Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data
Authors: Rumeng Li
Xun Wang
hong yu
Venue: EMNLP 2023 Findings (2023)
Keywords: Large language model
clinical text processing
Alzheimer's Disease
data augmentation
TL;DR: We use LLM to create data for AD-related symptoms identification/classification.

Abstract: Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and imbalanced medical data, is under-explored. We investigate whether LLMs can augment clinical data for detecting Alzheimer's Disease (AD)-related signs and symptoms from electronic health records (EHRs), a challenging task that requires high expertise. We create a novel pragmatic taxonomy for AD sign and symptom progression based on expert knowledge and generated three datasets: (1) a gold dataset annotated by human experts on longitudinal EHRs of AD patients; (2) a silver dataset created by the data-to-label method, which labels sentences from a public EHR collection with AD-related signs and symptoms; and (3) a bronze dataset created by the label-to-data method which generates sentences with AD-related signs and symptoms based on the label definition.
We train a system to detect AD-related signs and symptoms from EHRs. We find that the silver and bronze datasets improves the system performance, outperforming the system using only the gold dataset. This shows that LLMs can generate synthetic clinical data for a complex task by incorporating expert knowledge, and our label-to-data method can produce datasets that are free of sensitive information, while maintaining acceptable quality.

Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data
Rumeng Li and Xun Wang² and Hong Yu1.2.4 Umass Amherst, Amherst, MA, USA
2 VA Bedford Healthcare System, Bedford, MA, USA
Microsoff, Redmond, WA, USA Umass Lowell, Lowell, MA, USA
Abstract
two major obstacles: the scarcity and sensitivity of medical data. Medical data is often limited in quantity and diversity, due to the high cost and difficulty of data collection and annotation, which require expert knowledge and consent from patients and providers. On the other hand, medical data is highly sensitive and confidential, due to the ethi- cal and legal issues of data privacy and security, which impose strict regulations and restrictions on data collection and usage (Berman, 2002). These obstacles hinder the development and evaluation of clinical text mining methods, especially those based on data-hungry deep learning models.
Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text min- ing, domain with scarce, sensitive, and im- balanced medical data, is under-explored. We investigate whether LLMSs can augment clinical data for detecting Alzheimer's 's Disease (AD)- related signs and symptoms from electronic health records (EHRs), challenging task that requires high expertise. We create a novel prag- matic taxonomy for sign and symptom pro- gression based on expert knowledge and gener- ated three datasets: (1) gold dataset annotated by human experts longitudinal EHRs of AD patients; (2) a silver dataset created by the data- to-label method, which labels sentences from public EHR collection with AD-related signs and symptoms; and (3) bronze dataset cre- ated by the label-to- do-a method which gener- ates sentences with AD-related signs and symp- toms based on the label definition. We train system to detect AD-related signs and symp- toms from EHRs. We find that the silver and bronze datasets improves the system perfor- mance, outperforming the system using only the gold dataset. This shows that LLMs can generate synthetic clinical data for a complex task by incorporating expert knowledge, and our label-to-data method can produce datasets that are free sensitive information, while maintaining acceptable quality.
Recently L.Ms have demonstrated impressive performance on many natural language processing (NLP) benchmarks, (Wang et al., 201 8a, 2019; Ra- jpurkar et al., 2016), as well as in medical domain applications (Singhal et al., 2023, 2022: Nori al., 2023). However, they also face some common problems like hallucination, homogenisation, etc (Azamfirei et al., 2023). Hallucination means that LLMs produce factual errors or inconsistencies their outputs that do not match the input or the real world. This can damage the reliability and credibil- ity of LLMs, especially for applications in clinical domain that need high accuracy and consistency. In addition, data generated by LLMs tends be highly homogeneous and fails capture the diver- sity and realism of real data, which are essential for many downstream tasks. For example, for clinical text analysis, we need the generated data to cover different types of clinical texts, such as patient his- tories, diagnoses, treatment plans. This presents huge challenge for LLM. The difference between LLM generated data and real data makes people doubt LLMs' practical application value.
1 Introduction
Clinical text holds a large amount of valuable in- formation that is not recorded by the structured data fields in electronic health records (Wang et al., 2018b). Clinical text mining, which aims to ex- tract and analyze information from medical records, such diagnosis, symptoms, treatments, and out- comes, has various applications, such as clinical decision support, disease surveillance, patient ed- ucation, and biomedical research (Murdoch and Detsky, 2013). However, clinical text mining faces
In this paper, we investigated whether the out- puts of LLMs can be valuable data source for clin- ical text mining despite all these aforementioned drawbacks. We focus on Alzheimer's Disease (AD) signs and symptoms detection from electronic

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health records (EHRs) notes. Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide (Schel- tens et al., 2021; Schachter and Davis, 2022). It can cause cognitive impairment, behavioral changes, and functional decline. Detecting AD-related signs and symptoms from EHR is a crucial task for early diagnosis, treatment, and care planning (Leifer, 2003). In addition to the scareity, sensitivity, and imbalance of clinical data, this task is highly chal- lenging due to the high expertise required to inter- pret the complex and diverse manifestations of AD (Dubois et al., 2021).
LLM can be applied medical data annotation, and even its hallucinations can be leveraged crc- ate datasets that are free of sensitive information, while preserving acceptable quality.
The contributions of this paper are as follows: We create novel pragmatic taxonomy for AD expert
sign and symptom progression based knowledge, and it has shown to be reliably anno- tated using information described in EHR notes.
We investigate whether LLMs can augment clinical data for detecting AD-related signs and symptoms from EHRs, using two different meth- ods: data-to-labed and label-to-data.
We train system detect AD-related signs and symptoms from EHRs, using three datasets: gold, silver. and bronze. And evaluate the qual- ity of the synthetic data generated by LLMs using both automatic and human metrics. We show that using the synthetic data improves the system per- formances, outperforming the system using only the gold dataset.
We propose a novel pragmatic taxonomy for AD sign and symptom progression based on expert knowledge, which consists of nine categories that capture the cognitive, behavioral, and functional aspects of AD (Bature et al., 2017; Lanctôt et al., 2017). We created three datasets following the taxonomy: (1) a gold dataset annotated by human experts on longitudinal EHRs of AD patients; (2) a silver dataset created by the data-to-label method which labels sentences from public EHR collec- tion with AD-related signs and symptoms; and (3) a bronze dataset created by the label-to-data method which generates sentences with AD-related signs and symptoms based on the label definition. The "data-to-label" method employs LLMs annota- tors and has been widely adopted in many tasks. The "label-to-data", on the other hand, relies on the LLM's generation ability to produce data with labels based on instructions.
2 Related Work
2.1 Large Language Models
Large language models (LLMs) have enabled re- markable advances in many NLP domains because of their excellent results and ability to comprehend natural language. Popular LLMs including GPT-2 (Radford et al., 2019), GPT-3 (Brown et al., 2020), and GPT-4 (OpenAI, 2023), LaMDA (Thoppilan et al., 2022), BLOOM (Scao et al., 2022), LLaMA (Touvron al., 2023). etc. vary in their model size, data size, training objective, and generation strat- egy, but they all share the common feature of being able to generate natural language texts across vari- ous domains and tasks. They have achieved impres- sive results on many natural language processing (NLP) benchmarks by leveraging the large-scale and diverse text data from the web.
We performed experiments of binary classifica- tion (whether the sentence is related to AD signs and symptoms or not) and multi-class classifica- tion (assign one category from the nine pre-defined categories of AD signs and symptoms to an input sentence), using different data combinations to fine- tune pre-trained language models (PLMs), and we compared their performance on the human anno- tated gold test set. We observed that the system performances can be significantly improved by the silver and bronze datasets. In particular, combing the gold and bronze dataset, which is generated by the label-to-data method, outperform the model trained only the gold gold+silver dataset for some categories. The minority classes with much fewer gold data samples benefit more from the im- provement. We noticed slight degradation of per- formances for a small proportion of categories. But the overall increases in results demonstrates that
However, researchers have noticed the draw- backs of LLMs since their debut. Some limita- tions of LLMs have widely acknowledged and have drawn wide attentions from the research com- munity like hallucination, homogenisation, etc. (Tamkin et al., 2021)
Hallucination is well-known and widely- studied problem in natural language generation (NLG), which is often defined as "generated con- tent that is nonsensical or unfaithful to the provided source content" (Ji et al., 2023). Hallucination has been observed and analyzed in various NLG tasks,

