaries, one specifically for each label, then concatenate them into multi- label weak learner, which is later fed into the LLM for multiclass classification. The remainder of Summary Boost algorithm is kept mostly intact with two minor changes. First, we replace the simple error loss in binary classification with subser 0// loss for binary MI.C problems (Nam et al.. (017). We then set K, the number of classes (needed for computing the early stopping threshold and the coefficient of the weak learner at each boosting round), to min min [N,2?].
Although the present extension is sufficient for 29 < N. this assumption can be invalid given the generally small sample size of ubiquitous health datasets. other words, is possible to encounter situations where 2 approaches N i.e., each sample belongs to its class. This trivializes the originally proposed clusterSanpe ing subroutine, which selects a representative subset of the training data for each class such that same-class samples from smaller clusters (determined using hierarchical agglomerative clustering (Nielsen) 2016) on the language embeddings of each data description in SummaryBooss are more likely to be chosen. That is, when there are close to N different classes, clustering is clearly unnecessary because there is only one or two samples to con- sider, and hence ClusterSampl. ing would return more or less the same set of input samples, which is meaningless as its purpose is to select s < N representative samples from the training set. Thus, we modify this subroutine as shown in Algorithm [I] Conceptually, if the size of the representative subset is not substantially larger than the number of possible classes (Step 2), then we simply sample based on how "informative" the records are (i.e., having as many positive labels that ling. Otherwise,
rare possible) without having to do Cluster clustering for classes with no more than two examples (Step 13). Since this addition could return more than samples, we consider rarer classes k first in the for loop at Step 11. Moreover, if for some k. |S| + x [k]) > s, we only sample - |S| examples in Step 16 and return S preemptively.
can also skip
'The order of the labels to concatenate will be discussed shortly.
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Algorithm I ClusterSampling (Extended)
Input: Training data Xivuin with sample size N. label yiruin {yr > where ye {0, 13 sample distribution w for the current
boosting round, size of the representative subset army of class ratios array of length Q positive inleger
1: 5 e- new emply set
* =[k] and vr[g] are the propartion of samples in class & and of positive samples for label g. respectively.
2: if a <, min{ N, 2 } + is then
3:
army of same length as filled with
4: for == to do
5:
9 == to Q such that vf
6:
woii + w0 x v(o!
7:
4-
w)
no-oonaalzess the sumple weights; + denotes element- wise mulliptication
8:
Sample examples from Xirsin using w and appead to S
> sample without replacoment (same applics to Step 16)
9:
else
only do clustering S 24
10:
up <- new amay of same length as filled with I (for initialization)
11:
for
min N, 24) do
12:
X train 4- [X [Xtriin [1) -
samples that helong to class &
13:
ir [X S then
14:
Append them to and continue for next &
15:
Obtain w based on (vanilla) Clussersampl ling (X,g(X,, Xrain Vtraiie W.
lines 5 14 Alporithm Manikandan 2023
16:
Sample x r(k examples from Xtrvin using wo and append
Return:
9
used as input to the LLM for generaling summuries
Hence, our modification also helps deal with the rare label issue where a label may have imbalanced distribution of binary classes. We refer our approach discussed so far as MuHBoost.
3.3 ENHANCING MUHBoOST
We now describe two variants of MuHBoost based on advanced LP and CC methods follows.
MuHBoost[LP+]. One potential issue with MuHBoost is for larger Q. the increased com- putational burden due longer and more com-
L. Given the following drugs, [DRUG LISTJ, predict whether this drug user is risk from using it or not. Return an array of and 'No'.
plex prompts may lead to hallucinations from
LLMs (Huang et al., 2023 Liu et al.] (2024) and hence worsen predictive performances. In response, we propose a simple-yet-effective modification inspired by (Nam et al.) (017) in
LP+ Prodict which drug(s) from the following list this drug user is at-risk from using it, or 'None' otherwise: [DRUG LIST].
MLTC, which reformulates LP into the prob- lem of identifying all Q' < Q positive labels of a given Q-label sample. Essentially, instead of inferring the class & E {1...., (N, 2
Figure 2: Template for inference directives of MuHBoost and MuHBoost[LPtt] for our local dataset concerning PWUDs.
that corresponds to the sinultaneous prediction of all Q labels for a sample, we predict which of its labels are positive in sequential manner. While this idea was initially proposed traditional language models (RNN, GRU, and Seq2Seq) and hence required extensive architecture design, we can achieve this with LLMs by redesigning the inference directive in the inference prompt such that instead of asking for binary answers (e.g., an array of 'Yes' or 'No'), we prompt the names of the positive labels, any, shown in Figure 2 We refer to this approach as MuH[Boost[LP+].
MuHBoost[CCJ. Our second MuHBoost variant involves CC, with motivations and design ob- jectives elaborated in Appendix D.2. Given that SummaryBoo Boost utilizes AdaBoost as its backbone, focus latest works in MIC combine AdaBoost with CC. In particular, we leverage Ad- aBoost.C2 (Li et al. 2023). multi-path AdaBoost framework where a boosting path is established independently for each label, each of which aims to minimize the decomposed loss of the corre- sponding label while still being linked together in a chain. This allows flexibility during training since each of the Q boosting processes has its own set of weights and is hence able to fit the respec- tive label to the fullest extent as well as to early-stop asynchronously when needed. As a result, the label-wise predictive performance is maximized and the time complexity effectively reduced. Our adaptation of AdaBoost to MuHBoost, called MuHBoost[CC) is formalized in Algorithm 2 Note that since we mainly consider binary ML we set the number of classes to 2 beforehand.
'Generalizing MuHBoost[CC) to multiclass MI. (i.c., some or all of the Q labels are multiclass) is straight- forward and we defer its employment future work.

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Algorithm 2 MMHBBost[CCC]
Input: Training data Xerats with sample size N. label matrix Yeraio of size (N x Q) maximum number of boosing rounds T,, size the ropresentative subset s, permutation function d. hyperparameters 5 >> 0, j E (0,0.5).
new empty matrices of size x Q) 2: W < new mairix of size (Q x N) filled with
1:
W[g. :] the sample distribution for label is the ordering of labels at round containing most Q elements
3: t- --((f......
4:
+- urray of length Q' 6: Xtruin t- Xerain @ Y Veraial:: g') for E (I......) Q)) (n
5: Oft.:] +
q' locl:
7:
for lorg=1 1 Q1 do
8:
(X,, Y.) clustersampling (Xirase Yeruin B.g..
W(g.:. :l.s)
Algorithm (Manikandan al. 2023
9:
Hit. q] + Summary(X... Y.)
Summaxy prompts the LLM summarize the representativg sumples <(gl is the training for label g
10:
</al Ein, Wlo (1 (el
Eim Wlea))
if ]re[g] 0.5 then
enforces higher-quality summarics for accelerating convergence
12:
Go to Step
13:
Alt.ol +- log [=elo]) if 2 8 elase
14:
eell then
15:
for
Wig. i]
(x W[g; i]x (ete (bol))
16:
W W/g. +- Leemmliiz
17:
X Xirain + X X B (r
denotes the appending of true label(s) (if positive only) each training sample
18:
e(g] then
19:
Remove from Oz
for sullicient < T,, the rows after the 4th row of H, A, O can have less than Q elements
Return:
H, O
used during inference of unseen samples (see Equations Si and
For determining the label ordering, we opt for reasonable chain order defined by some heuristic permutation a and fix it throughout the boosting process. Please refer to Appendix [D.2] for an in- depth explanation. Note that in addition to chain ordering, we also order the labels according to a for the label information concatenated to each data description as well as the e.g., [DRUG LIST] in the summary and inference directives (Figure The same applies to MuHBoost and MuHBoost[LPt].
EXPERIMENTS
We start this section by describing the considered ubiquitous health datasets and their associated tasks in Section 4.1 In Section 4.2 detail the experimental setups for validating the efficacy of MuHBoost and its variants, including the relevant baselines and our evaluation criteria. Finally, we present our results in Section 4.3 Appendix C.2 includes further implementation details.
4.1 DATASETS AND TASKS
We consider four ubiquitous health datasets that contain both time-series and auxiliary data as well as sufficient information to form multiple labels for each: LifeSnaps (Yfantidou et al. 2022). GLOBEM (Xu al.] 2022), college students (CoSt) (Hayat & Hasan 2023). and PWUD (Tyler 2024). The first two are publicly available and have previously been considered by Eng al.
ct al.
2024 Kim al. (2024) (as mentioned in Section 2 whereas the latter are novel and
quire submitting an IRB protocol and ethical research plan to their authors. For each dataset, let Dts and Daus be the dimensionality of time series and auxiliary data, respectively. We then formulate an MLC problem by defining Q E [2, 6] prediction tasks that consider the same set of features, with or without the auxiliary data. Please refer to Appendix C. for the exact features used in each task.
LifeSnaps (N = 130). We consider two tasks (Q 2) predicting whether each sample would be risk from: (i) negative emotions, defined by having PANAS negative affect score (Watson al.) 1988) higher than positive affect score, and (ii) anxiety, defined by having above average STAI stress score (Spielberger et al. 1999). given daily time-series data (Dts 8) the past seven days in accordance with (Kim et al. 2024). The associated auxiliary data (D aua 28) are extracted from the health and psychological baseline surveys pertaining to the participants upon entry.
GLOBEM (N -6). We consider three tasks (Q 3) predicting whether each sample would be at risk from: (i) negative emotions, defined by having a PANAS negative affect score of 6 or greater, (ii) anxiety and depression, defined by having total PHQ-4 score (Kroenke et al.) 2009) greater than
5 (otherwise none score lower than 1), and (iii) stress, defined by having a total PSS-4 score
(Cohen et al. 1983 of 6 greater. Since the three surveys for forming these labels were not all
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delivered within the same day, while we still keep the size of each sample 28(days) 16(D in accordance with (Englhardt et al. 2024). we define its corresponding (multi-) with respect to the scores from these three surveys within 3-4 days after the 28th day. Following (Englhardt et al.) 2024), we then create a balanced dataset by randomly sampling 32 data points from each year (Years 2, 3, 4) with uniform distribution of labels (4 for each of the 2 = 8 label combinations), for total of 96 samples. The auxiliary data (Daus = 27) are derived from the "pre surveys", which contain questionnaires asking for personality, physical health, mental well-being, and social well-being.
