Adaptive Test-Time Training for Predicting Need for Invasive Mechanical Ventilation in Multi-Center Cohorts
Authors: Xiaolei Lu
Shamim Nemati
Venue: ICLR 2026 Poster (2026)
Keywords: Test-Time Training
Domain Adaptation
Electronic Health Records
Invasive Mechanical Ventilation Prediction
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Accurate prediction of the need for invasive mechanical ventilation (IMV) in intensive care units (ICUs) patients is crucial for timely interventions and resource allocation. However, variability in patient populations, clinical practices, and electronic health record (EHR) systems across institutions introduces domain shifts that degrade the generalization performance of predictive models during deployment. Test-Time Training (TTT) has emerged as a promising approach to mitigate such shifts by adapting models dynamically during inference without requiring labeled target-domain data. In this work, we introduce Adaptive Test-Time Training (AdaTTT), an enhanced TTT framework tailored for EHR-based IMV prediction in ICU settings. We begin by deriving information-theoretic bounds on the test-time prediction error and demonstrate that it is constrained by the uncertainty between the main and auxiliary tasks. To enhance their alignment, we introduce a self-supervised learning framework with pretext tasks: reconstruction and masked feature modeling optimized through a dynamic masking strategy that emphasizes features critical to the main task. Additionally, to improve robustness against domain shifts, we incorporate prototype learning and employ Partial Optimal Transport (POT) for flexible, partial feature alignment while maintaining clinically meaningful patient representations. Experiments across multi-center ICU cohorts demonstrate competitive classification performance on different test-time adaptation benchmarks.

ADAPTIVE TEST-TIME TRAINING FOR PREDICTING NEED FOR INVASIVE MECHANICAL VENTILATION IN
MULTI-CENTER COHORTS
Xiaolei Lu
Shamim Nemati
Divisions of Biomedical Informatics University of California San Diego La Jolla, CA 92122, USA xi x11270@ucsd.ed edu
Divisions of Biomedical Informatics University of California San Diego
La Jolla, CA 92122, USA
snemat @ucsd.
ABSTRACT
Accurate prediction of the need for invasive mechanical ventilation (IMV) in in- tensive care units (ICUs) patients is crucial for timely interventions and resource allocation. However, variability in patient populations, clinical practices, and elec- tronic health record (EHR) systems across institutions introduces domain shifts that degrade the generalization performance predictive models during deploy- ment. Test-Time Training (TTT) has emerged promising approach mitigate such shifts by adapting models dynamically during inference without requiring labeled target-domain data. In this work, we introduce Adaptive Test-Time Train- ing (AdaTTT), an enhanced TTT framework tailored for EHR-based IMV pre- diction in ICU settings. We begin by deriving information-theortiicc bounds on the test-time prediction error and demonstrate that it is constrained by the un- certainty between the main and auxiliary tasks. To enhance their alignment, we introduce self-supervised learning framework with pretext tasks: reconstruction and masked feature modeling optimized through a dynamic masking strategy that emphasizes features critical to the main task. Additionally, to improve robustness against domain shifts, we incorporate prototype learning and employ Partial Opti- mal Transport (POT) for flexible, partial feature alignment while maintaining clin- ically meaningful patient representations. Experiments across multi-center ICU cohorts demonstrate competitive classification performance on different test-time adaptation benchmarks.
INTRODUCTION
Invasive mechanical ventilation (IMV) is a critical intervention utilized intensive care units (ICUs) for patients with severe respiratory failure and acute respiratory distress syndrome (ARDS) (Grot- berg et al.) 2023). However, its use is complicated by the risk of ventilator-induced lung injury and complications resulting from prolonged IMV. Timely and accurate identification patients high risk for mechanical ventilation is crucial for optimizing clinical decision-making. Early recognition of these patients enables proactive medical interventions and facilitates efficient resource allocation within hospital system (Fan et al., 2018).
In recent years, the development of machine learning (ML) models has shown great promise in pre- dicting the need for IMV, which leverages electronic health record (EHR) data to identify complex patterns that human clinicians might overlook (Shashikumar al., 2021b). These models can incor- porate diverse features, including vital signs and laboratory results to enhance prediction accuracy and provide critical decision support in ICU settings, However, the effective deployment of such models in real-world clinical settings remains a challenge. A key issue is the variability in data distributions across hospitals due to differences in patient populations, clinical practices, and EHR systems. These shifts, often referred to as domain shifts, can substantially degrade the performance of predictive models that were trained on data from a single or limited number of sources. For in- stance, respiratory failure prediction model (Lam al. 2024) trained on ICU cohort from UC San

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Diego Health showed an approximately 12% drop in the area under the curve (AUC) when evaluated on an external ICU cohort.
Addressing this challenge requires adaptive methodologies that can account for site-specific hetero- geneity. Existing approaches include pre-training models on large multi-center datasets (Shashiku- mar al. 202 and transfer learning to fine-tune models on site-specific data (Lam et al.) 2024) to align feature representations across domains. While these methods have shown promise, many require access labeled data from the target domain during training involve computationally ex- pensive retraining processes, which are not always feasible in real-time clinical settings. Test-time training (TTT) offers a novel and efficient solution this problem by enabling models to adapt dynamically at the time of prediction, without requiring pre-access to target domain labels or costly re-training. TTT leverages an auxiliary task, trained alongside the main task, to update the model's parameters or representations using the test input itself.