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such as machine translation (MT) (Guerreiro et al.. 2023), text summarization (Cao et al., 2021), and dialogue generation (Das et al., 2023). Hallucina- tion can be caused by various factors, such as data noise, model bias, lack of commonsense knowl- edge, or insufficient supervision. It can be detected and mitigated by various methods. such as data cleaning, model regularization, knowledge injec- tion, or output verification (Ji et al., 2023).
data, or cross-domain data, enrich improve the data representation or diversity (Che al., 2017; Gligic et al., 2020; Gupta et al., 2018; Xiao et al., 2018; Amin-Nejad et al., 2020; Li et al., 2021).
However, synthetic datasets may not capture the naturalness and realism of human- medical texts, and may introduce errors or biases that can affect the performance and validity of clinical text mining methods.
In this paper, we explore a different angie on hal- lucination, and examine whether the hallucinations of LLMs can be valuable data source for clinical text processing, rather than difficulty. We propose that the hallucinations of LLMs can be leveraged create synthetic or augmented datasets that do not expose sensitive information, but still maintain the linguistic and semantic features of clinical texts, such as vocabulary, syntax, and domain knowledge, Experiments on classification tasks confirmed the validity of the proposal.
LLMs have also been explored for clinical text processing. Research has demonstrated that LLM holds health information (Singhal et al., 2022). Studies has shown that LLMs can generate unstruc- tured data from structured inputs and benefit down- stream tasks (Tang et al., 2023). There are also some work that leveraged L.MSs for clineal data augmentation (Chintagunta et al., 2021; Guo et al., 2023) In this paper, we propose a novel approach to leverage LLMs, especially its hallucination abil- ity via the label-to-data method as data source for clinical text processing, which can mitigate the scarcity and sensitivity of medical data.
Homogenisation is also a potential drawback of using LLMs at a large scale. While it ensures the stability of the text quality, it also reduces the diversity of text. This issue has been noticed and discussed (Marian, 2023), but there is still lack of research in this direction. In this work, we have observed homogenisation in the "label- to-data" method and conducted experiment which help reveal how it impacts the system performances on the studied task.
2.3 Alzheimer's disease signs and symptoms detection
Clinical text mining methods have been increas- ingly applied to detect AD or identify AD signs and symptoms from spontaneous speech or elec- tronic health records, which could be potentially severed as natural and non-invasive way of as- sessing cognitive and linguistic functions. (Kar- lekar et al., 2018) applied neural models to classify and analyze the linguistic characteristics of AD patients using the DementiaBank dataset. (Wang et al., 2021) developed a deep learning model for earlier detection of cognitive decline from clinical notes in EHRs. (Liu and Yuan, 2021) I) used a novel NLP method based on term frequency-invert doc- ument frequency (TF-IDF) to detect AD from the dialogue contents of the Predictive Challenge of Alzheimer's 's Disease. (Agbavor and Liang, 2022) used large language models predict dementia from spontaneous speech, using the DementiaBank and Pitt Corpus datasets. These studies demon- strate the potential of clinical mining methods for assisting diagnosis of AD and analyzing lexical performance in clinical settings.
2.2 Clinical Text Mining and synthetic data generation
Clinical text mining faces two main obstacles: the limited availability and the confidentiality of health data. Various attempts have been done to over- come the lack of and the privacy issues with health data. Public datasets, such as MIMIC (Johnson et al., 2016), i2b2 (Uzuner et al., 2011), or BioASQ (Tsatsaronis al., 2015) etc, are openly available for research purposes. Synthetic datasets, such as Synthea (Walonoski et al., 2018) and MedGAN (Choi et al., 2017) etc., are constructed based on sta- tistical models, generative models, or rules. They can be used to augment or complement real medical data, without violating the privacy confidential- ity of the patients or providers. Data augmentation transfer learning techniques are machine learn- ing techniques used to address the data scarcity or imbalance issue by generating or utilizing addi- tional or related data, such as synthetic data, noisy
Methodology
In this section, we introduce our task of AD signs and symptoms detection, and how we leveraged

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LLMs's capabilities for medical data annotation and generation. We also present the three different datasets (gold, silver and bronze) that we created and used for training and evaluating classifiers for AD signs and symptoms detection.
as output, and resolved the disagreements by dis- cussion. The inter-annotator agreement was mea- sured by Cohen's kappa as k=0.868, indicating a high level of reliability. This leads to the gold stan- dard dataset with 16,194 194 sentences with a mean (SD) sentence length of 17.60 (12.69) tokens.
3.1 Task overview
3.2.2 Silver data (Data-to-Label)
Alzheimer's disease (AD) is a neurodegenerative disorder that affects memory, thinking, reasoning, judgment, communication, and behavior. fifth-leading cause of death among Americans age 65 and older (Mucke, 2009). This task aims to identify nine categories of AD signs and symp- toms from unstructured clinical notes. The cat- egories are: Cognitive impairment, Notice/con- cern by others, Require assistancc/functiinaal im- pairment, Physiological changes, Cognitive assess- ments, Cognitive intervention/therapy, Diagnostic tests, Coping strategy, and Neuropsychiatric symp- toms. These categories indicate the stages and severity of AD. Capturing them in unstructured EHRs can help with early diagnosis and interven- tion, appropriate care and support, disease mon- itoring and treatment evaluation, and quality of life improvement for people with AD and their caregivers. This is very challenging lask as the AD-related signs and symptoms can vary in form and severity, and requires lot of knowledge and experience to capture them from a large amount of text. We ask experts to create the annotation guideline by defining each category of AD-related signs and symptoms and providing examples and instructions for the annotators (See Appendix A for details).
The silver dataset consisted of 16,069 sentences with a mean (SD) sentence length of 19.60 (15.44) tokens extracted from the MIMIC-III database (Johnson et al., 2016), which is large collection of de-identified clinical notes from intensive care units. We randomly sampled the sentences from the discharge summaries, and used the LLM model to annotate them with AD-related symptoms. The LLM receives the annotation guidelines and the clinical text as input and produces the sentence to be annotated and its categories as output. The out- puts are further checked by the LLM by asking for a reason to explain why the sentence belongs to the assigned category. In this step, the inputs to the LLM are the guidelines and the annotated sentences and the outputs are Boolean values and explanations. This chain-of-thoughts style check- ing has been proved to improve LLM performances (Wei et al., 2022). Although many LLMs can be used here, we adopt the Llama 65B (Touvron et al., 2023) due to its performances, availability, costs and privacy concerns.
the
3.2.3 Bronze data (Label-to-Data)
We used GPT-4, a state-of-the-art LLM that has been shown to generate coherent and diverse texts across various domains and tasks (OpenAI, 2023). GPT-4 is a transformer-basee model with billions of parameters, trained on large corpus of web texts. We accessed GPT-4 through the Azure Ope- nAT service
3.2 Datasets
As stated above, we created and utilized three dif- ferent datasets for our experiments: gold, silver and bronze.
In the generation task, GPT-4 takes only the an- notation guidelines as input and produces a piece of note text and outputs the sentence to be annotated and its categories. This is a bronze-level dataset that does not contain any sensitive personal infor- mation. It consists of 16,047 sentences with an average sentence length of 16.58 words. Figure shows snippet of the generated text and annota- tions. As shown in the example, the model firstly generates clinical text and then extract sentences of interests for annotation. The generated text is
3.2.1 Gold data (Human annotation)
We expert annotated 5112 longitudinal EHR notes of 76 patients with AD from the U.S. Department of Veterans Affairs Veterans Health Administration (VHA). The use of the data has been approved by the Institutional Review Board at the VHA Bed- ford Healtheare System, which also approved the waiver of documentation of informed consent. Un- der physician supervision, two medical profession- als annotate the notes for AD signs and symptoms following the annotation guidelines. They selected sentences to be annotated and labelled its categories
wtt.ainssw...oooooooo/oooecccceo services/opemmiiserviicd