CoSt (N- = 48). This dataset was obtained from 48 college students enrolled in an introductory programming course at the University of Nebraska-Lincol in which their self-evaluations on course performance and motivation (intrinsic and extrinsic) factors throughout the 16-week semester were recorded weekly via the aforementioned ODIN app (Khan al. 2019). We consider two tasks (Q = 2) predicting whether each student would be at risk of (i) underperforming in Project 1 (constitutes 10% of the final grade), defieed by receiving a grade below (out of 10) and (ii) failing the course, defined by receiving final letter grade of C+ or lower, given the stated weekly time-series data (Dis = 11) from the first eight weeks. We formed these definitions with the goal of ensuring there are sufficient positive samples for each task. The auxiliary data (Dauz = 26) comprise their background information (e.g., declared major, parents' highest level of education, total household income, etc.) acquired from an intake survey as well as grades of various lesser course items (e.g.. labs, quizzes, homework assignments, etc.) that were due within the first eight weeks.
PWUD (N 68). This dataset consists of 100 PWUDs from Nebraska, U.S. whose substance use, sociality, stress, and support were enquired daily via the ODIN app over the span of 30 days, with Dts 22. PWUDs also took an intake and an exit survey (same for both) about drug uses and individual attributes at the start and the end of the study, respectively. We construct auxiliary data (Dauz 108) using the intake survey and create labels using the exit survey. Hence, we require the period between the two taken surveys to be close to 30 days (i.e., within 27-33 days), and the period from the last answer in the ODIN app to the exit survey completion to be be small (i.e., within 3 days), which results in N = 68. Our six considered tasks (Q 6) involve predicting whether PWUDs would be at risk of using: (i) alcohol, (ii) marijuana, (iii) methamphetamine, (iv) cocaine, (v) amphetamines, and (vi) opioids. After conferring with domain experts in substance use research, we define: (i) as having binge drinking; (ii) as using more than once a week; (iii) and (iv) as using once a month or more; and (v) and (vi) using once week more.
4.2 EXPERIMENTAL SETUPS
Baselines. We consider the following methods from both problem transformation (+) and algorithm adaptation (+) approaches for MIC. We refer readers to Appendix C.2 full details.
(+) Zero-shot: Similarly as in the work of SummaryBoost, we query the LLM with a data description and directly infer its label. For MLC, this can be done in three ways: asking for either (i) Q labels one
at a time (hence Q separate calls to the LLM), (ii) Q labels simultaneously, or (iii) only its positive labels, which are reminiscent of BR, LP, and L.++ approaches, respectively. We therefore denote them as O-shot[BR], O-shot[LP], and O-shot[LP+]. Note that for single-label problems (Q 1), O-shot[BR] is equivalent to the zero-shot prompting techniques developed in (Englhardt al. 2024 Kim et al. 2024.. We use GPT-4 (gpt-4--o))) -t-urbo)) following (Kim et al. 2024).
(+) Few-shor: We follow the few-shot setting as described in SummaryBoone
experiments, where we leverage their BlusterSaape ing to select a representative subset of Xtrain for demonstration before asking the LLM to infer the label of some unseen sample. We set the number of shots to 10, which is consistent with fixed earlier. Similar to the zero-shot setting, there are three ways to ask the LLM for MLC, for which we denote as 10-shot[BRJ. 10-shot[LP]. and 10-shot[LP+J. We also use GPT-4 for the same stated reason! [2
(+) Random forest (Ho 1995) and XGBoost (Chen & Guestrin 2016): We consider problem trans- formation via both CC and LP when employing these baselines for MLC, for which we denote respectively as RF[CC], RF[LP], XGBoost[CC], and XGBooot[LPP.
(+) Multi-labell & Nearest Neighbours (MLANN) (Zhang & Zhou 2007) and twin multi-label SVM (MLTSVM) (Chen et al., 2016): Lastly, we include two estalbsstalished methods for MLC via algorithm adaptation, which respectively adapts KNN and SVM to MI.C directly.
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Method
LifeSnaps
LifeSnape+
GLOORMM
GLOBEEM+
CaSi
CoSe+
PWUD
PWUDE
[B]
[16 16 171
[14 14 14]
[18 17 16]
[13 151
[14 14 131
[13 12 121
[14
[13 1311111
D-sbot[L]]
[18 18 191
[15 14 151
[17 18 18]
[15
16 14]
[17 18 181
115 16 16]
[1618 191
[17 16171
Ds
117 161
112 131333
116 171
110 1001210 111
118 17
116 151
118 161
115 15 141
[BR
11 12 11]
[8 71
[12 10 10]
8 9 71
[10 9 91
7 7]
8 10 8]
[ 6]
10-shot|LPI
121
111
9
112
101
112
10 [0-sho[[PP]]
[1011 101
7 8
91
[14 13 12)
7 81
[1] 10
11]
18 6 8]
[1] 10]
[10 8 91
RFJCCI
121 19 181
122 21
201
123 22
191
234
130 27 291
127 301
128 30 301
126 29:29:288
RF[LP]
[29 27
[27 26
[28 29
[25 24 22]
[25 19 201
[22 24-2222
XGBoos[[CC]
[19 1192022 23]
[20 22 211
222 21 20]
22 19 24]
[23 25 27]
[26 28 24]
[24
[27 28
XGBooss[LP]
[25 28)
[30 26 25]
[21 24 211
[29 28 26]
[28 30 28]
[29 26]
[29 27 29]
[30 26
MLANN
[24 22]
[26 24 24]
[19 20 22]
[30 25 28]
[2] 19 20]
23]
[23 20 18]
[21 21
MLTSVM
[23/25:2]]
[27 28 26]
[25 27 29]
[26 30 25]
[22 20 21]
[20 21 19]
[19:25 21]
[20 22
MuliBoosa
6666 5]
2
[ 5 61
3
6 6
5 51
6 6 71
5
MuHBoos([LPe)
4
3
6
1
5 4
2
1]
McH
1 5 41
1 1
11
1 4
51
2 1 11
3 31
1 1 11
31
2
21
Table 1: Performance ranking (L) of 15 methods subject to [HA miFi maFi]. (+) next to the datasets' tags denotes incorporation of auxiliary data in addition to time-series data (hence there are 15 x = 30 methods total).
Evaluation Metries and Procedure. For each considered set of experimental configurations, we used the split ratio of 50/10/40 (for train/validationntess set), which follows Summary Boost's evaluation, for a total of 10 different splits. For partitioning multi-label datasets into training (rrainvvalidation) and test sets, we adopt the iterative stratification algorithm
[3] (Sechidis et al.
2011) We use the standard Hamming accuracy (HA) Q 1 = y E [0, metric for MLC (Tsoumakas & Katakis 2007), where y. ÿ are vectors of ground-truth labels of given sample and their corresponding predictions, and report the resulting summed-then-avee then-averaged value from all test samples. Since we encounter class imbalance for individual labels in 3 out of datasets, we also consider micro- and macro-averaged- -meesuure (Herrera et al. 2016). They are respectively de-
meessure
fined by Nam al. (0117) as mifi Σ (2TP, +FP, and maFi 2 -F where TP, FP, FN stand for true positives, false positives, and false negatives from all test samples. Whereas miF; evaluates the overall performance of a classifier (and hence rewards those giving correct predictions on frequent labels), ma/; focuses on assessing performance on rare labels.
Σ 2TP,
2TP
4.3 RESULTS
Table the predictive performance of our methods relative to the considered baselines, where each bracket contains rankings of the respective method (E f1,...,30) 300]. including whether auxil- iary data is incorporated or not) for the three aforementioned MLC metrics. MuHBoost, with or without integrating auxiliary data into the models, already outperforms all baselines in most cases. Its variants, [LP+] and [CC] for brevity, bring further improvements and hence yield the overall best performance. For MLC problems with small Q (from LifeSnaps, GLOBEM. and CoSt), [CC] seems to perform better than [L.+]]; whereas the contrary holds for the remaining MI.C problem with larger Q (from PWUD dataset). As expected, traditional supervised ML methods perform poorly across all datasets, trailing few-shot and even zero-shot methods. For those leveraging LLMs including ours, we also see that incorporating auxiliary data in the prompt brings enhanced performance. This observation agrees with prior works on modeling ubiquitous health data (Liu et al. 2023 Englhardi jtt al., 2024 Kim et al. 2024), which demonstrated the significance of providing rich contextual information when asking LLMs for inference. The same, however, does not apply for methods from classical MI. algorithms, which could be due to overfitting given their lack of encoded prior knowledge well the small sample size and high dimensionality of all employed datasets.
Impact of Refining Data Descriptions. We investigate the effect on predictive performance of the refinement step (i.c., abstractive summarization in the bottom panel of Figure in our proposed data conversion procedure. (We omitted vanilla MuHBoost due to its lesser performance compared to [LP+] and [CC]; and we did not ablate the extractive summarization step since it is required to trim each data description within reasonable word limit.) Recall that this step enforces concise- ness and fluidity in the data descriptions. As shown in Table 2 (where b from a(b) is the standard deviation expressed in terms of the two least significant digits (Bipm et al. 1995)). )) our ablation

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Published as a conference paper at ICLR 2025
demonstrates its positive impact across all datasets, which is reasonable since such refinement of information effectively alleviates hallucinations from the LLM.
MuHBBosst Po]
MoHBoos[[CC)
MuHBBoot [P++
MuHBoost(CC)
Rehie data descriptions?