Predictive systems based on EHR data, such as Composer (Shashikumar et al. 202 have shown significant real-world impact to improve clinical outcomes through real-time decision sup- port (Boussina et al. 2024). In a before-and-after quasi-experimental design study at two emergency departments (EDs), the Composer model for sepsis prediction significantly increased bundle com- pliance and reduced in-hospital mortality. However, limited prior work has explored TTT in the context of EHR data, particularly in real-world clinical scenarios. By enabling dynamic adaptation at prediction time, TTT addresses variability across institutions and patient populations, ensuring robust performance in critical tasks, such predicting IMV need in multi-center cohorts. Unlike tretraining/offlinng alignment approaches (e.g., MaskTab (Chen et al.). PhyMask (Kara et al. 20224) and SPOT (Gurumoorthy et al., 202II), which require source data and extended training, our setting is source-free and demands on-the-fly adaptation at deployment.
In this study, we introduce Adaptive Test-Time Training (AdaTTT) for predicting IMV need 24 hours in advance in ICU patients across multi-cented cohorts. AdaTTT is designed to address domain shifts in EHR data through adaptive self-supervised learning and robust feature alignment. Our key contributions are as follows:
We derive information- -theoreeic bounds the test-time prediction error to show that the error is constrained by the uncertainty between the main and auxiliary tasks, which guide the design of auxiliary tasks for better adaptation.
We introduce two SSL tasks: Reconstruction and Masked Feature Modeling along with a dynamic masking strategy that prioritizes the most informative features during test-time training. The masking probabilities adapt based on feature relevance to the primary task, ensuring that the SSL task remains aligned with the IMV prediction objective.
To prevent overfitting to individual test samples, we integrate prototype learning with Par- tial Optimal Transport (POT) to allow partial matching between source domain features and test-time distributions, which promotes robust generalization while avoiding excessive adaptation to test-domain noise.
We conduct extensive experiments multi-site ICU cohorts, where our method achieves competitive classification performance across various test-time adaptation benchmarks.
RELATED WORK
2.1 PREDICTIVE MODELS FOR INVASIVE MECHANICAL VENTILATION
Early IMV-risk tools such as ROX and regression scores are interpretable but struggle with nonlinear, time-varying physiology (Roca et al. 2019). Leveraging scale data, VentNet predicts IMV 24h ahead with a feedforward model (Shashikumar et al. 202 encoder-decoder designs like DBNet integrate structured signals and demographics (Zhang et al.
2021); and multimodal hybrids that fuse
CXR with EHR further boost discrimination (Tandon al. 2023). However, cross-site performance often degrades due to population, workflow, and EHR heterogeneity; recovery via target-domaa fine-tuning is common but label-intensive and operationally impractical for continuous deployment.
2

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2.2 TEST-TIME ADAPTATION
TTA adapts models on unlabeled test inputs without revisiting source data (Liagg et al., 2024)
Batch normaeizatoonBNN-eentiii methods include prediction-time BN statistics updates (Nado et al,,
2020) and TENT's entropy minimization for BN parameters (Wang et al. (2200). while fource-free
SHOT freezes the classifier and adapts the encoder with pscudo-labels (Liang et al., 2020). Test- time training (TTT) attaches auxiliary SSL branches for online encoder updates (Sun et al. 2020 extensions like TTT+ (contrastive) and ClusT3 (clustering) improve alignment but may inherit instability assume domain consistency (Liu et al. 2021 Hakim et al. 2023).
Three relevant directions are: (i) T3A, an optimization free method forming class prototypes from streaming test data reweight logits that is efficient but classifier-level only, assuming stable class structure (Iwasawa & Matsuo/ 2021); (ii) SAR, which filters unreliable samples and ap- plies sharpness-aware entropy minimization for stable BN updates that is effective with small batches yet BN-dependent (Niu et al. 2023); ; (iii) CoTTA, maintaining a moving teacher with augmentation- and weight-averaged pscudo-labels plus periodic restoration, is useful for long hori- zons but byperparameter-sensiiinn with potential eтror accumulation (Wang et al. 2022).
In EHR-driven IMV prediction, these approaches face practical challenges: tiny per-encounter batches undermine BN estimates; pseudo-labeling struggles with class imbalance and temporal non- stationarity; clustering assumptions break under irregular sampling and missingness; classifier-only adaptation cannot address representation shift, making direct application from vision to ICU EHRs difficult.
3 METHODOLOGY
3.1 PRELIMINARY: TEST-TIME TRAINING
Let X' denote input instance from the covariate space, ym Vm denote the corresponding label for the main task (e.g., classification), and y/s E V, denote the auxiliary label derived for a self-supervised task. The training is represented as [ 1- At test time, both covariate distribution p(X') and label distribution p(Y)) p(Y.) may change, which leads to domain shifts.
Test-Time Training (Sun et 2020) addresses these shifts by leveraging the same SSL task during both the training and testing phases to align features between the training domain and individual test instances. The framework consists of a shared feature encoder fe (=:0,,,, a primary classification head (- 02) and an SSL head h=(:; (-)...
During training, TTT jointly optimizes both the main classification loss Cmain and the auxiliary SSL loss Lus as
02, 02,02,05 arg min Eta Emmin me 0z) Lus(x;;; 0., 0.).
At inference time, rather than relying static model parameters, TTT dynamically adapts the en- coder for each test instance by optimizing the SSL objective with
0x(x')
arg min Caa 0e). 0.
The adapted encoder is then used to obtain the final prediction with
(3)
3.2 THEORETICAL INSIGHTS
Prior work (Liu et al. 2021) derives accuracy bounds under assumptions of distributional alignment and task consistency. We provide independent perspective based information theory that ex- amines how the auxiliary task informs the main task through shared representations. Let and Z' represent the feature representations from the feature encoder for the training and test domains,
3

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respectively, We define Tm is the main task classifier predicting Ym, and n. is the SSL classifier pre- dicting Y, The probability Pm (Z') = Ynm) quantifies the likelihood that the main task classifier Tm correctly predicts the main task label Y at test time.