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rich in AD signs and symptoms and contains no Protected Health Information (PHI) or (Personally Identifiable Information) Pll. While the text doesn't t completely convey the complexity of AD diagnosis, or the follow-up required to arrive at AD diagnosis (e.g. "I immediately drove over and took him to the ER. After series of tests, including an MRI and neuropsychological evaluation, he was diagnosed with Alzheimer's disease. In fact, the diagnosis of AD is challenging task and it's unlikely to get diagnosed with AD at the ER department), maintains the linguistic and semantic features of clinical texts, i.e., vocabulary, syntax, and domain knowledge, and represents high quality annotation.
optimizer (with learning rate of le-4 and batch size of 32), and 10 epochs for training. We select the best checkpoints based on the validation accu- racy for testing. The outputs are determined by a majority vote strategy.
We use subset of the gold dataset as the test set. We compare the performances using accuracy, precision, recall, and Fi-score.
3.3.2 Data Combinations
Gold only We fine-tuned the PLMs only on the training set of the gold data and evaluate the perfor- mance on the test set of the gold data.
Bronze + Gold, we fine-tuned the PLMs on the bronze data and further fine-tuned them on the train- ing set of the gold data and evaluate the perfor- mance on the test set of the gold data.
We processed the datasets by tokenizing, lower- casing, and removing duplicate sentences. We split them into train, validation, and test sets (80/10/10 ratio). Table shows the dataset statistics, The gold and silver data have similar average length and standard deviation. The bronze data has a smaller standard deviation and more balanced categorical distribution than the gold and silver data, which differs from real patient notes. The smaller SD in sentence lengths indicates less diver- sity in the bronze data. We will experiment with these datasets to see how the LLM's output can help clinical text mining.
Silver Gold, we fine-tuned the PLMs on the sil- ver data and further fine-tuned them on the training set of the gold data and evaluate the performance on the test set of the gold data.
Bronze + Silver + Gold, we fine-tuned the PLMs on the combination of the bronze data and silver data, and further fine-tuned them on the training set of the gold data and evaluate the performance on the test set of the gold data.
For each data setting, we trained the models as a binary classifier and multi-class classifier. For the binary classification task, we randomly sampled sentences with no AD signs and symptoms from the longitudinal notes of the 76 patients, where our gold data comes from, negative data. The nega- tive/positive data ratio is 5:1 2. The task is to test whether this sentences is AD signs and symptoms relevant or not. For the multi-class classification task, the classifiers need to identify specifically one category that the sentence belongs to among our nine categories of AD signs and symptoms.
3.3 Experiments
3.3.1 Classifiers
We use an ensemble method that integrates mul- tiple models and relies on voting to produce the final output. This reduces the variance and bias of individual models and enhances the accuracy and generalization of the prediction, which is cru- cial for clinical text mining (Mohammed and Kora, 2023).
For the base models, we utilized the power of pre-trained language models (PLMs), which are neural networks that have been trained on large amounts of text data and can capture general lin- guistic patterns. Three different PLMs, namely BERT (bert-base-uncased) (Devlin et al., 2018). RoBERTa (roberta-base) (Liu et al., 2019) and Clin- icalBERT (Huang et al., 2019) are used in this work. These models have been widely used for clinical text processing and achieved good performances (Vakili et al., 2022; Alsentzer et al., 2019).
4
Results and Discussion
4.1 Results
Table 2 and Table 3 show the performance of the system on the test set of the gold data, using differ- ent combinations of data for fine-tuning PLMs.
The results demonstrate that the system bene- fits from the silver and bronze data and most in- creases are significant. For binary classification, the highest performance is obtained by fine-tuning the system on both the bronze+silver data (over- all accuracy=0.94, 1.11% t), followed by adding
We fine-tune the PL.M models on different com- binations of the gold, silver, and bronze datasets, as described below. We use cross- cross-entropy loss, Adam
ASec Appendix for negative data generation.

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|ttrrt of text
"Sone personal history: 3 months ago received a call from my parents' neighbor that ny dad was wandering around their property at night. They said he seemed confused and disoriented. inmediately
and took to the ER After series tests, including MRI and neuropsychological evaluation, he was diagnosed with Alzheiner's disease. He was prescribed Aricept and Namenda,, and the doctor recommended that he see an occupational therapist for cognitive coping strategies, such using written reninders, keeping
stimulation. also suggested daily planner, and doing memory exercises." End of text]
Start of annotation]
("sentence": "3 nonths ago I received a call from my parents' neighbor that my dad was wandering around their property at night", "class": "Notice/concern others"),
{"sentence": "They said he seemed confused and disoriented.", "class": "Cagnitive inpairment ("sentence": "After a series of tests, including an MRI and neuropsychological evaluation, he was diagnosed with Alzheimer's disease.", "class": "Cognitive assessment",.
("sentence"; "He prescribed Aricept and Namenda, and the doctor recommended that he see occupational therapist for cognitive stimulation.", "class": "Cognitive intervention/therapy ("sentence": also suggested some coping strategies,
using written reminders, keeping daily planner, and doing memory exercises.", "class": "Coping strategy")
annotation]
Figure 1: Illustration of the text and annotation generated by the GPT-4 based on the annotation guideline. (Class
names are shown here better readability.)
Category
Gold
Silver
Bronze
Cognitive impairment
6240
1378
2704
Notice/concern by others
785
366
1710
Require assistance
1864
614
1205
Physiological changes
1340
5718
1769
Cognitive assessments
2099
327
2168
Cognitive intervention/therapy
1097
3508
2104
Diagnostic tests
1084
2933
2021
Coping strategy
343
893
1058
Neuropsychiatric symptoms
1342
318
1308
Total
16194
16069
16047
Avg length +/- SD (tokens)
17.60 +/- 12.69
19.60+/-15.44
16.58+/-4.69
Table Category Distribution of the Gold/Silver/Bronze Datasets
bronze data only (overall accuracy=0.93 3.33% T). The silver data, though derived from MIMIC-III, the real world EHR data, only improves slightly (0.91, 1.11% t).
better understand the results, conducted a series of analysis.
As Table 1 shows, the gold data has an imbal- anced distribution of categories. This poses chal- lenge for classification tasks. The bronze+silver data, with more balanced categorical distribu- tion, helps to mitigate this problem. We notice an increase in the performance for Coping strategy (3 (31.88 1288%)) using bronze+silver data. Performance gains are also observed for other minority classes including NPS, Requires assistance, Cognitive sessment, etc., by adding more training examples.
For multi-class classification, the bronze data alone provides the biggest overall performance improvement (7.35%1). On the contrary the sil- ver data does not improve much (1.47%t) and even reduces the performance when combined with the bronze data (5.88%1<<)) 7.35%1). For sub- categories, the performance gain is large in minor- ity classes including coping strategy (31.82%) by adding bronze+silver) and notice/concern by others (21.05% t by adding bronze+silver).
The amount of data is the only factor that influences the performance. For the physiological changes category, adding silver data times the size of the gold data makes no difference, while adding a smaller amount of bronze data results
4.2 Analysis
The performances of machine learning models are largely decided by data quantity and quality. To