Dataset
GPT-3.5
GPI-4
GP1-3.5
Dataset
No
Yes
No
Yes
0,731(29)
0,733(322
0.7.8K(77)
0.704(42)
0.731(29)
0.714(53)
0.739147)
LifeSnaps+
0.836(46)
0.839(50)
0.843356)
1. LifeSnaps+
0.798(65)
0.836(46)
0.806(39)
0.843(56)
0,779(71)
(.822(40)
0.787(64)
0.830(58)
0,822(40)
0,825(47)
0.830558)
0.676(54)
0.720(43)
0.692(49)
0.718(38)
0.720(43)
0.71
0.718(
GLOBEM+
0.829(62)
(,813(58)
0.833(47)
GLOBEM+
0.795(66)
0.829(62)
0,807(57)
0.833(47)
0.817(59)
0.806(55)
0.825(51)
0,784(70)
0.817(59)
0.796159)
0.825(51)
0.715(38)
0.718(42)
0.726(52)
0.697(45)
0.715(38)
0.699(44)
0.726(52)
CoSL+
0,782(60)
0.814(54)
0.777(47)
(.823(59)
CoS++
0.814(54)
0.820(45)
0.823(59)
0,803(49)
0.815(52)
0.812(48)
5.770X58)
0.803(49)
0.764(51)
0.812(48)
0.65%)63)
0.688(54)
(.662(47)
0.673(39)
PWUD+
0.688(54)
0.784(48)
0.696(53)
0.789(62)
0.775(44)
0.673(39)
PWUD+
0.746(59)
0.784(48)
0.739(55)
0.775(44)
0.771(41)
0.781(61)
0.769(36)
0.727(52)
0.771(41)
0.721(49)
0.769(36)
Table 3: Ablation of the underlying GPT model in MuHBoos([LP4]. In each cell: (top) HA, (middle) miFi, (bottom) mafi.
Table 2: Ablation of the abstractive summariza- tion step in our data conversion procedure. In each cell: (top) HA, (middle) miFi, (bottom) maFi.
Upgrading to GPT-4. Table 3 shows the effect of switching GPT-4 for MuHBoost(LP+]. (Do- ing the same for [CC] costs x more. which is prohibitively expensive given the substantially higher cost of calling GPT-4 compared to GPT-3.5 as specified earlier.) Although leveraging GPT-4 helps slightly improve the performance of [LP+]. it does not bring consistent improvement over [CC], especially small Q settings (from the first three datasets). Even when Q is larger (from PWUD dataset), the improvement with respect to those utilizing GPT-3.5 is small.
MuHBoost and its variants re- quire minimal computing power by leveraging commercial LLMs. terms of runtime, MuHBoost and [LP+] share the complexity with SummaryBoost and therefore take O(TM)) f), where T is the number of boosting rounds, M is the number of resampling for the weak learner at each round, and is the LLM's runtime. In terms of cost associated with training model via these two methods, according to Manikandan et al.] 2023), the upper bound for the total number of requested tokens is T x [M (S. + 0.5N x Pt) 0.1N Ps]. where S, and P. are the context length of the LLMs employed for summarization and for inference (same in our experiments), respectively. For [CC), an extra factor Q' S Q is added for both time and cost. For comparison with the state of the art in longitudinal human behavior modeling, the finetuning approach from (Kim et al. 2024] (exhibiting the overall best performance) takes OQQNN f) in total for an MLC problem. Thus, our developed methods offer a sesource-efficieen as well as effective solution for modeling ubiquitous health data. [LP+] versus [CC]: When comparing between the two MuHBoost variants that yield the overall best predictive performance, [CC] seems to excel for small to moderate Q and when there are less constraints on time and budget, whereas [LP+] is applicable to all other cases (see Figure 53). In- terpretability: Following SummaryBoost. MuHBoost and its variants also provide interpretability by storing --ge-rrertted knowledge regarding the task at hand into natural-languaa summaries.
Discussion (more in Appendix
Resource Consumption:
5 CONCLUSION
In this paper, we propose MuHBBoost a multi-label boosting method for longitudinal human behav- ior modeling, along with its two variants for addressing two practical aspects that are lacking in prior works: the heterogeneity in real-world ubiquitous health data and the consumption of com- puting, time, and monetary resources. We achieve so by adapting SummaryBoost. state-of-the-an method for heterogeneous and small tabular data prediction using LLMs, to our problem of inter- est, and then extending it to multi-label classification settings where multiple related tasks can be simultaneously learned. Our experiments covering 13 health and well-being prediction tasks from four diverse datasets demonstrate the effectiveness of all developed methods, in addition to their resource efficiency well interpretability. Our work thus potentially help domain experts from various medical and social science disciplines better understand human behaviors and develop effective personalized prevention or intervention strategies for at-risk individuals accordingly.
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ACKNOWLEDGMENTS
We would like thank all ICLR-25 reviewers for the valuable feedback and the interactive discus- sion during the rebuttal period, which significantly improve the overall quality of our work. Our project supported by the National Institute of General Medical Sciences of the National Insti- tutes of Health [P20GM130461], the Rural Drug Addiction Research Center at the University of Nebraska-Lincoln, and National Science Foundation under grant IIS:RI #2302999 and IIS:RI #2414554, The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
ETHICS STATEMENT
Our work proposes a novel method powered by large language models. Although achieving great promises, there could be potential biases stemming from subjective, biased, and ungrounded text corpora as well as quality degradation over time from these language models (e.g.. ChatGPT (Chen) al., 2024)). Therefore, we do not advise on the use of the proposed method for determining at- risk individuals on the fly, but rather for forming a decision-aid system that complements decision- making from human experts.
REPRODUCIBILITY STATEMENT
We refer readers to Sections 4.1 and 4.2 well as Appendices C.I and C.2 for complete details reproducing our results, including the link to our anonymous GitHub repository
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A LIMITATIONS OF PRIOR WORKS
Despite showing promising results with LLMs, the studies conducted by Liu et al. (2033 y Englhardt et al. (2024); Kim al. (2024) have two following major shortcomings in terms practicality.
They were only validated on health prediction tasks involving numerical time series from mobile and wearable sensing date This setting is unrealistic given that innovative data collection protocols, such as ecological momentary assessment (EMA) (Shiffman et al.) 2008), enable the acquisition of fine- aie--graind behavioral data not just passively via sen- sors, but also actively via e.g., swwrtphone- multiple-choice questions routinely. Hence, there could be non-numerical time series such as categorical (from either single- or multi-select questions) and those with mixed data types (from questions that ask for either e.g., free-form input or "not applicable"). Fur- thermore, advanced technologies (e.g., SARA (Rabbi et al. (2018) and ODIN (Khan et al.) 2019) that facilitate in situ data collection could bring further heterogeneity the resulting dataset by different means. The SARA mobile application, for example, provides various engagement strategies for incentivizing participants to self-report on their day (e.g., a vir- tual aquarium in the app grows with fish as they complete more daily self-reports). It has been shown in practice, however, that such intricate data collection platform could lead to technical software issues where survey data from self-reports are not consistently saved Coughlin et al. (202T). In another example, the ODIN software platform for implementing EMA methods offers specification of contextual rules that govern when each question will be asked (e.g.. at certain times of the day, geographic locations, or even on button press from a selection to some currently prompted question). Although this functionality could provide an effective means to gather contextual data, it may complicate data analytics in return. Most evidently, when a question is omitted by design, the current entry for the cor- responding time series is recorded as a missing value. As result, in both examples, the already high missing rate from ubiquitous health data is inadvertently exacerbated.
personalized surveys asking participants
There were no considerations on resource consumption when employing the LLMs via finetuning as well as zero-shot and sew-shot prompting approaches. Finetuning even small LLMs in (Kim et al.] (024) requires significant amounts of computing power and run- time, whereas zero-shot and few-shot techniques rely on much bigger LLMs that must be pretrained on enormous text corpora be effective, as implemented in (Liu et al.] 2023). On the other hand, utilizing commercial LLMs via their APIs as in (Englhardt et al. 2024 Kim al. 2024) requires paying nontrivial fee Furthermore, such consumption of com-
Fach API call to commercial L.MSs (e.g., OpenAT's GPT family, Meta's Llama, and Google's Gemini) incurs a specific dollar cost subject to the number of input/output tokens.
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puting, time, and monetary resources may quickly accumulate given the typical need for predicting multiple related health or well-being outcomes (c.g., comorbidity (Wosiak et al.; 2018) and simultancous usage of multiple drugs (Lorvick et al. 2023)). In fact, Liu et al. (2023): Kim et al. (202)) defined multiple tasks (up to 5) associated with each considered dataset, such anxiety and depression detection with GLOBEMM (Kim et al. 2024) (where they built a separate model for each).
B RELATED WORK (LONG VERSION)
Prior Efforts for Longitudinal Human Behavior Modeling. There has been an increasing in- terest in building ML models using ubiquitous health data, from detecting mental health disorders (e.g., depression (Bums et al. 2011 Chikersal et al. (02T) and stress (Vos al. (023)) predicting diverse health and well-being outcomes (e.g.. atrial fibrillation (Chen et 2002), binge drinking (Bae al. 2018), and glycemic response for dietary recommendations (Zeevi al. 2015)). How- ever, these models either are inaccurate or require hand-crafted features to be effective, and every one of them was validated on single, private dataset (Mohr et al., 2017 Xu al., 2022]. Fur- thermore, [Saeb et al. (016) found that substantial portion of earlier works used inappropriate cross-validation techniques, resulting in overestimated predictive performances. To overcome these generalizability concerns, several datasets have been publicly released for the first time, notably GLOBEM (Xu et al., 2022) and LifeSnaps (Yfantidou et al., 2022). Preliminary benchmark re- sults with GLOBEM on depression detection task (Xu et al. 2022) (same work that released the dataset) show the subpar predictive performance of all 18 existing ML algorithms for the consid- ered problem, calling for methodological advances in longitudinal human behavior modeling. At the same time, research on integrating L.Mss in health applications (e.g.. generic medical question answering (Singhal al. 2023) and mental health prediction (Xu al. (2024) has flourished over the past years. For this reason, latest works (Liu et al. 2023 Englhardt et al. 2024 Kim et al.) 2024) explored the efficacy of LLMs in this area using standard in-context learning (e.g., zero-shot and few-shot prompting) and finetuning techniques, showing they are indeed the state of the art in longitudinal human behavior modeling. We hereby further discuss these works the following paragraph.