In test-time training, we assume the Markov chain Y" + -' + Y holds, which captures the dependency structure where the auxiliary task labels Y influence the main task labels Y, only through the shared representation Z'. Under this assumption, we establish the relationship between the mutual information of the auxiliary and main tasks (please refer Appendix A.1 for derivation details).
In test-time training, where only the shared representation layers are updated using Y the following inequality holds:
(4) Building on this, we derive information-theorrticc bounds on the main task prediction error with binary case in the ideal scenario (please refer Appendix A.2 for derivation details and multi-class case). Let 77²2) = P(Y 1 Z' = 2 > 0.5 as positive, the minimum classification error p(e) Iz min
i 2 [Y Y,,).
ddpp)))((z) and (a)(n) =1-- log 1) (1 (1-1) log(1 17), the
prediction error is bounded by
HH YD) p(e) & H(" (Y))
(5)
The lower and upper bounds on the prediction error of the main task highlights the relationship between the main task and the SSL task in performance after adaptation. The upper bound shows that error is limited by the conditional uncertainty H (Yy YJ) while the lower bound demon- strates that lower H(Y, Y?) improves worst-case guarantees. Additionally, under domain shift
w(ys) - ) (Y,, (Y)) = E.. P(y)) H.), Y = Va). Theorem shows
overfitting to test-domain auxiliary task distribution P(Y") can lead to overweighting regions with high uncertainty H(", Y.). Enforcing P(Y)) P(Y.) without accounting for test-specific shifts can further harm model generalization. These findings emphasize the necessity of designing framework that ensures strong alignment between the main and auxiliary tasks while remaining robust to domain shifts for effective test-time adaptation.
3.3 ADAPTIVE TEST-TIME TRAINING
The effectiveness of test-time training depends SSL alignment with the main task and handling distribution shifts. To address this, we propose Adaptive Test- Time Training (AdaTTT) to enhance TTT with dynamic self-supervised learning and prototype-guided adaptation, which aims to improve generalization under clinical domain shifts in EHR data.
3.3.1 DYNAMIC SELF-SUPERVISED LEARNING
Fixed SSL transformations (e.g., random feature masking) may introduce spurious patterns unre- lated to the main task. In EHR data, some features are more predictive, and treating all equally reduces adaptation effectiveness. To mitigate this, we introduce two pretext tasks: Reconstruction and Masked Feature Modeling along with a dynamic feature masking strategy that prioritizes infor- mative features.
SSL Loss. Given input vector x and the the corrupted input x, the reconstruction loss and masked feature modeling loss are defined as
Crecon
Σ (.) (1,-22)² 2,)²
where is the total number of features, and ê, represents the reconstructed value of feature Iy predicted by the model.
Lonfn
Σ (x) - 02)²
M
JEM
where M is the set of indices of masked features (my = 1), and M denotes the number of masked features.
4

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The overall supe-sspe learning loss combines the reconstruction loss and the masked feature modeling loss with
Arecon
Lrecon
Lmfm:
(8)
Dynamic Feature Masking, Instead of fixed random masking, we introduce an adaptive masking strategy that assigns higher masking probabilities more informative features. Given an input vector the corrupted input x is generated as follows:
f = my f(x,)) j) + (1 - m2) x/
where m E [0, 1]d is the mask vector with elements m,. f(x,j) ~ P(x,) is a replacement for feature 2, where P(zy) is the empirical distribution of training or testing data.
Masking probabilities are dynamically updated based on global feature relevance scores derived from the main task:
(n)
1 This
(10)
dzy (m)
1 ming Is
Pm. max; 47- ming 1s
(11)
The masking phrases in the training section are described as follows: In the training phase, we employ a two-stage masking strategy. During the warmup phase (Epochs I-N), dynamic masking is applied using fixed prior mask probability to encourage broad feature exploration (the prior is derived from a pretrained respiratory failure prediction model). In the subsequent adaptive masking phase (Epochs N+1-End) feature relevance scores updated each epoch based on the model's main task predictions from the previous epoch. These relevance scores are then used to refine the masking probabilities enable the model to focus more informative features over time.
During test-time training, the model continues refining masking probabilities at each gradient step to ensure SSL tasks remain aligned with the primary task. Prioritizing the most informative fea- tures challenges the model to reconstruct or predict essential aspects of the data and then improve generalization under domain shifts.
3.3.2 PROTOTYPE-GUIIT ADAPTATION
Prototype-guide adaptation is particularly important in the context of EHR-based test-time train- ing. Instance-level SSL updates may overfit to noisy or highly site-specific measurements, especially when the target-domain distribution differs substantially from the source. To mitigate this, we in- troduce a set of learned prototypes that capture population-level structure from the source domain. stable anchors during adaptation. By leveraging Partial Optimal Transport (POT), the model can perform soft and partial alignment between test-time features and prototype distributions, enabling flexible yet robust adaptation under domain shift while avoiding the brittle- ness of full-transport matching.
These prototypes
Training Stage. We introduce prototype learning loss that encourages the shared layer features to align with their corresponding prototypes P. These prototypes are designed to effectively represent the distribution of the feature space 2 and ensure that the feature embeddings are compact and structured around these representative points.
The prototype learning loss defined as
Cproto Zg P) = 1| PA(=)) 2
(12)
where P = (PI,P2. P2..P p is the set of k prototypes. PA(a.) is the prototype assigned to the feature 2; based on the cluster assignment A(z;).
To prevent all features are assigned single prototype, regularization term added balance the cluster assignments:
The
1 -
Creg (P)
Σ
Tha Σ
I(A(zz) =
(13)
5

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where I(A(z)) j) is indicator function that equals 1 if 2 is assigned to prototype pj-
The final loss function incorporating prototypes is given as
C Ex (f. u.) + Cui(())) + Aprcto + Aree (P),
(14)
where Lmain is the respiratory failure prediction loss. Last is the self-supervised loss (see Section 1.).1), Leg is the regularization loss for balanced assignment. Aproto and Areg are hyperparameters controlling the importance of the prototype and regularization terms.