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Gold
Only
+ Bronze
+ Silver
+ Bronze Silver
Precision (Positive)
0.73
0.88 (20.55%1)
0.73 (0%)
0.86 (17.81%1)
Recall (Positive)
0.75
0.7 (6.67%.)
0.77 (2.67%1)
0.74 (1.33%))
F-1(Positive)
0.74
0.78 (5.41%))
0.75 (1.35%))
0.8 (8.11 %1)
Overall Accuracy
0.9
0.93 (3.33%))
0.91 (1.11%1)
0.94 (4.44%1)
Table 2: Performance (P/R/F-1/Acuraee (change compared to gold only)) of the ensemble system on the gold test set using different data combinations for training (Binary Classification).
Gold Only
+ Bronze
+ Silver
+ Bronze Silver
Cognitive impairment
0.72
0.73 (1.4%))
0.72 (0%)
0.74 (2.78%1)
Notice/concern by others
0.38
0.4 (5.26%))
0.41 (7.89%1)
0.46 (21.05%))
Requires assistance
0.64
0.64 (0%)
0.63 (1.56%..
0.68 (6.25 f)
Physiological changes
0.64
0.78 (21.88 % 1)
0.64 (0%)
0.76 (18.75%1)
Cognitive assessment
0.69
0.75 (8. (8%70%))
0.7 (1.45%1)
0.77 (11.5%%1)
Cognitive interventioon/herapyy
0.71
0.74(4.23%11)
0.72 (1.41%1)
0.76 (7.04%1)
Diagnostic tests
0.84
0.83 (1.19%])
0.87 (3.57%11
0.82 (2.38%..)
Coping strategy
0.44
0.42 (4.55%))
0.47 (6.82%1)
0.58(3182%%
NPS
0.67
0.71 (5.97%))
0.68 (1.49%1)
0.69 (2.99% f)
Overall Accuracy
0.68
0.73 17
0.69 (1.47%1)
0.72 (5.88%1)
Table 3: Performance (F- (F-1/Accuracy (change compared to gold only)) of the ensemble system on the gold test set using different data combinations for training (Multi-clan Classification).
a significant improvement of 21.88% in F-scores. This suggests that the bronze data has a higher quality than the silver data for some categories.
get labelled as Concerns by others. The LLM in- fers that specific medical knowledge of children or spouse/children being present at hospital may indicate concern, but this could be wrong.
We randomly selected 100 samples from both the silver data and the bronze data and asked our human experts to check the quality the anno- tation. The annotation accuracy on bronze data is around 85%, and annotation accuracy on silver data is around 55% indicating the complexity and challenge of real world data. Some examples of LLM's labeling errors shown in Table 4. Experts identify at least two types of labelling errors by the LLM in the silver data:
The mis-classified data impacts the two tasks dif- ferently. For binary classification, the system only needs to distinguish sentences with AD-related signs and symptoms from other texts, so the mis- classification is less critical. However, for multi- class classification task, the system needs to cor- rectly assign the categories of the AD-related signs and symptoms, which be confused by the LLMs outputs. This partially offsets the advantage of increasing the amount of data, especially when using the silver dataset, which has a much lower accuracy than the bronze dataset. We observe that the silver dataset even harms the performance on "Requires Assistance" in multi-class classification task.
1. Over-inference (example 1&2), the LLM tends to make inference based on the informa- tion that is presented, and goes beyond what is supported by the evidence reasoning.
2. The LLM couldn't handle negation properly (example 3).
On the other hand, when using the bronze dataset, which has relatively higher quality, we see overall performance improvements for both binary and multi-class classification tasks. We no- ticed that in the multi-class classification task, the bronze data causes performance degradation on
In example 1, the LLM infers that the patient is weak so assistance must be required. Similar to example 2, we found that there are some sen- tences that mentioned son/daughter as nurses also

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some categories. The bronze data differs from the gold or silver data, which are real patient notes. This may cause distribution mismatch with the test dataset and lower performance for some categories. Table shows the bronze data has less variation in lengths (4.69 vs 12.69,15.44), This suggests that we need to steer LLMs to produce data that matches the data encountered in practice.
using prompts, feedback, or adversarial learning. And we would also tackle the ethical and practical issues of using LLM for medical data annotation, by adhering to the best practices and guidelines for responsible and trustworthy AI. We also intend to apply our approach to other clinical text processing tasks, such as relation extraction, entity linking, and clinical note generation.
To sum up, different data combinations affect the results (Table 2&3) by varying the training data in amount, quality and distribution. However, the performance generally improves with the addition of the bronze and/or silver data, though further analysis is needed for each category.
Limitations
Despite the promising results, our approach has sev- eral limitations that need to be acknowledged and addressed in future work. First, our experiments are based on the experimented LLMs and a sin- gle clinical task (AD-related signs and symptoms detection). It is unclear how well our approach can generalize to other LLMs, and other clinical tasks. Different LLMs may have different hallu- cination patterns and biases, and different clinical tasks may have different annotation criteria and challenges. Therefore, more comprehensive and systematic evaluations are needed to validate the robustness and applicability of our approach.
5 Conclusion and Future Work
In this paper, we examined the possibility of using LLMs for medical data generation, and assessed the effect LLMs' outputs on clinical text mining. We developed three datasets: a gold dataset annotated by human experts from a medical dataset, which is the most widely used method for clinical data gen- eration, silver dataset annotated by the LLM from MIMIC (data-to-label) and bronze dataset gen- erated by the LLM from its hallucinations (label- to-data). We conducted experiments train classi- fiers to detect and categorize Alzheimer's disease (AD)-related symptoms from medical records. We discovered that using a combination of gold data plus bronze and/or silver achieved better perfor- mances than using gold data only, especially for minority categories, and that the LLM annotations and hallucinations were helpful for augmenting the training data, despite some noise and errors.
Second, our approach relies on the quality and quantity of the LLMs annotations and hallucina- tions, which are not guaranteed to be consistent or accurate. The LLMs produces irrelevant, incorrect, or incomplete annotations or hallucinations, which will introduce noise or confusion to the classifier. Moreover, the LLMs may not cover the full spec- trum of the AD-related signs and symptoms, may generate some rare or novel symptoms that are not in the gold dataset. Therefore, the LLMs' annotations and hallucinations may not fully reflect the true distribution and diversity of the clinical data. To mitigate these issues, we suggest using some quality control mechanisms, such as filtering, sampling. or post-editing, to improve the LLMs' outputs. Fine tuning on high quality gold data can partially address these problems. We also suggest using some data augmentation techniques, such paraphrasing, synonym substitution, or adversarial perturbation, to enhance the LLMs' outputs.
Our findings suggest that LLM can be a valuable tool for medical data annotation when used care- fully, especially when the data is scarce, sensitive, or costly to obtain and annotate. By using LLM hallucinations, we can create synthetic data that does not contain real patient information, and that capture some aspects of the clinical language and domain knowledge. However, our approach also has some ethical and practical challenges, such as ensuring the quality, diversity, validity, and reli- ability of the L.LM annotations and hallucinations, protecting the privacy and security of the data and the model, and avoiding the potential harms and biases of the LLM outputs.
Third, our approach may raise some ethical and practical concerns regarding the use of LLMs for medical data annotation, especially its hallucina- tions. Although not observed in this work, there is still slight possibility that the LLMs may pro- duce some sensitive or personal information that may breach the privacy or consent of the patients or the clinicians. The LLMs may also generate
For future work, we will investigate other meth- ods and techniques for enhancing and regulating the LLM annotations and hallucinations, such as

---

No.
Sentence
LLM annotation
Human comments
[Pt] was profoundly weak, but was no longer tachycardic and had normal blood pressure. Her husband is a pediatric neurologist at [Hospital].
Requires assistance
Over-inference
Notice/concern by others
Over-inference
3
Neck is supple without lymphadenopathy.
Physiological changes
Miss negation
Table 4: Examples of the LLM's incorrect annotations from the silver data
some misleading or harmful information that may affect the diagnosis or treatment of the patients or the decision making of the clinicians. Therefore, the LLM outputs should be used with caution and responsibility, and should be verified and validated by human experts before being used for any clinical purposes. We also suggest using some anonymiza- tion encryption techniques to protect the confi- dentiality and security of the LLM outputs.
McDermott, 2019, Publicly available clinical bert
embeddings. arXiv preprint urXiv:1904.0332
Ali Amin-Nejad, Julia Ive, and Sumithra Velupillai. 2020. Exploring transformer text generation for med- ical dataset augmentation. In Proceedings of the Twelfth Language Resources and Evaluation Confer- ence, pages 4699-4708.
Razvan Azamfirei, Sapna R Kudchadkar, and James Fackler. 2023. Large language models and the perils of their hallucinations. Critical Care, 27(1):
7 Acknowledgement
Fidelia Bature, Barbara-ann Guinn, Dong Pang. and Yannis Pappas. 2017. Signs and symptoms preceding the diagnosis of alzheimer's discase: a systematic scoping review of literature from 1937 to 2016. BMJ open, 7(8)=e015746
This study was supported by the National Insti- tute on Aging of the National Institutes of Health (NIH) under award number RO1AG000670. The authors are solely responsible for the content and do not represent the official views of the NIH. We are grateful to Dan Berlowitz, MD from Univer- sity of Massachusetts Lowell, Brian Silver, MD and Alok Kapoor, MD, from UMass Chan Medical School for their clinical expertise in developing our annotation guidelines of Alzheimer's Disease. We also appreciate the work of our annotators Rae- lene Goodwin, BS and Heather Keating, PhD and Wen Hu, MS from the Center for Healthcare Or- ganization & Implementation Research, Veterans Affairs Bedford Healthcare System, Bedford, Mas- sachusetts, for developing annotation guidelines and annotating electronic health record notes that were essential for training/evaluating our natural language processing system and assessing the qual- ity of the automatically generated data by LLMs. Finally, we thank our anonymous reviewers and chairs for their constructive comments and feed- back that helped us improve our paper.
Jules Berman. 2002. Confidentialityy issues for medical data miners. Artificial intelligence in medicine, 26( 1- 2):25-36.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam. Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 1877-1901..
Meng Cao, Yue Dong, and Jackie Chi Kit Cheung. 2021. Inspecting the factuality of hallucinated entities in abstractive summarization. CoRR, abs/2109.08 09784..
Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, and Yan Liu. 2017. Boosting deep learning risk prediction with generative adversarial networks for electronic health records. In 2017 IEEE International Conference Data Mining (ICDM). pages 787-792. IEEE.
Bharath Chintagunta, Namit Katariya, Xavier Amatri- ain, and Anitha Kannan, 2021. Medically aware gpt-3 as a data generator for medical dialogue sum- marization. In Machine Learning for Healthcare Conference, pages 354-372. PMLR
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---