Longitudinal Human Behavior Modeling with LLMs. in this area was conducted by Liu al. (2023). By experimenting on multiple self-curated toy d 8 the authors showed that with only few-shot prompting, their pretrained L.L.M is viable for various health prediction tasks e.g., heart rate measurement, atrial fibrillation detection, and mood score prediction. Following up this work, EEglhardt et al. (2024) instead considered different zero- shot prompting strategies with commercial LLMs (e.g., GPT-3.5/4 and PaLM 2) for depression and anxiety classification tasks GLOBEM. Despite yielding low accuracy in clinical screening con- text, these general-purpose LLMs, even without any annotated examples provided, still generally outperformed the considered baseline ML models that required training from scratch all labeled data. Lastly, Kim et al. (2024) expanded the experimentation from (Englhardt et al., (202)) by eval- uating 12 cutting-edge LLMS with different in-context learning and finetuning techniques for 10 consumer health tasks derived from four ubiquitous health datasets (with GLOBEM and LifeSnaps included). They found that zero-shot prompting methods already yield comparable results to the con- sidered supervised baselines (e.g., SVM, random forest, and classical pretrained language models such as BERT), and with proper few-shot prompting and finetuning techniques, the corresponding predictive performance can be improved significantly. All discussed works have thus demonstrated the compatibility of LLMs for longitudinal human behavior modeling. Nevertheless, they share the same nontrivial shortcomings as explained in our introduction, which severely undermine their implications for real-world health applications.
To our best knowledge, the earliest work
Multi-Labe Classification (MLC). Approaches to MLC are mainly categorized into algorithm adaptation and problem transformation (Gibaja & Ventura 2015 Herrera et al.) 2016). The lat- ter approach, which transforms MLC problems to well-established single-label problems, has seen wider applications. Three most popular transformation methods are binary relevance (BR), classifier chain (CC), and label powerset (LP). BR and CC convert to multiple binary classification problems, "The work was conducted prior to or during GLOBEM release and hence did consider this datasct.
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whereas LP converts to a multiclass classification problem. BR methods often employed across various fields due to their simplicity and interpretability. However, unlike CC's, they omit the de- pendencies between labels altogether, which in fact relevant in health and wellness applications (Wosiak et al. 2018: Blanco et al., 2020: Ge et al., 2020). On the other hand, CC methods are computationally expensive (Read et al. 2021) whereas LP's are prone to suffer from class-wise data scarcity (Tsoumakas & Vlahavas 2007). Given the discussed underperformance of traditional ML methods and the promise of LLMs longitudinal human behavior modeling, we approach MLC using ideas from the NLP literature (as elaborated in Sections 3.2 and 3.3 where the problem is
better known as multi- labell text classification (MLTC) (Chen & Ren 2021
Early notable works in MLTC that apply neural networks employ the Sequence-to-sequeeee (Seq2Seq) model (Nam al.) 2017 Yang al. 2018 2019). Most recently, [u et. (023) explore hybrid model that combines CNN and LSTT for labeling short Chinese texts. Works that leverage LLMs, mostly via finetuning, are much rarer due to the extravagant resources required. [Chang al. 2020) attempt to finetune pretrained transformers such as BERT and XLNet for extreme MLTC (i.e., where the label collection is large), and Betianu et al. (2024) propose semi-supervised do- main adaptation framework for BERT in order to ace MLTC tasks on target domains, to name few.
All aforementioned methods were benchmarked on generic text datasets comprising at least thou- sands of annotated samples. Therefore, also seek explore the uncharted feasibility of LLMs in MLTC problems on real-world ubiquitous health data.
C MORE ON EXPERIMENTS
C.I DATASETS
LifeSnaps. Table S4 lists the features extracted from LifeSnaps. During data conversion, we di- vide the auxiliary data into three topics for the three respective questionnaires from the entry survey, namely TTM, BREQ. and IPIP. The negative/positive class ratios for tasks (i) and (ii) associated with this dataset are 99/31 and 86/44, respectively. Note that the definitions of "at risk" stated in are for demonstration purposes only.
Section 4.1
Type
Feature
stress. stress_score
score
Time series (Dsa 8)
lighty active minutes, moderately active minutes, very active minutes
sleep efficiency
sleep wwwppeepppoe ratio, sleep_dight skeep_sem_ratio
(x [1 Stages and Processes of Change Questionnaire (TTM)
Auxiliary
(x6) Behavioural Regulations Exercise Questiomnare (BREQ)
(D., = 28)
(x11) International Personality Item Pool version the Big Five Markers (IPIP)
Table $4: LifeSnaps features used.
GLOBEM. Table S5 lists the features extracted from GLOBEM. During data conversion, we divide the 28-day time-series data into four topics (i.e., "first week". "second week". "third week". and "fourth week") and the auxiliary data into four topics as grouped in Table SS (i.e., social well- being, mental well-being, physical health, and personality, respectively). Unlike LifeSnaps, the label definitions were respectively determined based on GLOBEM's percentile, the justifications from (Englhardt et al. (204)), and prior studies from multiple countries (Arnold et al. 2012 Warttig al.
2013 Malik et al. 2020).
CoSt. This dataset consists of three components. The first is an intake survey of 9 questions" m asking for class standing, major, international student status, whether parent(s) went to college, highest education level of parent/guardian and (if any) the other parent/guardian, total household income in the past 12 months, the extent seeing themselves as a future engineer/scientist, and the perceived extent of others seeing them as future engineer/scientist. The second component contains the students' grades of 17 lesser course items that were due within the first eight weeks: the first two diaries, six labs, four quizzes, and five homework assignments. Table the chronology of these items. Collectively, these two components form the auxiliary data (Daur = 26) and hence
Excluded 2 questions regarding race and gender identity.
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Type
Fearure
date
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stops:fitbit steps intruday rapids counlepisodeactiveeeee wwwtoeeeeeiieotttallaa.g
f Esteps:fitbit.sit intraday rapids sum.uraaiionactiveeoono
fslp:fiibit_soo seapiiitsleeiiittaaaae wwppddaapdddddduaa www.s//rralooossoeppaslseeeeeeee
ssp::iibit.sseep www.ppeitteddaayy......aa cenuddyyrriiis..o httpliaaaaiioossuuuaaaiiitnnoe www.uuuatinnaawwaek.o.o.. aamnddrriiioawwccciieeenneii
UCLA Loneliness Scale
Sense of Social and Academic Fi Scale
4x Way Social Support Scale
Everyday Discrimination Scale
Chronic Discrimination Harassmnt
Perceived Stress Scale
(x Emotion Rogulation Questionnaire
Briet Resilience Scale
Auxiliary
State Trail Anxiety Inventory for Adulis
(Di 27)
Center Epidemiologic Suudies Depression Scale Cole version
Bock Depression Inventory-II
Mindful Attention Awareness Scale
(x2) Brief Coping Orientation to Problems Experienced
Gratitude Queationnaire
Flourishing Scale & Psychological Well Well-Being Scale
Cohen Hoberman Inventory of Physical Sympions
The Brief Young Adulr Alcohol Conssquences Questionnaire
(x3) The Big-Five Inventory-10
Table S5: GLOBEM features used.
correspond to two topics during our data conversion. The third component of CoSt comprises time-
series data collected weekly via the ODIN app, with a total of 11 features (Dis = 11) on students' self-evaluations and motivation (intrinsic and extrinsic) factors throughout the course listed in Table S7 The negative/positive class ratios for tasks (i) and (ii) pertaining to this dataset are 38/10 and 36/T2, respectively.
Week
Item(s)
Lab
(due by Mon)
Lab 2
(Mon)
Quiz
(Wed)
Homework
(Wed).
Quiz (Fri)
Lab 3
(Mon),
Homework
(Wedy, Lab
(Thu)
Diary
(Tue)
Quiz 4 (Mon),
Lab
(Mon),
Homewark (Wed).
Lab
(Wed)
7
Homework
(Wed),
Diary
(Thu)
8
Quiz 5
(Mon). Homework 5
(Wed)
Chronology of the course items' deadlines. There one-week Spring break between the end of Week 7 and the start of Week 8. Project (not included) accounts for 10% of the final course grade and due during the 10th week.
Table S6:
PWUD. The auxiliary data derived from the intake survey comprise individual attributes as well as substance use behaviors with respect to wide variety of prescription and recreational drugs, including stimulants (methamphetaninne cocaine, amphetamines), depressants (benzodiazepines, opioids, heroin), and hallucinogens (marijuana, PCP, Ecstasy). Table S8 summarizes each feature group, each of which corresponds to a topic during our data conversion. Please refer to the attached ZIP file in our supplementary material for the complete list of survey questions. Table S9 lists the features collected daily via the ODIN app that constitute the 30-day time-series data. We create three topics accordingly i.e., "first 10-day", "second 10-day", and "third 10-day".. The negative/positive
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Feature
Type
Question
Rule
Q1
single-sclect select
What grade do you think you might earn in CS1?
Saturlay at 12:01 PM
Q2
single-select
How much confideace do you have in your ability to complete all the roquire-
Follow-up from Q1
ments of the CSI class?
Q3
single-sehe select
How much confidence do you have in your ability to excel in the CSI class?
Follow-u up from Q2
Q4
single-select
How salislied are you with your performance in class?
Saturday 07:00
Q5
single select
How do you think other students in CSI are performing compared to you?
Follow up from Q4
Q6
single-select
How worried are you about our performance
CSI class?
Follow-up from Q5
Q7
single-sclect
How much do you yourself futuree ongineer or scientist?
Sunday 12:01 PM
Q8
single-sclect
How much do you think others (Tamily, friends, pects) see you as future engineer scientis!?
Follow-up from Q7
Q9
single select single-select
How important do you think the CSI class is for your future career?
Follow-up up from Q8 Follow-up from Q9
Q10
Thinking towards the future, how important do you think doing well in your college classes to having good life?
Q1I
single-select
What type of ca-campus extracuricuaar activities are you involved in?
Sunday at 07:00
Table S7: CoSt's time-series data. Amuuli-seleett features are derived from bheck-all-that-ppp questions.
class ratios for tasks (i)- (defined as suggested by domain experts in substance use research in our
leam) associated with this dataset are 23/32, 29/37, 18/49, 51/16, 50/18, 58/10, respectively. Note
that the total counts vary across tasks due to missing data required for forming the corresponding labels in the exit survey. When computing loss and performance measures, if a sample has Q' < Q missing labels, we simply ignore them and only consider the performance on the remaining Q Q' labels.