Test-Time Training Stage. Traditional Optimal Transport (OT) assumes a full alignment between source and target distributions, which may be too rigid in the presence of domain shifts. We refine the alignment between the training prototypes P and the test-time feature representations incorporating POT. Instead of constraining the transport plan to only partially align prototypes with the test instance (Chapel et al. 2020), we augment the set of z' by adding k - 1 perturbed duplicates transform the transport problem into standard optimal transport setting while enabling partial matches between z' and the prototypes P.
by
z' ='= ,
(15)
where each duplicate= z is sampled as
j,d 2/4 + +N(0. ()),
(16)
0 Ppjd pa)
(17)
where Pj.d is the value of the d-th dimension of the j-th prototype Pj, M4 is the mean of the d-th dimension across all prototypes.
The loss function employed during test-time training is defined as
Lisst List + Aot
Yho Cijs
(18)
where An is hyperparameter balancing the importance of the OT cost in the overall loss. Vij
defines the mass transported from z to the j-th prototype. Cij || -p,-pplll represents the squared Euclidean distance between z and the prototype P,.
EXPERIMENTS
4.1 EXPERIMENTAL SETTING
Datasets. We conduct retrospective study using de-identified EHR data of all adult patients (2 18 years) admitted to the ICU at Site A between January 1, 2016, and December 31, 2023. This dataset served as the development and validation cohort. To evaluate the mechanisms of test-time training, we utilize additional datasets, including ICU admissions at Site A between January 1, 2024, and June 30, 2024, Site В between January 2023, and August 31, 2024, well the publicly available MIMIC-IV dataset. Institutional Review Board approval was obtained for the use of these datasets. Appendix I provides full details on cohort selection and data processing. Our devel- opment cohort consists of 24,943 encounters, with 1,308 positive cases (IMV prevalence: 5.2%). The testing cohorts include Site (1,835 encounters, 104 positive cases, IMV prevalence: 5.7%) and Site B (2,564 encounters, 141 positive cases, IMV prevalence: 5.5%). The original MIMIC-IV dataset contains 35,534 encounters with an IMV prevalence of 15.4%. For computational efficiency, randomly downsampled MIMIC-IV to 2,069 encounters (244 positive cases with an IMV preva- lence 11.8%).
Implementation Details. Our network architecture follows Vent.io (Lam et al. 2024 (Ap- pendix B.2). We use Bayesian optimization for source-domain pretraining to tune general network hyperparameters (learning rate, weight regularization, number of hidden layers). The prototype set
For anonymization purposes, the of the healthcare institution has been replaced with Site x.

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Table 1: AUC (%) across testing sites (1 higher is better).
PrITT (Do))
D (wai
MaTTT (Owmn)
Datavel
TEST
Titer
NC-TFI
Site A
$4.01
SL1220022
82,020044
S1.0000
82.090011
#2602009
82.0020000
#2366200
82.0220.06
54.110000
84.4420110 8.002:10.0
Sice B
53.75
50.8110.04
83.102005
83,2020.10
88,00200:8
#2814005
60.5020.10
819920.12
81.020.10
63.9820000
83.8400112 04.10
MIMIC-IY
76.00:0.05
76.105008
18.72:0001
763312006
76.450007
76.21:0.08
744100.11
18.2720.06
70.3120.3
78.7000.05
77.1740.08
Table 2: Brier score across testing sites (4 lower is better).
WITTY (Oon)
Dpa (Oam)
AbOTTT (0am)
Datred
TIST
CITYA
TTT
Charf
NCTTI
0.002:0.0L
0,0030.0LL
0.09100022
30003004
0.0850003
0,0000000
00000001
0.080=00002
0.0010001
0,0110.122
0.0010.02
A0911008
0.0400022
0.0650000
0:0050:00
00013000
0.090:0.05
0.085 0,112::0.04 0.106:20044
MIMIC-IV 2.11
0.110=0.12
0.10000000
0.11
2.1142004
0.10029.04 0.111
0.1132004
0.112 0.110::0.05
size is h: = 4. For dynamic masking, we adopt two-phase schedule: a warm-up phase with fixed prior mask probability of 0.5, followed by an adaptive phase where masking probabilities are updated from feature relevance. During deployment, test-time training (TTT) performs five gradient steps per input (we refer to each step as an "iteration"), and we follow the standard reset protocol (Sun al. 2020) by restoring encoder weights to the pretrained state after each instance. For par- tial optimal transport, we use Sinkhorn with entropic regularization & = 0.1, maximum of 1000 Sinkhorn iterations, and mini-batch size equal to the prototype count (K k). All baselines are re-implemented with the same encoder, data pipeline, and hyperparameter search budget to ensure a fair comparison.
Evaluation. We follow the clinical labeling scheme (Lam et al. 2024 to capture the physiological states of respiratory failure, defining score 3 as positive and as control (Appendix B.I Encounters are categorized as True Positive, False Positive, True Negative, or False Negative based on predictions within the specified prediction window (criteria in Appendix [B.]. Performance is reported as AUC with mean + standard еттоr over 20 independent test-time training runs. In addition, following best practices for clinical prediction model assessment (Huang et al., 2020), we report Brier score for all models to evaluate calibration and clinical utility more comprehensively.