A Annotation Guideline
relevant this category.
STM: short memory [Definition endl
The anmotation guideline comprises the main part of the prompts used in this work. It is created by experts and revised based on LLM outputs. The version used in this work is follows:
[Class end]
[Class begin|
Class 2:
ITitle begin| Notice/concern by others |Title end|
[Start of annotation schema]
[Definition begin|
These classes as follows:
These concerns are about cognition, mood or
daily activities, not from nurses or doctors or medical care providers, but from friends or family or neighbors.
[Class begin|
Class
[Title begin| Cognitive impairment |ittle end| [Definition begin|
family complains of something (may be
related to any class including physiology)
(collect broadly, will not specific AD), Cognitive impairment is when person has trouble comembering, learning,
noticed changes in ability, speed concern expressed by family/friends complaints of pt. easily angered some examples:
concentrating, or making decisions that affect their everyday life.
Currently captured by patients subjective
Daughter reports that she repeatedly asks
statements as well as Dr. statements as follows:
the same question. had difficulties using her smartphone.
Forgetting appointments and dates.
Daughter reports that she has issues with decrease in personal hygiene, forgets to take meds, forgets where food is in the house, etc.
Forgetting recent conversations and events.
banking...
Having a hard time understanding directions or instructions
Losing your sense of direction. Losing the ability to organize tasks. Becoming more impulsive. Memory loss.
Pt. has gone out at 1:30
without telling anyone; they concerned, but always has response.
She (daughter) tells that her has repeatedly changed the medications the pill boxes that she has arranged for her.
Frequently asking the same question or repeating the same story over and over (perseveration)
Not recognizing familiar people and places Having trouble exercising judgment, such as
Definition end]
[Class end!
knowing what to do in emergency Difficulty planning and carrying out tasks, such as following recipe or keeping track of monthly bills
[Class begin|
Class 3:
meaningless repetition of words, lack of restraint, wandering and getting lost lose your train of thought the thread of conversations
[Title begin| Requires assistance [Title end] [Definition begin| defined as Requires assistance from person needs help with or loss ability with
trouble finding your way around familiar environments
ADLs/iADLs, difficulty with self-care, trouble managing belongings ADLs: dressing, eating, toileting, bathing. grooming, mobility
problems with speech language feel increasingly overwhelmed by making
decisions, planning steps to accomplish task understanding instructions mental decline, difficulty thinking and evening understanding, confusion
iADLs: housekeepingrelater activities (cleaning, cooking, and laundry) and complex activities (telephone use, cadication intake, use of transpor peaaiio//diiing budget/Finance management, and shopping) some examples:
hours, delusion, disorientation, lack orientation, forgetfulness, making things up, mental confusion, difficulty concentrating, inability create new nemories, inability to simple math, inability to recognize common things, poor judgment, impaired communication, poor concentration, difficulty remembering recent conversations, names or events forget things more often, forget important events such as appointments or social engagements, issues with recall, changes in abstract reasoning ability attention, cognition, speech, orientation, judgment
The patient will continue to require assistance with all complex medical, legal and financial decision making. She will need 24-hour supervision for her safety.
Direct supervision is required for medications using pillbox. Best not to have him stove. If left alone for period of time, will need guardian alert consider camera surveillance.
He is able to make meal, dress himself, to bathe, shave, but continues need help with finances.
AD, dementia, MCI: capture diagnoses

---

Wife has remind him about appointments, particular.
VS: Computerized Neurocognitive Assessment Software Vital Signs
Dr riiinn should not be permitted, and will
COWAT: Controlled Oral Association Test
need assistance with IADLs and decision making.
CVLT: California Verbal Learning Test
DRS:
Dementia Rating Scale, Mattis Dementia
Veteran does need assistance with all IADLS and most ADLs.
Rating Scale
D-KEFS: Delis-Kaplan Executive Function System
Traveling out of neighborhood, dri driving, arranging to take buses-limited night driving now
FAS: a test measuring phonemic word fluency (using words starting with letters F,
Resides in assisted living facility or nursing home
A, S)
HVLT-R: Hopkins Verbal Learning Test-Revised
Writing checks, paying bills, balancing
HVOT: Hooper Visual Organization Test
mheckbook-miniime (automatic payment)
Mini Mental State Exam (MMSE; also known as
N/A
Folstein Test): >=24 and <28 out of 30 (maybe MCI) no CPT code II VAMC MMSE Guidelines: 25-30 normal, 21-24 mild dementia, 13-20 moderate dementia, 0-12 severe dementia 1| Dr. Peter Morin's scoring: 30 normal, 28-29 MCI, 22-27 mild dementia, 14-21 moderate dementia, 8-13 severe dementia
Playing game of skill-no hobbies N/A Definition endl
[Class end]
[Class begin|
Class 4:
[Title begin| Physiological changes ITitle end] [Definition begin|
Montreal Cognitive Assessment (MoCA): >=17 and <25 out of 30 (MCI) free, there is also a Blind MoCA with total score of 21, not 38. VAMC MoCA Scoring: 26-30 normal, 20-25 suggestive of mild impairment, 15-19 suggestive of moderate impairment, 10-14 suggestive of significant impairment, 8-9 suggestive of Peter Morin's scoring: normal, 23-26 MCI, 18-22 mild dementia, 10-17
senses: vision, hearing, smell loss, SNHL: sensorineural hearing loss, HoH
sleep: Excessive daytime sleepiness, changes in sleep patterns
speech/swallovie (speech difficulties also
"Cognitive Impairment" class) t/aitenn/gait/balaneee
inability to combine muscle movements:
impairment Dr.
jumbled speech, difficulty speaking, aphasia, dysphasia, difficulty swallowing, dysphagia, di difficulty walking, mobility, problems with gait and balance, gait slowing
severe dementia NBSE: Neurabehavioral assessment of thinking, reasoning and judgment, e.g., acquired knowledge, attention, language, memory, planning problem solving, and visual spatial abilities)
moderate dementia,
NAB: Neuropsychological Assessment Battery (clinical
Brain (and blood vessel -asociaas))))))ee) abnormalities
stroke, ischemia, blood vessel ecclusiio/steensiss infarct, encephalomalacia, small vessel changes, vascular/mmicrovaccul. changes (in
NCSE (Cognistat): Neurobehavioral Cognitive Status Exam
brain), carotid artery
NPT/Neuropsych test/neuropsych inventory Paced Auditory Serial Addition Test Proverb interpretation (test of abstract reasoning; part of MMSE)
loss of appetite, loneliness, general discontent, TBI, skull fracture
PASAT:
[Definition tion end] [Class end]
RBANS: Repeatable Battery for Assessment of
Neuropsychological Status
RCFT: Rey Complex Figure Test (sometimes ROCFT)
[Class begin|
Class 5:
RFFT: Ruff Figural Fluency Test
[Title begin| Cognitive assessment [ittle end] [Definition begin|
RMT: (Warrington) Recognition Memory Test Saint Louis University Mental Status
memory tests, scores irrelevant; mark all
Examination (SLUMS): 21-26 out of 30
present
(MCI) free I| VAMC SLUMS Scoring: high school education 27-30 normal, 21-26
Blessed Orientation Memory and Concentration (BOMC) test: 0-10 out of 28 is normal to minimally impaired; 11-19 is mild to moderate impairment || VAMC BOMC Scoring: score >10 is consistent with the presence of dementia, score 7 are considered normal for the elderly BNT: Boston Naming Test
mild neurocognitive disorder, 1-20 dementia; less than high school education 25-30 normal, 20-24 mild
neurocognitive disorder, 1-19 dementia
SDMT Symbol Digit Modalities Test measure of processing speed, concept formation Serial sevens (part of MMSE)
BVMT-R: Brief Visuospatial Memory Test CERAD-NAB: Consortium Establish Registry for Alzheimer's Diseas-Neuropptthoooiical Assessment Battery
SILS: Shipley Institute of Living Scale Spelling word forward and backward (part of MMSE)
TOMM: Test of Memory Malingering
Trail Making Test
Clock Box
UFOV: Useful Field of View test