Feature group/ Topic
Summary of features
-peppl individual can confide ohtain emotional support, and peovide financial assistance
Social support (9/9)
-perceived number of confidants and number of drug co-users
Substance use behaviors (19/21)
frequency of smoking cigareties, drinking alcohol, binge drinking, and using various non injection drugs and reasons for using alcohol and/or drugs right before while having sex
Drug overdose (13/15)
experiencss of overdoses (c.g. personal overdose history and awareness of Narean/nallxone)
Substance use treatment (15/17)
individual participation in alcohol and drug treatment programs (e.g outpatient and detox))
length of longest cossation period and count of relapse instances
frequency of feeling nervous, restless, worried, and annoyed
Feelings (24/25)
history of being diagnosed with various mental health disorders e.g. depression, ADHD, PTSD
Recent adverse experiences (10/10)
-whether being stolen, beaten up. robbed, threatened, physically and sexually assaulted the past 30 days -whether being incarceruted in the past 6 months and if so, whether it was drug related -community's a perception regarding trustworthiness, harmfulness, accountability, and laziness of people who use cocaine caalm///mthmmphtaamiin and opioids/heroin
Community views of substance use (8/8)
-age, marital status, education, current employment status, religion, influence of religious or spiritual belicfs, ever been homeless, total houschold income
Demographics (10/16)
-whether experiencing job less pay cut due to COVID-19
Table S8: Feature groups in our intake/exit survey data. In parentheses next to each feature group, we provide [# of features we used from it} of features it contains initially].
C.2 IMPLEMENTATION DETAILS AND EXPERIMENTAL SETUPS
All experiments were conducted under Ubuntu 20.04 on a Linux virtual machine equipped with NVIDIA GeForce RTX 3050 Ti GPU and 12th Gen Intel(R) Core(TM) 17-1700H CPU @ 2.3GHz. We used PyTorch 1.13, CUDA 11.7, OpenAl 1.23, and scikit-learn 1.3. Code for MuHBoost is publicly available To We employed GPT-3.5 (Brown et al. 2020) via OpenAl's Completions API as utilized in Summary-
(gpt-3. 5 ww:uu-uuu-o-- wwwruuuboonssstte instruct) instead of GPT-3 Boost, which as of 2024 is deprecated. We opted not to consider GPT-4 since has been em- pirically shown in the work of Summary Boost that larger GPT models do not consistently improve upon smaller models. While GPT-4 may exhibit stronger predictive performance than GPT-3.5 for zero-shot and few-shot settings (Kim et al.) 2024)). we show in Table 3 that the improvements are negligible when aggregating weak learners via boosting. Hence, in practice, the extra incurred mon- etary cost (x6.7[nnput]/> 15[output] for GPT-4 and x3.3/x7.5 for the newly released GPT-4o with respect to GPT-3.5) from calling larger PLMS may outweigh the added benefits
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Fenture
Type
Question
Rule
Time
multi select
Yesterday, did you use any substances in any of these time intervals?
10:00 AM
RelUseSelf
singlo-seloct
How much would you say you used substances yesterday?
WhichUse
multi-selecc
What substances did you use yesterday? ? (check all that apply)
UseWith
multi select
Who did you use with yestenday? ? (check all that apply) Yesterday, did you drink alcohol in any of these time intervals?
UscoholUsed
multi-select
DrinkWith
multi-sclect
Who did you drink with yesterday? rehock all that apply) many drinks
NumDrinks
single-select
you have yestenday?
Numinteract
fill number
Enter the number of people you interacted with yestenlay? ? You must enter whole number (e.g. (:no one, mmone person, 2=two people, etc.)
FrucKnow
single select
Ofall the peuple you interacted with yesterday, how many knew that you times use substances?
RelUseOther
single-seloct
Think the people you yesterday who might be using substances them- solves. How mach do you think that they use?
CoUsse
selele
people you saw yestenday who might using substances. Did you and any of them use substances together yestenday?
Think
WhrUssWhoUse
single-select
Think about any stressful interactions you had yesterday. Which of these state- ments is most truc?
05:00 PM
StressWhoKnow
single-select
Think again about the stressful interactions you had yesterday, Which of these statements most true?
StressExxeen
single-select
Think about the stressful interactions you had yesterday. On scale of 10, how stressful were they?
Victim
multi-sllect
Did any of the following happen to you yesterday: (ebeck all that apply)
SupportWhoUise
single select
Think about people who have helped you or who have been supportive today. Which of these stateements most true?
08:00 PM
SuoportWhoKnow
single-select
Think again about people who have helped you who have been supportive today. Which of these statements most truc?
SupportExtent
select
Think about the supportive interactions you had today. On scale of 10. how supportive were they?
SnySupport
single-select
Did you seceive any support today?
Ty peSuupoort
multi select
What type of support did you receive today? (check all that apply)
TypeSupportOther
Please list what other type support you received.
Nervous
single-scloct
Today I felt nervous or anxious. (Agroe not?)
Annoyed
single-seloct
Today I was easily annoyed or irritable, (Agree or noc!)
Table S9: Features collected daily via ODIN app in chronological order. "multi-sclect features derived from check-all-thin -hatt-ppppl questions. (f) denotes questions that can be skipped from skip logic: If (i) Used is "I did not use any substances yesterday", (ii) AlcoholUsed is "I did not drink alcohol yesterday", and (iii) TypeSupport is not "Other".
For data conversion, we limit each data description to 200 words in order to accommodate as many data descriptions in the summarization prompt as possible As opposed to HommaryBoost. we do not preprocess numerical data (where they are supposedly binned into percentiles and encoded descriptively e.g., "low", "medium", "high"" to ensure minimal information loss, especially for nu- merical time series. We believe this omission can be counterbalanced by the fact that SummaryBoost originally employed GPT-3's Curie, a comparatively small and now deprecated model, and that bigger LLMs have shown much more encouraging capability in numerical reasoning (Englhardt jet al., 2024). The number of boosting rounds T is set to a large value of 100 for training till con- vergence (Lypically within 10-20 rounds) and the size of the representative subset & is set large as p (without exceeding the maximum context length) to 10. We set ju, the nonzero hyper- parameter for raising the bar for each weak learner, to O since we notice no significant increases in predictive performance otherwise. The stopping threshold at each round for both MuHBoost and MuHBoos([LPP]] defined by 1- 1 K - jr, is hence 1/ min N, 22) (below which is considered satisfactory and no further resampling is needed in accordance with SummaryBoost). For MuH- Boost[CC] (K 2), we introduce the discount factor ? = 0.95 into this threshold, which becomes 1- (1/2))²8 for each label E [0, Q 1J. to relax the training further down the chain (i.e., subject to error propagation). For the permutation function a required in this MuHBooos variant, we order the Q labels by descending frequency (i.e., labels with high proportion of positive samples come first), which has been shown to yield decent predictive performance (to be discussed shortly) among other heuristics (Wang et al. 2016 Nam et al. 2017).
Baselines. We consider the following methods from both problem transformation (+) and algorithm adaptation (+) approaches for MLC.
These configurations were shown to universally improve predictive performance according to the ablation studies from SummaryBoost..
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Zero-shot: Similarly in the work of SummaryBoost, we query the LLM with data description and directly infer its label. For MLC, this can be done in three ways: asking for either (i) Q labels one at time (hence Q separate calls to the LLM), (ii) Q labels simultaneously, or (iii) only its positive labels, which are reminiscent of BR, L.,, and LP+ approaches, respectively. We therefore denote them O-shot[BR], O-shot[L.P), and 0- shot[LP+]. Note that for single-label problems (Q = 1), O-shot[BR] is equivalent to the zero-shot prompting techniques developed in (Englhardi et al. 2024 Kim et al. 2024). We use GPT-4 (gppt -4-uurb)) given its superior performance compared to GPT-3.5 as shown by Kim et al. (2024).
Few-shot: Given the good performance of few-shot prompting methods considered in (Liu et al.3) Kim et al. 2024), we also include this baseline in our evaluations. We follow the few-shot setting as described in Summary Boottrs experiments, where we leverage their Cluster SenterrSapp. Samp 1 ing to select a representative subset of Xtrain for demonstration before asking the LLM to infer the label of some unseen sample. We set the number of shots to 10, which is consistent with fixed earlier. Similar to the zero-shot setting. there are three ways to ask the LLM for MLC, for which we denote as [0-shot[BR]. 10-shot[LP], and 10-shot[LP+]. We also use GPT-4 for the same stated reason 12
Additionally, we consider the following methods from classical ML algorithms. Given the various challenges in working with ubiquitous health data as discussed in Section 1 and the current lack of dedicated representation learning techniques for them to our best knowledge, following the bench- marking of CommaryBoosss we instead embed the data descriptions via OpenAl's embeddings API (text eettediibddg ada -002,, which are then fed into these baselines input features.
Random forest (Ho 1995) and XGBoost (Chen & Guestrin 2016) We consider problem transformation via both CC and LP when employing these baselines for MLCC, for which we denote respectively RF[CCJ, RF[LP], XGBosst[CC), and XGBoost[LP]..
Multi-label k Nearest Neighbours (MLKNN) (Zhang & Zhou| 2007 and twin multi-label SVM (MLTSVM) (Chen et al. 2016): Lastly, we include two well-established methods for MLC via algorithm adaptation, which respectively adapts ANN and SVM to MLC directly without requiring problem transformations.
Evaluation Metrics and Procedure. For each considered set of experimental configurations, we used the split ratio of 50/10/40 (for train/validationntest set), which follows HommaryyBosst eval- uation, for total of 10 different splits. It has been shown that conventional stratified sampling ap- proaches are not effective when partitioning multi-label datasets (Sechidis et al. (00T)) into training (train+validatin)
and test sets. We therefore adopt the iterative stratification algorithm proposed in the same cited work, which solves this issue by considering each label separately, starting from the one with fewest positive examples (i.e., most rare label) and working its way to the best represented one (i.e., most frequent label).