4.2 COMPARISON WITH BASELINES
Baselines. We consider a set of foundational and representative TTT and TTA methods and adapt each the EHR domain to ensure fair and meaningful comparison in our experimental evaluations
(Appendix B.3 provides full details of adapting baselines to EHR setting): TEST, TENT (Wang)
al. 2020). TTT (Sun et al. 2020), TTT++ (Liu et al., 2021). ClusT3 (Hakim al. 2023). NC-
TTT (Osowiechi et al. 2024). T3A (Iwasawa & Matsuo 2021 SAR (Niu et al. (223)) and COTTA
Wang et 2022). We also evaluate two ablated versions of our method: PriTTT, which removes
the adaptive distribution matching module and relies solely on updates to the mask probabilities for test-time training. DynTTT, which removes the dynamic masking module and focuses only on adaptive distribution matching.
Results Analysis. Tables[[AAG and 2 (Brier) report discrimination and calibration across three cohorts. AdaTTT obtains the top AUC on every site. Among non-TTT/TTA methods, SAR is closest on Site A (84.30=+0.04) and CoTTA is competitive on MIMIC-IV (7..60+0.005, yet both remain below AdaTTT. Regarding calibration, AdaTTT improves or matches calibration Site A/B (Brier 0.086/0. 0.5 vs. TEST 0.089/0.089) and achieves O. 10660.0004 on MIMIC-IV.
The behavior of the baselines is consistent with their assumptions and with the numbers in Ta- bles 2 TENT's entropy minimization encourages confidence that can be misplaced on out-of- distribution patients. Standard TTT and TTT+ attach fcature-agnostic SSL objectives and, in the latter, enforce relatively rigid source-target alignment; both are brittle when clinical feature im-
CClean version implementation http://githuhh.co//Slllllaaaaaa

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portance is highly unequal across variables and when hospital shifts complex. ClusT3's discrete codes and
assumption struggle with continuous physiology and site heterogene- ity; NC-TTT's contrastive likelihoods depend well-specified "noise," which is hard to define for heterogeneous EHR features. Classifier-only adjustment T3A helps when classes are tightly clus- tered (MIMIC-IV 76. 10++0.03) but cannot correct representation shift (Site A 82.50++0.04). SAR stabilizes BN-based updates and fares well on Site A (84.30=0.04) but remains sensitive to batch- statisties quality, yielding inconsistent improvements on Site B/MIMIC-IV.
We also examine how AdaTTT achieves its gains. Figure illus- trates the evolution of risk scores across time points for patient from Site A. Early predictions trend lower, but risk escalates approach- ing the intubation event (within 24 hours), demonstrating dynamic re- sponse to clinical deterioration. First, aligning the auxiliary SSL objective with the clinical endpoint is critical: with dynamic, feature-aware mask- ing, the SSL branch prioritizes clin- ically salient variables and down- weights weak signals. Figure 2 traces
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2.5
2.0
Iterations
Risk score evolution during test-time training for patient from Site A. Risk increases as intubation nears, which reflects model adaptation.
Figure 1:
feature importance from pretrained priors through early to late training epochs During warm-up, masking follows the priors; as training proceeds, the distribution stabilizes and aligns with learned relevance, indicating tighter coupling between SSL and IMV prediction than in feature-agnostic TTT/TTT++. Second, deployment the model refines importance per input: Figure 3 shows that some features remain stable while others (c.g.. respiratory rate) gain weight across iterations, sistent with model faithfulness and clinical plausibility. In parallel, prototype-guide transport flexibly matches test-time representations learned prototypes, limiting overfitting id- iosyncratic or noisy samples while preserving elinically meaningful structure (see Appendix C. for illustrative alignment). Ablations support this interpretation: PriTTT (feature-awarl masking only) and TT (distribution matching only) each improve over TTT/TTT++ but their combina- tion yields the most consistent AUC and Brier gains across sites.
partial optimal
ICULOS
SUN
HR
LD
Gender
scat
Resp de
prebo
anesthesia
on on_pain_mec
WBC
Iter
Iter
Figure 2: Feature importance evolution during training. The heatmap shows the changes in fea- ture importance in the initial epochs and final epochs.
An example of feature impor- tance evolution during test-time training. The heatmap shows the changes in feature impor- tance across different iterations.
Figure 3:
Computational Cost. Our proposed AdaTTT framework incorporates both feature importance up- dates and optimal transport computation during the test-time training phase. These additional op- erations inevitably increase the computational cost compared to standard TTT frameworks. We use the Sinkhorn algorithm (Sinkhorn) 1967) that leverages entropy regularization to ensure scalable computations. We evaluate the execution time of single gradient update during test-time training. The average execution time is 0.29s, and did not change much, remaining at 0.26s when increasing the prototype size from 4 to 16.
PICULOS ::: ICU length-of-stay to time Resp: respiratory rate; HR: heart rate; L.D: lymphocyte differential; BUN: blood urea nitrogen; OySat: oxygen saturation; WBC: white blood count.
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AdeITT
MITTT
72.00
P-TIT
19.00
64.5
70.00
8415
75.70
baratora
Iarataat
Faratora
(a) A.
(b) Site B.
(c) MIMIC-IV.
Figure 4: Evaluation of the number of gradient updates for test-time training different test cohorts.
05
hand
0.750
AUC hoad
8:0
0.725
71.5
0.733
01 12.5
0.555
36.0
6.5
250
500
150
1050
1250
1500
1750
1360
12.0
2000
69.5
752
3000 1252
1500 1750 2000 pelants
Number puteeT
(a) Site A.
(b) Site B.
(c) MIMIC-IV.
Figure 5: Cumulative AUC trend over an increasing number of patients.
4.3 SENSITIVITY ANALYSIS
In this section, examine the effect of the number of test-time training iterations and compare reset versus sequential update mechanisms. Additional ablations are provided in Appendix C.2 which investigate (1) the impact of prototype size, (2) the role of prototype learning, and (3) the effect of dynamic masking and the two SSL objectives.