---

VF: Verbal Fluency (test) WAIS: Wechsler Adult Intelligence Scale
not considered.
IDefinition endl
WCST: Wisconsin Card sor tint Test
[Class endl
WTAR: Wechsler Test Adult Reading
[Class begin|
[Dfinitiio end] [Class end|
Class 8:
[Title begin| Coping strategy [Title end]
[Definition begin|
[Class begin|
repetition and written reminders may be
Class 6:
useful tool in therapy has been encouraged to keep mentally active to slow the rate of cognitive decline requires shopping list when going for groceries otherwise she will forget items uses a planner for appointments
[Title begin| Cognitive intervention/thenn therrppyy
|Title endl
[Definition begin|
This includes mentions of drugs, doesn't
require pt to actually start drug or adhere to taking drug
Aricept being taken
reliant on GPS for driving memory exercise
occupational therapy, cognitive linguistic
therapy, cognitive behavioral therapy memory group therapy
keep mentally active
uses medication organizer Definition endl CCass end]
informed pt. of memory group and she had possible interest in this
SmartThink: (regional VA offering) large group available to any Veteran who would like to improve memory, attention, other cognitive function. Dementia-related medications, any interventions initiated by provider e.g., medications, therapies. relevant meds: cholinesterase inhibi tors (general term), Aducannmab/Aduuhlle /aaamm/nnn//aamenn///aaaiiiccc Razadyne (galantamine), Exelon (rivastigmine), Aricept (donepezil)
[Class begin| Class 9:
[ittle begin| Neuropsychiatric symptoms [Title
endl
[Definition begin|
mood changes: depression, irritability, aggression, anxiety, apathy,
personality changes, behavioral changes, agitation Feeling increasingly overwhelmed by making
Pimavanserin (for htwp://rr/raiaaatn///////ee/eeee
decisions and plans.
paranoia, delusions, hallucinations
experimental)
[Definition endl [Class end!
flickering light therapy
vitamin aan/coaanooblllmin
vitamin B1/thiaming
End annotation schemal
vitamin D/cholecalciferol (in context of memory issues only)
[Definition end]
B Prompts
[Class end]
We used 3 prompts in this work.
[Class begin| Class
1. Prompt is to ask LLM to annotate provided text following the above guidelines.
[Title begin| Diagnostic tests of the head or brain that are related to neurocognitive symptoms. Tittle end]
Task: Annotate the text based the provided annotation guideline.
[Definition begin|
including CT, EEG, EMG, FDG-PET, MRI, PET,
PET-CT, MRA, CSF
|5tart of text
MRA=Magnetic resonance angiography radiology study (context: header neuroimaging) imaging (referring to MRI PET imaging) NOT capturing diagnostic test results in separate sentences from the test name NOT capturing imaging header if specific info (MRI) follows
[text here]
[nd of text
|Start of annotation guideline] [annotation guideline here] [En of annotation guideline! Format output as a valid json with the following structure:
Include distant MRI (e.g., from childhood); concussion/head trauma be relevant CTE
[
[
"sentence":str \\ The sentence that is annotated.
genetic testing: APCE4 for sporadic AD, mutations in APP, PSEN1 (PS1 protein), PSEN2 linked to early onset AD
"class":int V\ The class that the sentence belongs to.
Note MRI context of spine joints EMG in context of carpal tunnel syndrome should
}

---

2. Prompt is ask LLM to check the annota- tion results and explain the reasons for making judgements.
C Negative Data Generation
The negative data is sampled from the notes anno- tated by experts. It consists of data that are not an- notated as having any AD-related symptoms. Also sentences that are too short (<5 tokens after re- moving punctuation and stop words) are removed. Tabbe/ffoms//uesiiomaanss are excluded. The tio of negative:positive data is decided based on statisties from VHA data.
Task:
Check the annotations of the text based on the provided annotation guideline are correct or not and explain why.
|Start of text|
[text here]
|End of text|
IStart of annotation guidelinel [annotation guideline here] [End of annotation guideline]
Model Training
The system contains 3 base models. All models PLMs that are fine tuned on the training data. The models implemented using Transformers' ? The training parameters are:
|Start of annotation| [annotation here] [End of annotation]
Format output valid json with the following structure:
epoch=10.
optimizer=Adan.
1r=1e-3.
beats=(0.9, 0.999).
"sentence":strr AV The sentence that is annotated
eps=1e-6.
warmup_steps=z00
"class" innt The class that the sentence belongs "decision": boool, V\ Whether the annotation is correct or not. "reason":str Explain why.
weight_decay=0.0o
3. Prompt 3 is to ask LLM to generate a note and conduct annotations based on the provided guideline.
Task: Generate a clinical note and annotate the text based the provided annotation guideline.
|Start text| [text here] [End text|
|Start annotation guideline|
[annotation guideline here] [End of annotation guideline]
Format annotation output as valid json with the following structure:
"sentence" sttr, The sentence that is annotated.
"class"
innt int V\ The class that the sentence belongs to.
These prompts are for reference and are slightly modified to adapt to each LLM for format control
in practice.