Since work with MLC problems having small to moderate Q. we accuracy (HA)= = a 10 = y} E [0, 1] metric for MLC (Tsoumakas & Katakis 2007 where y. 9 are vectors of ground-truth labels and their corresponding predictions. It can be described as the fraction of correctly predicted labels to Q given a sample, and hence we report the resulting summed- then-averaged value from all test samples. Since we encounter class imbalance for individual labels in 3 out of 4 datasets, we also consider so-called label-based metrics, namely micro- and macro- Eneraged-Fiio -mmeasure (Herrera et al. 2016). They are respectively defined by Nam et al. (2017) as Ee 2TP, miF, Σの (2TP, (P, and ma = = D Eg +NN, where TP, FP, FN stand for true
the standard Hamming
2IP,
positives, false positives, and false negatives from all test samples. Whereas the former evaluates the overall predictive performance of a classifier (and hence rewards those giving correct predictions on frequent labels), the latter focuses on assessing performance on rare labels.
Note that did not include the finetuning technique from Kim et al. 2024 in our baselines, which reportedly yielded the overall best performance, due the fact that the employed finetuned model has yet been released at the time of writing this paper.
fhttps: / / thub. com/ / sci k m-m i 1ea www.nnnnniiii. -it-mmllitttttttt 1ear anann (Syymáiski &
Kajdanowicz 2017)
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C.3 RESULTS
MuHBoost vs. MuHBoos([LP4]. Even though MuHBoost and MuHBoost[LP4 only differ in the inference directive essence, such modification has nontrivial impact as highlighted in Table SST (which extends Table D.. In particular, MuH[Boss(LPP4] generally outperforms its predecessor, and the improvement in predictive performance most noticeable when the number of labels Q is large (for PWUD dataset). This observation supports our claim in Section 3.3 that LLMs may hallucinate when being asked to answer multiple related questions simultaneously. In response, [uH alleviates this issue by simplifying the inference prompt, where instead of asking for an array of 'Yes' and 'No' in the exact order (e.g., listed in [DRUG LIST] from Figure 2). only requests the names of positive labels in any order. (MuHBoost[CC] achieves the same goal of alleviating hallucinations during inference, albeit by reformulating the MLC problem into multiple binary classification problems instead.) This greatly reduces the chance of inconsistencies between the output prediction array from LLMs and the corresponding input list of labels (e.g., [DRUG
LIST)).
LifeSnapst: Perform data conversion?
Method
LifeSnaps
GLOBEM+
CoSi+
PWUD+
Yes
0.734(44)
0.712(51)
0,701(49)
0.625(61)
No
Refine data description?
MallBoost
0.837(53)
0,824(60)
0.796(56)
0.734((3)
Yes
0.818447
0.815633)
0.785(54)
0,710K59)
0.625(58)
0.709453
0.731(29)
0.731(29)
0.720(43)
0.715(38)
0.688(54
0.667(64)
0.806(62)
0.836(46)
MaHBoos[[.Pa]
0.836(46)
0.829(62)
0.814(54)
0.784
0.649(57)
0.784(57)
0.822(40)
0.822(40)
0.817(59)
0.803(49)
0,771(41)
Table S11: Ablation of the (en- tire) data conversion procedure on LifeSnapst using MuH- Boost[LP+] with GPT-4.
Table S10: Detailed predictive performance MuHBoost MuHBoos[[LP]] from Table [ In each cell: (top) HA, (middle) miFi, (bottom) maFi.
Further Ablation. We ablate the data conversion procedure (in addition to the abstractive summa- rization step previously conducted in Table to better understand its overall impact on predictive performance. Because the set of representative samples from raw (serialized) date in the summa- rization prompt can easily exceed the context length of GPT-3.5, we use GPT-4 (with 30x longer context length). More specifically, we perform this ablation using MuHBoost[LPt] on LifeSnaps (with auxiliary data included). As shown in Table $11 there is sharp decline in predictive perfor- mance when prompting the LLM using raw data. This result demonstrates the importance of our data conversion procedure, which faithfully extracts the most relevant information from record and subsequently mitigates hallucinations in the LLM during model training and inference.
15 Du et al. 2021). which was Additional Benchmarking. We include another baseline, AdaRNN
recommended by the authors of GLOBEM as a potential solution longitudinal human behavior modeling. As shown in Table S12) MuHBoost (and hence its variants) still outperforms this method by a considerable margin, which again highlights the efficacy of our proposed methods.
Method
LiteSnaps+
GLOBEM+
COS++
PWUD=
0.656(41)
0.628(39)
0.614(33)
0,343(40)
AdaRNN
0.742(46)
0.735(44)
0.708(37)
0.636(52)
0.730(51)
0.729(41)
(466)
0.608(49)
0.734(44)
0.712(51)
0.701(49)
0.625(61)
MuHBoost
0.837(53)
0.824(60)
0.796(56)
0.734(63)
0,818(47)
0.815(63)
0.785(54)
0.710159)
Table S12:
Predictive performance of MuHBoost vs. AdaRNN. In each cell: (top) HA, (middle) Fi (bottom) maFi.
Computing, Time, and Monetary Resouree Consumption. MuHBoost requires minimal com- puting power by leveraging state-of-the-ard L.LMs via commercial APIs similarly as Summary Boost
"Recall that the extractive summarization step in data conversion proceuure ef trims each record within a reasonable word limit.
'https: // thub. com/ http:///.w...ooooo.eee.eeeeeee/a
adarnn
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does, which has the advantages of being gradient- gradient-free and operating without accessing and tuning the LLM's internal parameters (Manikandan et al. 2023). This property is particularly useful for domain experts to quickly incorporate the method into their usual practices. Since calling these L.Mss costs a certain amount as well as taking arbitrary runtime To it is important to analyze the time complexity and expected cost of MuHBoost and its variants Iy First, MuHBoost and [LP+] share the complexity with SummaryBoos and therefore take O(TM f), where I is the number of boosting rounds, .M is the number of resampling for the weak learner at each round, and is the LLM's runtime (see Appendix[D.. D.5 for an analysis). In terms of cost associated with training a model via MuHBoost or [LP+], according to the estimation by Manikandan et al. (2023), the upper bound for the total number of requested tokens is n,, = T x [M x (S, + 0.5N x P.) +.1N x Pt]. where St and P3 are the upper bounds of input tokens for summarization and inference (S, R P, x 8), respectively, and 0.5 and 0.1 are the split percentages chosen for the train and validation sets. For [CC], given that the CC approach transforms an MLC problem with Q labels into Q single-label classification problems, naive complexity analysis would simply add extra factor of Q for both time and cost. However, recall that the adoption of AdaBoost. as backbone for [CC] (Algorithm 2) allows asynchronous training for each label, which means it can be easily stopped in advance and excluded from further boosting rounds when needed. This neat property hence effectively reduces the extra factor Q' < Q. For comparison with the state of the art in longitudinal human behavior modeling, the finetuning approach from (Kim et al. (2024) (exhibiting the overall best performance) takes O(TN f) for one task, where T is the number of epochs, or ((QTNf) in total for an MLC problem. Thus, MuHBoost offers resource-efficien as well as effective solution for modeling ubiquitous health data.
[LP+] or [CC]? Given the overall best predictive performance of both MuHBoost variants, we summarize cases when one may be more favorable than the other in Figure S3 below.
Is Q large (~5 or more)?
Yes
No
[LP+]
Is cost or time concern?
Yeu
[.P+]
[CC]
Figure S3: Decision tree for considering the two MuHBoost variants.
Interpretability. Following SummaryBoost, MuHBoost and its variants also provide interpretabil- ity by storing LLM-generated knowledge regarding the task at hand into natural-language sum- maries. By this means, domain experts can directly inspect the factors that drive the LLM's predic- tions by looking at these summaries and their associated coefficients in all boosting rounds, which is valuable in practice for filtering erroneous summaries (done simply by tuning their coefficients to 0).
D MORE ON MUHBoOST
D.1 ILLUSTRATIVE EXAMPLE OF DATA CONVERSION PROCEDURE
Figure S4 provide an illustrative example of our data conversion procedure for LifeSnaps. which follows the template introduced in Figure I Note that since the time-series data from LifeSnaps contains only seven time points, we do not divide the record into multiple topics/subperiods.
16There could intermittent delays when calling GPT due to the API's rate limits (tttps://platform
openai i c.mm /tc///ccccccooccccccccc gui des/ rate- imits).
"Vaniila MuHBoost and MuH follow the same analysis.
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The depsect containe ecruur duily sloep, pcliiiillciiiiiyyy.oooo and hort rote over
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extrect words Do not add any additiionl inforiation.
summary should contain than 200
extracted dats
summarizer that follows the
You are an extractive
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Please revise the extrocted sumerary hased on the docusentt The revised summury should inchull the information extructdd summary.
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summarizer that the output pattern.
Document: (data description)
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prosents palicmn achoes days. The with levels lightly active misutes and deceut sloop elfisiency. thar day miltiple missing low activity minntes, On the fourth day, there was good sleep efficiency and higher deep sleep natoo The fifth day finvreed missing values and high maii of light sloep The siuih day showersed high sclivity lovels and good sloep efliciency, while the seventh day revcaled high stross scoecs, modentte activity minutes, and good sleep ellisiency.
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The danseet comalis bealth and psychological survey data over the spon of? days from study participants in Europe.
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Stages Chonge Quecoommain, parmcipant Mainommnno stgg Their Regrlttton Exerrier Quocummerr warn mosty except iddetiited regulasion' which socred 2.25 The partcipast scored esmovension, agreesblenes, consoenioumess subilii, incelis the International Persoulity Pool survey, They ideutiffid in male, with serving personalty trii calegories.
(b)
Figure $4:
Conversion of (a) time-series and (b) auxiliary data from LifeSnaps. The purple box contains the respective refined data description.
D.2 MUHBoOST(CC)
Motivations and Design Objectives. Conceptually, CC approaches MLC problems by linking Q binary classifiers in a 'chain', such that the output prediction of one classifier is appended as an additional feature to the input all subsequent classífiers (Read et al. [01I]). By this means, unlike BR, CC can model relationships between labels and hence yield improved predictive performance over BR approaches (Read et al. 2021). Compared to LP, CC is more reliable in practice since it does not suffer as much from the aforementioned rare label issue and can generalize to the case of multiclass MLC (i.e., some or all of the Q labels are multiclass) (Kocev et al. Read et all 2014), in exchange for being more computationally expensive (by factor of Q). CC is also prone
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the error propagation issue (i.e., the errors produced in the front may propagate down the chain and bring further errors, especially as the chain gets longer) and raises additional questions on how to best order the labels in the chain (Read et al. 202I). Therefore, we aim to address these bottlenecks when developing MuHBoost(CC)..