Comparison of the number of iterations. training iterations on model performance across three different test cohorts. We evaluate the AUC scores the number of gradient updates increases from 5 iterations. Across all sites, AdaTTT exhibits stable and consistent improvement in AUC with more iterations. In contrast, PriTTT and DynTTT show less consistent trends, with fluctuations in performance, particularly in Site B. PriTTT updates feature importance dynamically without stable reference and is sensitive to imitial feature importance variations. Meanwhile, DynTTT lacks feature selection control, which can lead to suboptimal emphasis on features between the main and SSL tasks.
the impact of the number of test-time
Reset versus Sequential Update Mechanism. Compared with the reset strategy, the sequential up- date mechanism applies one gradient step at each new data point and retains the updated parameters across subsequent data points. Figure 5 presents the cumulative AUC trend across different sites under the sequential update mechanism. In Site B and MIMIC-IV, performance initially improves as the model adapts to recent distributional shifts (c.g.. achieving 80.27% AUC on MIMIC-IV), demonstrating the benefits of short-term adaptation. However, updates continue across larger patient population, the model's performance gradually deteriorates. This degradation likely due to accumulated adaptation noise, which shifts the model's focus away from its originally learned feature structure.
CONCLUSION
this study, we introduce AdaTTT, a lightweight and adaptive test-time training framework for pre- dicting the need for invasive mechanical ventilation (IMV) 24 hours in advance across multi-center ICU cohorts. AdaTTT integrates feature-aware self-supervision with partial based distribution alignment address domain shift that naturally arises in real-world EHR time-series data. Across all external cohorts, the method yields consistent improvements in discrimination and calibration over strong TTT baselines, demonstrating practical value for real-time clinical risk monitoring.

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To better contextualize these gains, have added (1) analysis of calibration and clinical rele- vance, and (2) additional experiments on secondary outcomes including acute kidney injury (AKI) and in-hospital mortality, showing similar trends.
We also explicitly acknowledge limitations: the method may be sensitive to extreme conditional shifts in p(Y X) (e.g., changes in clinical practice patterns), and prototype-basee adaptation adds moderate computational overhead at test time. Addressing these challenges is an important direction for future work.
ETHICS STATEMENT
Compliance and oversight. This study analyzes de-identified electronic health records (EHR) from multiple partner institutions (anonymized as Sites A/B) under appropriate institutional review and dave-govere oversight, with a waiver of consent where applicable due to de-identification and minimal risk. All activities complied with relevant privacy regulations (c.g., HIPAA) and lo- cal security policies. No attempt was made to re-identify individuals, and all reported results are aggregate.
Intended use and clinical safety. The model is research prototype for risk stratification and is not a stand-alone medical device. should only be used with clinician oversight. Any real-world deployment would require prospective evaluation, safety monitoring, and regulatory review. We report discrimination and calibration (AUC, Brier score) to support assessment of clinical utility and risk.
Fairness and shift. We evaluate across distinct clinical sites to assess robustness under distribution shift, and we report calibration as recommended for clinical prediction models. Despite these efforts, residual bias and inder-representation are possible; models trained in one setting may underperform elsewhere. We caution against out-of-scope use.
Source-free adaptation safeguards. Test-time adaptation updates only the encoder on a per- encounter basis and carryover. No patient- level exemplars or gradients are stored; adaptation logs contain no protected health information.
then resets weights before the next patient/time point, preventing poss--pateent
Transparency and confficts. We will release code and configuration sufficient for reproduction (subject to data-use constraints). Funding and potential conflicts will be disclosed in the camera- ready version. All authors adhere to the ICLR Code of Ethics.
REPRODUCIBILITY STATEMENT
We describe the cohort construction, preprocessing, and labeling schemne in Sec. B.I the architecture and training protocol in Sec. B.2 and Sec. 4.I] and sensitivity analyses/ablations in Sec. C.22 We report full metric definitions (AUC, Brier) and evaluation procedures. We will release anonymized code (data loaders, feature engineering, training/evaluation scripts, and plotting utilities) to allow end-to-end replication with public data, and provide configuration files to reproduce all tables/figures from logs.
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A THEORETIC ANALYSIS
A.1 PROOF OF I(Z': 2 I(Y::)))
Using the chain rule of mutual information, we can expand the mutual information between Z' and the joint variables (Y% m. Y?)= as
I(Z'; me = 12:; (y).
(19)
Using the chain rule of mutual information again, we have
( [[2; Y. YX) 1(Z'; Y. Y (I(ZZ);))) +/::))(((())))) (Z'', Yt) 1Z';Y, YM.).
(20)
In the test-time training framework, Z' is optimized to retain information from Y? that is predictive of Yz We model this by assuming
I(Z':Y' Y²) - I(Z'; Y 0.
Applying the Data Processing Inequality (DPI) to our Markov chain, we obtain
T(Y;; 7')
(21)
12

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From the mutual information decomposition derived earlier, know that (Z 2 ((Z'; 1(21; Y, Yg).
(22)
Since mutual information is always non-negative,
T(Z'; (g) 2 0.
(23)
Therefore,
T(Z 2 T(Z
(24)
Combining this with the DPI result
I(Y:: - I(Y;: 2)) I(Z';
obtain the final inequality as
I(Z'; Y 2 I(Y:: Y.))
(25)
A.2 PROOF OF PREDICTION ERROR BOUNDS OF THE MAIN TASK
A.2.1 BINARY CASE
For the two-class problem, the classifier predicts the input 2' with the posterior n1z') P(Y 1 Z' = >²} > 0.5 as positive, the minimum prediction error is
p(e) = /
(26)
Then Shannon entropy for binary random variable with the distribution (n. 1 n) is defined
(n) 17 log r (1 (1-11) 17) log(1 - -)), 7 E [o, 1].