Reviews:
[{"review_id": "Q8rdCSkVye", "reviewer": "Reviewer_5zTH", "summary": "", "questions": "Can you more thoroughly compare to previous work and explain any methodological novelty of this approach over the previous work (for example for the papers suggested in the missing references section)?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the valuable comments. We have addressed the comments as follows:\n\nWe appreciate the relevant references suggested by the reviewer; We will update our related work section to include a broader review of related literature.\n\nRegarding the novelty, existing work on data augmentation like the ones you suggested mostly leverage LLMs for tasks like refining and expanding question-answer pairs, to enhance domain-specific QA datasets, (Guo, Zhen, et al), dialogue summarizer datasets (Chintagunta, B et al), NER task (Li, Jianfu, et al.) etc. We presented a novel task, which was to identify nine categories of Alzheimer’s Disease signs and symptoms from unstructured clinical notes. Our work of augmenting training data using synthetic data is one of the steps of creating a high-performance model for identifying the severity of Alzheimer’s disease. In the revision, we will detail the novelty of our task. \n\nThe novelty of this work lies in several aspects:\n\n1.\tWe proposed a novel pragmatic taxonomy for AD sign and symptom progression. These categories indicate the stages and severity of AD. Capturing them in unstructured EHRs can help with early diagnosis and intervention, appropriate care and support, disease monitoring and treatment evaluation, and quality of life improvement for people with AD and their caregivers. However, existing schema for AD severity identification is challenging in practice, since signs and symptoms are subjective, and in many cases, not present in EHR notes. So we have created a novel taxonomy based on exhaustive literature review and neurologists' professional input. And it has shown to be reliably annotated using information described in EHR notes. \n\n2.\tThis task is more challenging than existing LLM tasks in the clinical domains, as it requires classifying data into multiple categories based on complex annotation guidelines (see appendix A). This task demands high levels of in-domain and contextual knowledge. For instance, slow speech problems are usually indicative of cognitive impairment, while other speech problems such as aphasia are related to physiological changes. These distinctions pose a significant challenge to LLMs and machine learning classifiers. We evaluated LLMs on a complex task that needs domain knowledge and language understanding. This work shows how combing expert knowledge (the annotation schema) and LLMs can help with complex tasks.\n\n3.\tThis work introduces a novel “label to data” generation scheme, which contrasts with the conventional “data to label” scheme that has been widely used in previous work. We find that this scheme works better under some scenarios, especially in clinical domains where data privacy is highly valued. This suggests a new direction of applying LLMs to clinical tasks. Our proposed approach in creating the silver and bronze data greatly reduces human efforts for annotation. \n\n4.\tThe human analysis reveals the problems with LLMs: failure to handle negation and over-inference. These problems, especially the over-inference issue, have not been explicitly pointed out or well addressed in previous work. This work, with the selected examples, demonstrates the limitations of existing LLMs and suggests a possible future research direction.\n\nWe agree with the reviewer and it would be our future work to evaluate the augmentation by synthetic data framework to other tasks for generalizability. \n\nIn terms of the evaluation, in our initial submission we evaluated the quality and characteristics of the bronze and silver data using automatic and human analysis. \n\nWe reported in the manuscript that\n\n1.\tThe bronze data has less diversity as evidenced by the std of length but has a higher accuracy per human evaluation and also more balanced categories. We believe lacking of diversity is detrimental but the higher accuracy and more balanced category distribution are beneficial.\n\n2.\tThe sliver data has more diversity (larger std of sentence length) but has a relatively lower accuracy which can be detrimental.\n\n3.\thuman analysis showed typical errors made by LLMs in generating data include: failure to handle negation and over-inference.\n\n4.\tWe adopted a chain of thought checking for the silver data to increase its annotation quality. As we stated in Section 4.2: \"The outputs were further checked by the LLM by asking for a reason to explain why the sentence belongs to the assigned category. In this step, the inputs to the LLM were the guidelines and the annotated sentences and the outputs were Boolean values and explanations. This chain-of-thoughts style checking has been proved to improve LLM performance.\"\n\nIn the revision, we will explain our efforts in improving the diversity and quality of the bronze data.\n\n1.\tWe also adopted chain of thought checking for bronze data but did not observe any performance improvements. We suspect it is due to the fact that bronze data are all generated by LLMs and a chain of thought check cannot capture the errors. We will elaborate this in the revised version.\n\n2.\tWe have tested different strategies to increase the diversity of bronze data by adding instructions like ”create data of different styles and lengths”. However, as the prompts are very long, LLMs are not sensitive to the diversity/length constraints instructions. This will be our research topic in the future. \n\nWe are conducting more thorough human evaluations on the generated data, but it is challenging due to the specialized domain knowledge required.\n\nWe will explain these efforts and results in more detail in the revised version. We will also provide more details on how we addressed the LLM errors. We hope that these revisions will address the reviewers' concerns and improve our paper.\n\nWe are grateful for your professional suggestions on how to improve our presentation in terms of clarity and aesthetics. We will include them in the final version. We believe these modifications will polish the paper and make it more professional, readable and well-looking.\n\nFor simplicity, we only reported the significance tests for the overall accuracies in this table. We will include the significance test results for all metrics and clarify the different tests that we used for different metrics. For precision and recall, we used matched-pair t-tests (sign or Wilcoxon tests are also plausible) that do not require independence assumptions. For F-score, we used a randomization test that is more computationally intensive (ref: More accurate tests for the statistical significance of result differences, Alexander Yeh). And the majority of them were significant. We apologize for not explaining this clearly in the manuscript.", "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": "1. The authors perform an interesting comparison of two different types of data augmentation, i.e. either generating the input using the class or generating the class using the input.\n2. The results make a compelling case for their method and the combination of different data generation techniques, and the method can help with data imbalance problems, which is especially useful in the clinical domain.\n3. The writing is clear and makes a valid argument for the approach.\n4. They validate their approach with human evaluations and enumerate two different types of errors.", "reasons_to_reject": "1. Limited novelty and missing cites: Given the large amount of work on data augmentation using large language models, even in the medical domain (see missing cites), a more thorough related works section is also necessary. Most of the novelty lies mainly in just the application of data generation to this task and the evaluation, which is limited.\n2. Limited scope and results: the authors only test this on one dataset and two (related) tasks (binary and multi-class classification). It would be interesting to see if the same procedures (i.e. producing silver labels from inputs and bronze inputs from labels), would induce similar results on a wider variety of tasks. Also, because of the limits in novelty of the approach itself, it might be useful to do a more thorough analysis of the types of errors talked about in section 4.2, maybe through a more thorough manual evaluation, perhaps including chain of thought. Or potentially it might be useful to attempt to remedy some of these mistakes or, for instance the lack of diversity in length of the bronze data. There is also no analysis of the types of errors that occur on the bronze dataset creation, including what the model hallucinations are “beneficial” or potentially “detrimental” to training.\n3. Presentation is incomplete and creates confusion: Adding a concept figure seems necessary. Figures and tables could use some major work aesthetically (e.g. less lines in the tables, Figure 1 could be clearer, maybe color code the labels). Additionally, error bars would be welcome in the table, and it is confusing that * represents statistical significance but it looks like this was only calculated for accuracy, so everything else looks like it’s statistically insignificant even though that’s probably not true.", "questions_to_authors": "Can you more thoroughly compare to previous work and explain any methodological novelty of this approach over the previous work (for example for the papers suggested in the missing references section)?", "paper_topic_and_main_contributions": "The paper uses GPT-4 to augment an existing gold dataset with two additional datasets, silver and bronze. Silver is formed through using GPT-4 to label sentences sampled from MIMIC-III, and Bronze is formed through using GPT-4 to create examples that correspond to a specific label. They show that using both silver and bronze datasets help with performance on their classification task in both binary and multi-class classification settings.\n\nInteresting to note: In their setting, they find that the bronze dataset is actually more accurate as judged by humans than the silver dataset, which is surprising and cool.", "excitement": 2, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "See point 3 in reasons to reject."}, "extra_text": {"Missing_References": "Medically aware GPT-3: https://arxiv.org/pdf/2110.07356.pdf\n\nDr. Llama: https://arxiv.org/pdf/2305.07804v4.pdf (though in fairness, this might be considered concurrent work)\n\nExploring Transformer Text Generation for Medical Dataset Augmentation: https://aclanthology.org/2020.lrec-1.578/"}}, {"review_id": "07w9PSpIw3", "reviewer": "Reviewer_Qqnk", "summary": "", "questions": "A few specific questions:\n1. Line 300. What is the number of sentences that contain positive mentions of signs or symptoms? Is it an imbalanced dataset?\n2. Line 331. A period is missing after \"16.58\".\n3. Line 374. Why only BERT models are considered? How about LLMs, such as GPT-4 or LLaMA?\n", "limitations": "", "rating": 3, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the valuable comments. We have addressed the comments as follows:\n\nWe are grateful for the relevant references suggested by the reviewer; we will update our related work section to include a broader review of related literature. We also appreciate the careful checking and pointing out of typos by the reviewer, which helps us improve our paper. \n\nRegarding the novelty, existing work on data augmentation like the ones you suggested mostly leverage LLMs for tasks like refining and expanding question-answer pairs, to enhance domain-specific QA datasets, (Guo, Zhen, et al), dialogue summarizer datasets (Chintagunta, B et al), NER task (Li, Jianfu, et al.) etc. We presented a novel task, which was to identify nine categories of Alzheimer’s Disease signs and symptoms from unstructured clinical notes. Our work of augmenting training data using synthetic data is one of the steps of creating a high-performance model for identifying the severity of Alzheimer’s disease. In the revision, we will detail the novelty of our task. \n\nThe novelty of this work lies in several aspects:\n\n1.\tWe proposed a novel pragmatic taxonomy for AD sign and symptom progression. These categories indicate the stages and severity of AD. Capturing them in unstructured EHRs can help with early diagnosis and intervention, appropriate care and support, disease monitoring and treatment evaluation, and quality of life improvement for people with AD and their caregivers. However, existing schema for AD severity identification is challenging in practice, since signs and symptoms are subjective, and in many cases, not present in EHR notes. So we have created a novel taxonomy based on exhaustive literature review and neurologists' professional input. And it has shown to be reliably annotated using information described in EHR notes. \n\n2.