Chain Order. For determining the label ordering. AdaBoost.C2 heuristically reorders the labels in the chain at each boosting round by ascending training error rate. This aims to alleviate the error propagation issue in exchange for additional bookkeeping of the order matrix O, which is later accessed during inference. Formally, let H, = H[t, =] be the classifier chain at round /. whose order given by O = Olt, :]. During training, the prediction of H, given a training sample æ is an array of length Q, wherein 2²'- 4 is the atomic prediction for its 9th binary label y at round t and can be expressed as
gr. Hit, g)(2z=1)) x 0- 1 x x--2 Or
(S1)
x² = Z.
i] is the index of the label o' that immediately precedes q in Ot (i.e., clq'l < clg)), and so on. At inference time, the final prediction for y given an unseen sample is then produced by a weighted majority vote from the predictions of the classifiers H[:gg) in all boosting rounds, where the corresponding weights are Al: On the one hand, this method severely reduces interpretability since each round i. may potentially yields a different ordering O,, requiring extra efforts to interpret all Q predictions especially for larger boosting rounds T. On the other hand, its effectiveness is dubious as no related ablation studies were conducted (Li et al. 2023). Furthermore, Read et al. (202T) challenged this idea by demonstrating how poor predictions for particular label may potentially serve as good feature expansions for other labels' predictions. Combined with the fact that chain ordering is still an open problem (Scanagatta) al., 2019), following (Nam al., 2017),
Here, == =Ot, q] is the index of q in O2, 0 = Olt, q
therefore simply opt for a reasonable chain order defined by some heuristic permutation a and fix throughout the boosting process. The first two lines of Equation [S]] can hence be simplified as
g = H[1, q (o-1)), 1)),
(S2)
x: (9-1) = x ( By (
Note that in addition to chain ordering, we also order the labels according to a for the label infor- mation concatenated to each data description as well as the e.g. [DRUG LIST] in the summary and inference directives (Figure 2b. The same applies to MuHBoost and MuHBoos([LPt].
D.3 ONLINE BOOSTING
To ensure MuHBoos can efficiently update from the influx of new ubiquitous health data, we demonstrate how it can be extended to online learning settings as shown in Algorithm 3 Let R and 3, denote the number of boosting rounds and the coefficient of a weak learner at round r < R. respectively, in this online learning setting. The subroutine Co Caat incorporates new data into the existing dataset, which can be from either newcomers or additional time points for the same N participants. Note that our previously discussed data conversion step can accommodate both types of new data. After re-initializing the weights of the samples (Step 5), we select their representative subset via Cluster SummaryBoost in order to enrich the existing pool of T weak learners, H, which is obtained from the last checkpoint of MuHBoost (Steps 6-9). Then, Steps 10-15 constitute the online boosting procedure inspired by (Grabner et al.) 2006), where at each round r we first select the best weak learner h; in the pool subject to its training eтror rate (which depends on the sample weights w), The returned set of R weak learners, H. would serve as
inng and use it to build T up-to-date weak learners similarly as in
followed by computing B,, then updating the pool H in the next update. Notice this simple extension takes only ORf) instead of (RMJ)) were to retrain MuHBoost from scratct[T) where M is the number of resampling (of summaries) per round and f is the LLM's runtime in accordance with Manikandan et al. 2023).
18 Applics to its variants as well. MMuHBoost[CCC] requires another factor of Q for both.
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Algorithm 3 Online MuHSoost
Input: Xtroin. data exe pool weak Jearners H (hi,.... ., returned from last checkpoint of MuHiBoost (ie.. trained
1: Xtrais), number of boosting rounds R.
new empty arrays
length
2: H' t- new empll away of length T
3: Xirain t- Collaa e traine knww
4: N Xeraiin
5: w 4- new arryy of longth filled with t
6: for to de
7: X, Clwstee:Smmpiigg(g....... .....
8: H'[4] + Summary(X,)
9: <- Hf H²
total of x weak Teamers in the pool
10: -==
11:
for each he hi f- hy that yields lowest ct
Estimate errer
Xtrras using
Compute confficient frr for hū using ct
Update w using hū and B,
15:
7d[r] +- hg: Blr] +- B,
Return: M4,
This step can be repeated multiple times as needed. For instance, there are additional topics such as newly collected temporal data, we can simply prompt the LLM again to describe the e.g., "fourth 10-day" data.
D.4 SUMMARY OF How MUHBoOST COMPATIBLE UBIQUITOUS HEALTH DATA
Recall that ubiquitous health data brings three additional challenges existing ML approaches for TSC. Our proposed methods, MuHBoost and its variants, address them and stand out from existing
LLM-based approaches (Liu al. 2023 Englhardt al. 2024 Kim al. 2024) follows:
1. Long time series. MuHBoost divides long time series into multiple smaller chunks called 'topic" using automated procedure that leverages zero-shot summarization capabilities of advanced LLMs, whereas the aforementioned LLM-based approaches simply fit the entire record into the prompt, which might not be feasible given the employed LLM's context length or would result a decline in performance (Gruver et al. 2024 Liu al. 2024).
2. High rate of missing values. Although all LLMs can naturally accommodate any amount missing values (by denoting missingness with text placeholders such as *-" or 'NaN',, for time-series data in particular, they may encounter difficulties interpreting the tempo- ral pattern of multiple features for a record when its missing pattern arbitrary (Figure S5p). Given that all cited LLM-based approaches fit the entire record into the prompt with minimal data preprocessing, such shortcoming from L.MSs applies to these approaches as well. In contrast, MuHBoost prompts the employed LLM to describe each record in natu- ral language, which directly focuses on the temporal pattern of each feature and hence can effectively deal with any data missing pattern.
3. (Very) Small sample size. LLMs can effectively deal with time-series data of small sample size from their encoded prior knowledge (Gruver al. (0244), and hence all LLM-based approaches including ours have shown to work well on small datasets. However, existing approaches were not evaluated on more general ubiquitous health datasets with heteroge- neous time series commonly encountered in longitudinal human behavior modeling.
Time
Time
Feature 2
Feature
3
(a)
Figure 85: Example of (a) non-arbitrary vs. (b) arbitrary data missing pattern in multivariate time- series data, where each black cell denotes missing value.
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D.5 COMPLEXITY ANALYSIS AND FLOPS ESTIMATION
Time Complexity. Algorithm 4 shows the complete procedure for training MuHBoost and MuH- [oost[[PP]] which follows the boosting process of SummaryBoost (Algorithm 2 in (Manikandan al. ).23)). At boosting round & E (1...., . T. candidate summary or weak learner h[& for the representative set of data descriptions (X,, y.) is sampled by prompting the LLM (Step 5). Then, we compute the training error e for h[t] to determine if it performs at least better than random guess- ing (Step 6). which involves calling the LLM N times for inference. Let f be the arbitrary runtime [6) e is not satisfactory, we repeat the process (Steps 7-8) and hence resample h[t] for total of M > 1 times. Once the desired weak learner is found after O(M we compute its coefficient (Step 9), recompute and re-normalize the sample distribution (Steps 10-11), and compute the validation error for round only once. Thus, the final complexity is f).
of the LLM for both summarization and inference.
Algorithm 4 MuHBBoost and MullBoos([PP]]
Input: Training data Xerain with sample size N. label aray Yerain length N. maximum number boosting rounds T. size of the
1: h. representative subset hyperparameter μA E [0, 0.5).
emply arnys of length T
2: w t- new arry of length N filled with x
3: for 1 = to
4: ( 4- ClusterSampl (ing( trasns ( Verains w,s)
Algorithm 0
5: h[(] + (oumaary(X,,.>
Summaxyy prompes the LLM summarize the representative samples
6:
EN wit]x((hei)) (x (eat())))
is the training error enforces higher-quality summaries for accelerating convergence
EE w(s)
7:
i 0.5 y then
8:
Go Step
9:
a[r] log (2=9)
10:
for to N do
11:
w|1] - w/i] alt) (lll (1)))))))
12:
Normal 11 (w)
Return:
FLOPs Estimation. MuHBoost queries ny tokens in total during training as previously discussed. Therefore, according to Kaplan et al. (2020) from OpenAl, it requires approximately 2mparm FLOPs (i.e., add-multiply operations) to train MuHBoos [7 where Rparam is the number of LLM parameters (175 billions for GPT-3.5). Assuming we are dealing with a dataset consisting of = 100 samples and we respectively allocate 50% and 10% of them for training and validation. In this case, n4 would be 15 rounds x [10 resampling x (3072 summary tokens (0.5 x 100 training samples) x 333 prediction tokens) + (0.1 x 100 validation samples) x 333 prediction tokens] x million tokens. Thus, training MuHBoost on this dataset takes 1.05 x 10 FLOPs or 1.05 x 10 GFLOPs, and training MuHBoost[CCC takes additional factor of Q'.
D.6 LIMITATIONS AND FUTURE WORK
MuHBoost and its variants were developed to deal with ubiquitous health datasets that typically have small sample size. Therefore, we do not expect our proposed methods to scale to larger datasets (i.e., with thousands of samples) in terms of resource efficiency. When the opportunity work with large ubiquitous health data arises, approaches that require ample sample size such as LLM finetuning become more suitable than ours i.e., boosting LLM-generated weak learners. Regardless, some core ideas of our work can still apply to these approaches. More specifically, our data conversion procedure and prompt engineering techniques when developing MuHBoost and MuHBoostt[LP4, for MLC can be leveraged to effectively (i) deal with heterogencous time series and (ii) reduce resource consumption as discussed throughout the paper.
Although our data conversion procedure can deal with long time series, we only experimented on datasets with at most 30 time points (PWUD). We were not able to extend our experiments to longer time series due to the limitations of currently available ubiquitous health datasets. We believe the release of datasets that capture human behaviors at finer scales would further validate the efficacy of methods in addressing this challenge.