(27)
The expectation of the above function with respect to 2' Z' is
H(Y z') = E2.w2r (H((22))) / H(n(z)))dp(z')
(28)
Based on Fano's inequality, have Hpr()( > H(Y; Z'). As p(e) S 0.5 and the function Hen(p(e) is monotonically increasing for S 1) 0.5, we have
p(c) H
H (H(, Z2)).
(29)
Given that (Y', Z') H(Y))) ((Z and H\ Y!) - in the
ideal test-time training under our Markov chain assumption Y! + Z' + Y Z' is as informative about Y% as Y is, we have
H(" (') = H(Y,) YY)
(30)
then the lower bound of p(e) is obtained
p(e) 2 H H (H(Y), Y.))-
(31)
Under Hellman's 's inequality (Hellman & Raviv 1970), we have
ple)= < 2 H(* Iz),
(32)
given ((Z'; 2 I(Y;; Y,), the upper bound of p(e) derived as
p(e) < 2
HYy" Y.).
(33)
13

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A.2.2 MULTI-CLASS CASE
In multi-class scenario, we assume & classes, denoted {I,..., k), and the main head classifier y maps the input z' E Z' to one of these classes. For 1j = [m..... mek. we have
h(n)
Σ m
Ty log M/y,,
(34)
H( Z²) E''nz'' [h((n(=))]]== S h(n(2'))ddo(')),
(35)
p(e) P(Y, pm) = 1-
Σ
/
1 (Y (Y%-8% y Maa (z')d((z') B (nax(n(=))))
2
Yi,
(36)
Based on the simplified Fano's inequality
(Thomas & Joy
2006 we have
p(e) 2 H(m ?")
log(
(37)
( m Z") 1
log(k)
Similar to the derivation in binary base, we can obtain
p(e) N HY Yi)
(38)
log(k)
and when A 2 4, the following always holds:
(Y% - S ple) s log (k:
1 H(Y, (Y))
(39)
B DATASET, MODEL AND BASELINES
B.I DATASET
Patient inclusion and exclusion criteria. Patients were included in the respiratory failure predic- tion analysis if they had an ICU stay of at least five hours, were not mechanically ventilated before ICU admission, and had documented vital signs and laboratory values prior to the prediction start time. Those with a Do Not Resuscitate (DNR) order were excluded, and data within 24 hours of surgery were omitted to avoid bias from surgery-related ventilation. Monitoring continued until me- chanical ventilation was initiated ICU discharge. To ensure sufficient data, predictions began four hours post-admission and were updated hourly using the latest clinical information.
Data abstraction and processing. We extracted EHR data encompassing 50 vital signs and labora- tory measurements, 6 demographic features, 12 Systemic Inflammatory Response Syndrome (SIRS) and Sequential Organ Failure Assessment (SOFA) criteria, 12 medication categories, and 62 comor- bidities. To handle varying sampling frequencies, vital signs and laboratory values were aggregated into hourly time-series bins, with multiple measurements per hour summarized using the median. Data updates occurred hourly, with missing values carried forward for up 24 hours if were available. Remaining missing values were imputed using the mean. Additionally, we derived 150 features from the 50 vital signs and laboratory measurements, including baseline values (mean over the previous 72 hours), local trends (change since the last measurement), and time since last measured (TSLM).
data
Clinical labeling scheme for the various physiological states of respiratory failure. Table 3 lists the labeling criteria used in (Lam al. 2024h.
Encounter-level evaluation. Table lists the details of evaluation metrics.
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Table 3: Criteria of clinical labeling scheme.
Condition
Criteria
Points
200 < PaO2 / I 300 mmHg
PaO2/FiO S 200 mmHg (severe hypoxemia)
PaOz/FiO; (not NaN)
IMV < 24 hours
PaO2/FiO2 S 200 mmHg and IMV < 24 hours
IMV > 24hours
141 < SpO,/FiOz S 221 mmHg
SpO,, /Fi02 141 mmHg (severe hypoxemia)
SpOy/FiO, (not NaN)
IMV < 24 hours
2p0,/FiO, S 141 mmHg and IMV 24 hours
IMV > hour
Table 4: Definitions of evaluation metrics based on predictions and labels.
Metric
Definition
True Positive (TP)
positive prediction (predictions(t) 2 threshold) where there is at least one positive label within the prediction window (up to 24 hours before T
False Positive (FP)
positive prediction (predictions[t) > threshold) where no positive labels exist within the prediction window (up to 24 hours before To).
False Negative (FN)
A negative prediction (predictions(t) threshold) where positive label exists within the prediction window (up to 24 hours before To).
True Negative (TN)
A negative prediction (predictions[1] < threshold) where no positive labels exist throughout the evaluated timestamps.
To is defined as the first timestamp where a patient is ventilated based on the simultaneous recording of PEEP and FiOz.
B.2 NETWORK ARCHITECTURE
Patient
Risk score
demagraphice
TSLMs TSLM
TT
f>A
layer
vitals/labs
comorbiditier
others
SSL output
medications
Ryg
Anema
Scendde
Figure 6: The developed network architecture. The encoder consists of a TSLM Layer followed by feedforward neural network. Both main head and SSL head are feedforward neural networks
Our model follows a Y-shaped design with a shared encoder and two task heads (Fig. At each hourly timestamp we assemble a structured input comprising static demographics, comorbidi- ties/medications, and time-varying vitals/labs augmented with their (TSLM). lightweight TSLM layer ingests each raw value Ij and its recency At,, applying learnalle decay/gating function to down-weight stale observations and inject recency embedding; the resulting features are concatenated with the static covariates and passed to a multilayer per- ceptron encoder f:; fe) to produce latent representation E Rdd. Two shallow feedforward heads operate on 2: a main classifier ,. (: 0,) outputs the probability of IMV within 24 h, and a self-superes self-supervised head n, (:)0)) supports reconstruction and masked-feature modeling driven by our
15

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dynamic, feature aware masking scheme. At deployment the classifier and SSL head frozen, and for each test example we adapt only the encoder for few gradient steps; the adapted encoder is then used to produce the final risk score, after which weights are reset before the next instance.