\tThis task is more challenging than existing LLM tasks in the clinical domains, as it requires classifying data into multiple categories based on complex annotation guidelines (see appendix A). This task demands high levels of in-domain and contextual knowledge. For instance, slow speech problems are usually indicative of cognitive impairment, while other speech problems such as aphasia are related to physiological changes. These distinctions pose a significant challenge to LLMs and machine learning classifiers. We evaluated LLMs on a complex task that needs domain knowledge and language understanding. This work shows how combing expert knowledge (the annotation schema) and LLMs can help with complex tasks.\n\n3.\tThis work introduces a novel “label to data” generation scheme, which contrasts with the conventional “data to label” scheme that has been widely used in previous work. We find that this scheme works better under some scenarios, especially in clinical domains where data privacy is highly valued. This suggests a new direction of applying LLMs to clinical tasks. Our proposed approach in creating the silver and bronze data greatly reduces human efforts for annotation. \n\n4.\tThe human analysis reveals the problems with LLMs: failure to handle negation and over-inference. These problems, especially the over-inference issue, have not been explicitly pointed out or well addressed in previous work. This work, with the selected examples, demonstrates the limitations of existing LLMs and suggests a possible future research direction.\n\n\nClinical NLP applications have been an important research topic in the ACL community. The ClinicalNLP workshop and CLPsych workshop have attracted huge interests from the NLP research community. Every year, many influential clinical NLP papers are published in EMNLP and ACL Anthology. A search of \"clinical\" in ACL Anthology gives about 12,400 results, and \"Alzheimer's\" returns 955 results. With the applications of NLP techniques in clinical domains, more and more works appear in NLP conferences and we believe this work will add to the attraction of EMNLP and make it a spotlight as AI for healthcare and AI for human well-being.\n \nHere are a few examples of impactful works that appear in ACL Anthology: \n\n\"Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models\" NAACL 2018\n \"Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling\" NAACL 2019\n \"A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer’s Type\" ACL 2020\n\"Large language models are few-shot clinical information extractors\"  EMNLP2022\n \"ClinicalT5: A Generative Language Model for Clinical Text\" ACL2022\n \"MedicalSum: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations\"  EMNLP 2022\n\nAbout Line 300, We used the MIMIC-III dataset, which contains around 60,000 discharge summary notes, as the source of our silver data. We did not review all of the notes, but randomly sampled some of them until we obtained the desired number of positive instances for our silver dataset. The notes fed to the LLM were highly imbalanced, with sentences containing positive signs and symptoms being relatively rare.\n\nWe chose BERT models as our base models for verifying the effectiveness of the generated data, because of the following reasons: \n\n1) BERT models have been widely adopted in clinical tasks and their effectiveness has been proved in previous works. In contrast, T5 and LLaMA have not yet been widely adopted in the clinical domain. \n\n2) BERT models have low resource requirements and enable local computation. The use of BERT models makes it possible for on-edge computation, which can facilitate daily personalized health management.  \n\n3) Our gold data is from VHA, which does not allow sending data to any external APIs like GPT-4 for security reasons.\n\nWe would further explore leveraging GPT-4 following the privacy guidelines for clinical NLP and meanwhile, we will adopt llama and T5 as our future work.", "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": "This is a good use case in clinical NLP and empirical evaluation of a hypothesis whether combining gold standard dataset with generated text improve the overall information extraction performance. ", "reasons_to_reject": "The idea of combining synthetic text to enhance NLP performance is not new. Having said that, the application in Alzheimer's disease is new. That being said, this study may be a better fit to informatics community rather than ACL community.", "questions_to_authors": "A few specific questions:\n1. Line 300. What is the number of sentences that contain positive mentions of signs or symptoms? Is it an imbalanced dataset?\n2. Line 331. A period is missing after \"16.58\".\n3. Line 374. Why only BERT models are considered? How about LLMs, such as GPT-4 or LLaMA?\n", "paper_topic_and_main_contributions": "This paper examined whether combining gold standard dataset with text generated from large language models will improve the performance of information extraction. The authors tested the approach on a unique dataset from VA health and focused on a specific task for Alzheimer's disease signs and symposiums extraction. Their results showed that combining the generated text will improve the performance.", "excitement": 3, "reproducibility": 1, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "This relevant work is missing: Li, J., Zhou, Y., Jiang, X., Natarajan, K., Pakhomov, S.V., Liu, H. and Xu, H., 2021. Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition. Journal of the American Medical Informatics Association, 28(10), pp.2193-2201."}}, {"review_id": "LNDLDnOzgD", "reviewer": "Reviewer_AkGZ", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 2, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the valuable comments. We have addressed the comments as follows:\n\nAs for assessment of data quality, for our gold data quality, we reported the inter-annotator agreement, which is k=0.868 by Kappa score, indicating a high level of reliability. \n\nThe performance boost obtained on the gold test set proved the effectiveness of the generated silver/ bronze dataset. We have conducted human evaluation on a small subset and provided analysis. Due to the high cost, we evaluated another 100 from both silver/bronze datasets and our conclusions still hold. We will include this in the revised version.\n\nFor ethical concerns, as stated in the manuscript, this approach aims to address the privacy and security issues in clinical NLP. The MIMIC-III dataset is de-identified and widely used in clinical NLP. The bronze data is purely generated by the model, and can help protect privacy. Moreover, the models we trained in this work are Bert-based, which are small and require less computation resources. They can be easily deployed to assist with clinical AI for wellness and fairness.\n\nAs for comparison with existing method, this study presents a novel application, which is a computational approach to detect the early signs of AD. It can be considered as a text classification application. We adopted Bert-based models, which are widely used as the state-of-the-art in clinical text classification tasks [refs: What Clued the AI Doctor In? On the Influence of Data Source and Quality for Transformer-Based Medical Self-Disclosure Detection in EACL2023 ,Bio+Clinical BERT, BERT Base, and CNN Performance Comparison for Predicting Drug-Review Satisfaction in KDD2023 workshop etc.]. We plan to extend our methods to other standardized tasks and available data in the future.\n\nRegarding the novelty, existing work on data augmentation mostly leverage LLMs for tasks like refining and expanding question-answer pairs, to enhance domain-specific QA datasets, (Guo, Zhen, et al), dialogue summarizer datasets (Chintagunta, B et al), NER task (Li, Jianfu, et al.) etc. We presented a novel task, which was to identify nine categories of Alzheimer’s Disease signs and symptoms from unstructured clinical notes. Our work of augmenting training data using synthetic data is one of the steps of creating a high-performance model for identifying the severity of Alzheimer’s disease. In the revision, we will detail the novelty of our task. \n\nThe novelty of this work lies in several aspects:\n\n1.\tWe proposed a novel pragmatic taxonomy for AD sign and symptom progression. These categories indicate the stages and severity of AD. Capturing them in unstructured EHRs can help with early diagnosis and intervention, appropriate care and support, disease monitoring and treatment evaluation, and quality of life improvement for people with AD and their caregivers. However, existing schema for AD severity identification is challenging in practice, since signs and symptoms are subjective, and in many cases, not present in EHR notes. So we have created a novel taxonomy based on exhaustive literature review and neurologists' professional input. And it has shown to be reliably annotated using information described in EHR notes. \n\n2.\tThis task is more challenging than existing LLM tasks in the clinical domains, as it requires classifying data into multiple categories based on complex annotation guidelines (see appendix A). This task demands high levels of in-domain and contextual knowledge. For instance, slow speech problems are usually indicative of cognitive impairment, while other speech problems such as aphasia are related to physiological changes. These distinctions pose a significant challenge to LLMs and machine learning classifiers. We evaluated LLMs on a complex task that needs domain knowledge and language understanding. This work shows how combing expert knowledge (the annotation schema) and LLMs can help with complex tasks.\n\n3.\tThis work introduces a novel “label to data” generation scheme, which contrasts with the conventional “data to label” scheme that has been widely used in previous work. We find that this scheme works better under some scenarios, especially in clinical domains where data privacy is highly valued. This suggests a new direction of applying LLMs to clinical tasks. Our proposed approach in creating the silver and bronze data greatly reduces human efforts for annotation. \n\n4.\tThe human analysis reveals the problems with LLMs: failure to handle negation and over-inference. These problems, especially the over-inference issue, have not been explicitly pointed out or well addressed in previous work. This work, with the selected examples, demonstrates the limitations of existing LLMs and suggests a possible future research direction.", "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": "The paper discusses the implications of utilizing LLM-generated data, even with hallucinations, for creating synthetic datasets free of sensitive information. ", "reasons_to_reject": "1. This paper focuses on a specific clinical domain for generating dataset. Yes, we know LLMs can generate data and build a dataset.  It might be novel in clinical scenario, but not novel more broadly. \n2. 100 samples for human evaluation is small. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper explores the potential use of LLM-generated data to supplement the scarcity, sensitivity, and imbalance issues commonly encountered in medical datasets. However, the paper should provide more rigorous assessment of data quality, address ethical considerations, and include comparisons with existing methods for a well-rounded contribution.", "excitement": 2, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The authors compare two types of data augmentation for the task of detecting Alzheimer's signs and symptoms from clinical notes. They showed that adding in the two augmented datasets (silver and gold) led to an improvement in performance. During the discussion, the authors asserted that the novelty of their work lies mainly in the application area and in the label-to-data direction of generation.

There are comments from numerous reviewers that point to a lack of excitement due to the paper's novelty primarily lying in the application of LLMs to a specific task. While this may get more interest in a medical informatics community (as some reviewers state), it largely does not effect the soundness of the work, and the work seems reasonably connected to NLP. The re-framing that the authors are working on, as well as further discussion of how their findings may apply to other tasks, could make this work more interesting to the NLP community.
Decision: Accept-Findings