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Published as a conference paper at ICLR 2025
D.7 BACKGROUND ON SUOMARYBOOST
SummaryBor Boost was proposed as a novel approach to address tabular data classification under small sample size and high heterogeneity (i.e., containing different feature types, e.g., categorical and tinuous as well as missing values) (Manikandan et al., 2023). Its novelty is two-fold. First, because has been shown that data preprocessing is particularly important for deep learning approaches when working with tabular data Borisov et al. (2022), the majority of works in this area focus on extensive data preprocessing techniques. SummaryBooss on the other hand, leverages the natural capabilities of LLMs in text summarization to convert tabular data records into natural language de- scriptions, which cireumvents the difficulties stemmed from data heterogeneity altogether. Second, SummaryBood Boost incorporates LLMs into traditional boosting framework (i.e., AdaBoost) by itera- tively asking them to summarize a given set representative samples from the (transformed) data, the output of which serves weak learner for the current boosting round. This approach is espe- cially useful for small sample size settings (i.e., S $100 samples) since the prior knowledge encoded into LLMS (which has improved tremendously improved predictive performance as sufficient J.L---enrrated weak learners are included.
the years) be fully exploited, resulting in
Because real- world ubiquitous health data share the same stated challenges tabular data, be- lieve these two novel ideas from Summary Boost can be applied to our problem.
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Reviews:
[{"review_id": "nyHEqSDBX2", "reviewer": "Reviewer_wDHz", "summary": "The paper proposes a novel approach named MuHBoost to model longitudinal human behavior. It is based on large language models (LLM) and aims to address the limitations of prior related works: inadequate consideration of realistic data type and the consumption of computing resources.", "questions": "1.\tIn Section 1, the authors need more explicit narration of how their model tackles the three challenges presented. Adding a clearer distinction between MuHBoost and previous LLM-based approaches would help readers better understand the unique contributions of the model.\n\n2.\tIn Section 2, most of the Multi-Label Classification related works are a bit out-of-date, more recent works (2023/2024) should be included.\n\n3.\tIn Section 3, “the increased computational burden due to longer and more complex prompts may lead to hallucinations from LLMs”needs more solid proof. The hallucination of LLM is a widely and deeply studied topic, the authors haven’t provided clearly (experimentally/theoretically) how their proposed method addresses this issue.  For section 3.1: The data transformation process could be graphically illustrated to make it clearer to readers who are not familiar with data aggregation techniques.\n\n4.\tIn Section 4, some of the compared baselines are too old (e.g., Random Forest and XGBoost). The state-of-the-art results are not convincing enough. \n\n5.\tIn Section 4 and Appendix C.3, the resource consumption problem is not fully elaborated. The time complexity analysis needs further proof. In addition, more experimental indicators (e.g., FLOPS) should be included to prove the efficiency of the proposed method.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. the proposal of MuHBoost as a resource-efficient multi-label classification method holds promise for solving the problem of multi-outcome health prediction.\n2. The inclusion of both time series and ancillary data demonstrates the versatility of dealing with heterogeneous data formats common in health behavior modeling.\n3. Comparative analyses show that MuHBoost generally outperforms benchmark methods on a variety of datasets, demonstrating its effectiveness.", "weaknesses": "1.The limited sample size of some datasets may affect the generalizability of the findings. This issue is only briefly acknowledged.\n2. Scalability issues remain, especially in terms of the computational resources required when using MuHBoost for larger datasets or a wider range of applications.\n3. The ideas presented in the article should be supported by more detailed derivation of the principle formulas.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "p6IngveqnO", "reviewer": "Reviewer_aJj3", "summary": "This paper proposes a LLM-based method for multi-label classification, with focus on longitudinal human behavior. Built on SummaryBoost, this paper aims to address two limitations in the existing framework. First, the data could be heterogeneous in terms of data type (e.g., continuous measurements such as heart rate, and categorical responses, such as EMA data). Second, minimize the consumption of computing, time etc for inference. The proposed MuHBoost can handle the heterogeneous data type and enable efficient MLC. At the same time, two variants are proposed to mitigate hallucinations from LLMs. Extensive numerical experiments are conducted to evaluate the performance of the proposal, showing its potential advantages in longitudinal human behavior.", "questions": "1. In the original SummaryBoost paper, the numerical results show that SummaryBoost actually perform worse than traditional Xgboost when datasets have many continuous features. I think this is also the case for longitudinal human behavior data, such as heart rate. Did authors observe this phenomenon in the experiments? \n\n2. Following the question 1, what are the input for MLkNN and MLTSVM? are they original measurements or embedding of text information or something else?\n\n3. I think it worth comparing the proposed method with some traditional machine learning algorithms dealing with longitudinal data, such as RNN-type methods.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper identifies some significant issues of the existing LLM-based prediction methods, especially when dealing with multi-labels in longitudinal human behavior data. \n\n2. The paper is well-written, and easy to follow, even if I do not have much research experience in this area.\n\n3. The proposed enhancement for MuHBoost is helpful for small sample size problem, where N < 2^Q.", "weaknesses": "1. I don't think the proposed method can handle realistic high-dimensional time series, for example, movement, heart rate data collected from smartwatch. In the numerical experiments, the highest dimensionality of consideration is less than 30. Traditional machine learning, such as (Zhang et al., 2024) can handle very high frequency time series data (seconds, minutes), which can capture the human behavior in much finer level. Therefore, I think it is essential to discuss the limitations of the proposed method for high-frequency time series data, or justify the performance in numerical experiments, for example, authors can test the proposal on LifeSnaps dataset with much finer data. In addition, the authors may compare the proposal with the aforementioned method for prediction. If it is difficult to handle high-frequency time series data in the current proposal, authors may discuss how their method can be extended to those realistic settings.\n\nReference: Zhang, J., Xue, F., Xu, Q., Lee, J. and Qu, A., 2024. Individualized dynamic latent factor model for multi-resolutional data with application to mobile health. Biometrika, p.asae015.\n\n2. I think the enhancement of MuHBoost in Section 3.3 lack novelties. Two approaches are largely motivated by two papers mentioned in the text. Specifically, LP+ is motivated by (Nam et al., 2017) while CC relies on the AdaBoost.C2 (Li et al., 2023). Please emphasize the novelties differentiated from these papers. Emphasize its benefits when adapting these approaches to the MuHBoost.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qM3wFSeH9x", "reviewer": "Reviewer_8yZu", "summary": "This paper introduces MuHBoost, a multi-label boosting approach for predicting health and well-being outcomes from longitudinal human behavior data. MuHBoost is designed to address challenges associated with heterogeneous data, missing values, and computational demands in existing models. By incorporating LLM prompting with multi-label classification, MuHBoost aims to predict multiple health outcomes concurrently. Additionally, two variants of MuHBoost are proposed to manage potential hallucinations in LLMs, potentially enhancing model robustness. Evaluated on 13 prediction tasks using four health datasets, MuHBoost reportedly outperforms baseline models across multiple metrics, with resource efficiency highlighted as a key feature", "questions": "Comments:\n* \\cite is incorrectly used throughout the paper when \\citep should be used", "limitations": "", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "* Demonstrates how LLMs can be used as multi-label boosting method for temporal tabular data and auxiliary tabular data.", "weaknesses": "* Iterative improvements on prior SummaryBoost work with limited novelty\n\t* The new data conversion uses a seemingly straightforward two-step approach based on LLM prompting in Section 3.1. The prompting itself does not seem to be very innovative, and while Table 2 does demonstrate an empirical performance increase, it is unclear how exactly each step of the two step approach helped.\n\t* MuHBoost modifies the original ClusterSampling algorithm by emphasizing rarer classes in the sampling procedure, then the follow-up methodologies MuHBoost[LP+] and MuHBoost[CC] seem to be straightforward adaptations of prior methods from Nam et al., 2017 and Adaboost.C2. \n\t* Prior work section does not seem to contextualize the importance and relevance of SummaryBoost, especially in context of the longitudinal ubiquitous computing domain.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "msOa40nVpo", "reviewer": "Reviewer_qBMd", "summary": "The authors consider the task of using longitudinal ubiquitous health data to predict human behavior. They create a model, called MuHBoost, based on both LLMs (SummaryBoost model methodology) and multilabel classification (label powerset and classifier chain methods). The authors compare their model to various baselines and show that MuHBoost outperforms all baselines and presents two key advantages: (i) MuHBoost can consider different feature types and missing values and (ii) MuHBoost requires less computing time and cost. To address LLM hallucination the authors also build two extensions of their base model (which result in better performance). The authors apply their model to 4 datasets to predict psychology, student performance, and drug use.", "questions": "1. How do the authors combine data descriptions across topics?\n2. For the two psychology datasets (LifeSnaps and GLOBEM), why are some tasks (e.g., negative emotions or anxiety) defined differently across datasets?\n3. When describing baselines in Section 4.2, what do problem transformation and algorithm adaptation mean?\n\nMinor comments / suggestions: \n\n- Throughout I recommend using parenthetical citations (citep instead of cite)\n- At multiple points, footnotes referenced in the main text were placed in the appendix. Could each footnote be placed on the page they are first referenced on?\n- Line 081: “have the two following”\n- Line 206: “require” instead of “requires”", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "S1 - Important application. The authors investigate an important application of longitudinal human behavior modeling with applications to patient monitoring, interventions for drug users, etc.\n\nS2 - Multiple datasets: The authors evaluate their model on multiple, diverse datasets with a range on the number of labels (ranging from 2 to 6 labels).\n\nS3 - Multiple baselines and metrics: The authors compare their model to a diverse set of baselines and evaluate on multiple standard MLC metrics.\n\nS4 - Multiple robustness checks: The authors run various robustness checks including on SummaryBoost’s extract-then-refine procedure and on the use of GPT4 (more resource expensive) vs GPT3.5 (less resource expensive).", "weaknesses": "W1 - Needs more background clarification: The main weakness of the paper is a lack of background clarification. The authors should give more clarification on the SummaryBoost, LP and CC methods which the MuHBoost model is built off of.\n\nW2 - Comparison to multilabel text classification: The authors state that the use of LLMs for MLC type prediction tasks have been done in multilabel text classification works. How do these methods compare to MuHBost?\n\nW3 - Statistical significance: Are the results in table 1 statistically significant?\n\nW4 - Only binary class predictions: The authors claim that MuHBoost presents an advantage over prior work because their model can consider different feature types. However, all experiments run in the paper only consider binary class predictions. Have the authors evaluated the model on other feature types as well (e.g. categorical/continuous prediction)?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)