B.3 BASELINES
We compare against representative TTA/TT methods and adapt each fairly to the EHR setting: TEST. TENT (Wang et al.] 2020), TTT [Sun et al. 2020), TTT+ Liu et al. (2021). ClusT3 (Hakim al. 2023). NC-TTT (Osowiechi et al. 2024). T3A (Iwasawa & Matsuo 2021 SAR
Niu et al. 2023 and COTTA (Wang et al. 2022).
Fairness and EHR-specific protocol. All methods use the same data pipeline (hourly aggrega- tion, carry-forward <24h, mean imputation, and TSLM features), the same shared encoder as ours (Sec. [.2), and identical source-domain pretraining (optimizer, weight decay, early stopping). At deployment enforce the source-free constraint (no source samples/labels). For gradient-based methods we fix the same test-time budget: five update steps, identical step size schedule, and reset- to-pretrained after each instance; the classifier head is frozen unless the baseline explicitly modifies it. Methods that require batch statistics (e.g., BN-based TTA) use the same first-in-fist-our buffer of recent test samples to compute moments; the buffer size and confidence thresholds are tuned on the development validation split under the same hyperparameter budget (Bayesian optimization) for all methods. For approaches that rely data augmentation (e.g.. CoTTA), replace image transforms with tabular augmentation: feature masking.
Methods.
TEST: evaluate the pretrained model with no adaptation.
TENT (Wang al., 2020): minimize prediction entropy at time; we update only BN
affine parameters and running statistics.
TTT (unn et al. 2020): jointly train the network on the main task and the same auxiliary SSL objective ours (reconstruction masked-feature modeling) in the source domain; at test time, adapt the encoder only by minimizing this SSL loss. For faimesss TTT uses uniform, feature-agnostic masking (no dynamic masking), and does not use prototypes or optimal transport.
TTT+ (Liu et al. 202T): identical SSL setup TTT above and the same encoder-only test-time updates; additionally aligns first/second-order moments between source and tar- get. Because source data are unavailable at deployment, source moments are cached from the development split during training and used for alignment at test time.
ClusT3 (Hakim et al. 2023): add projector on top of the shared encoder and adapt by maximizing mutual information with discrete codes. We use MLP projector (tabular analogue of the original CNN projector) and the same codebook size across sites.
NC-TTT (Osowiechi al. 2024): optimize a noise-contrastive auxiliary likelihood test time. The noise distribution is factorized Gaussian with per-feature mean/variance estimated on the development split.
T3A (Iwasawa Matsuo, 202T): optimization-free classifier adjustment that builds class prototypes from confident test predictions and reweights logits. We compute prototypes from the FIFO buffer, with the confidence threshold tuned on the development split; no backprop encoder change.
SAR (Nuu et al.) 2023): sharpness-aware entropy minimization with unreliable-sample fil- tering for small/test-time batches. We follow the BN-only update rule as in TENT, add SAM-style perturbations to BN parameters, and use the same FIFO buffer; filter thresholds are validated once on the development split.
CoTTA (Wang et al., 2022): maintain an EMA teacher and perform augmentation//eeigtt averaged labudo-labeling with periodic weight restoration. Tabular augmentation is feature masking; teacher momentum and restoration period are tuned under the shared budget. Encoder is adapted.
16

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034
sia Protolyyee
Protebyp can
Protobype
Figure 7: An example of POT between prototypes P and z'.
C ADDITIONAL RESULTS AND FIGURES
C.I AN EXAMPLE OF PARTIAL OPTIMAL. TRANSPORT (POT)
To further illustrate how our method aligns test-time features with learned prototypes, we visualize an example of Partial Optimal Transport (POT) in Figure 7 The chord diagram shows the transport plan y between the test-time features z' (top half of the circle) and prototypes (bottom half). The width of each are reflects the transported mass "/j between feature 2/ and prototype Pj-
Unlike fixed alignment methods, our formulation allows flexible, soft alignment by augmenting the test-time features with perturbed copies to enable better adaptation to test-time distribution shifts, which prevents overfitting to noisy test samples while preserving meaningful prototype relation- ships.
C.2 ADDITIONAL SENSITIVITY ANALYSIS.
Comparison of the size of prototypes. Figure shows the AUC performance for Site Site B and MIMIC-IV across different prototype sizes. Site B and MIMIC-IV exhibit slight increase in performance as the prototype size increases, while Site A maintains relatively stable AUC values with minimal variation. Site B and MIMIC-IV have more diverse underlying data distribution and while Site may have more homogeneous patterns. Given that the complexity of external cohorts is unknown in advance, model calibration may be necessary to ensure optimal generalization.
Comparison of Prototype Learning. Our framework leverages prototypes to capture the training domain distribution and facilitate more effective alignment with test-time representations. To assess contribution of prototype learning, we conduct two ablation studies.
First, as reported in Table the manuscript, we evaluate the PriTTT baseline, which removes the adaptive distribution matching component, thereby isolating the effect of prototype-guidd align- ment. Second, we examine the impact of prototype learning by replacing end-to-end learned proto- types with post hoc cluster centroids. Specifically, we extract training representations after model training and apply k-means clustering. Each cluster is represented by the average embedding of its members, and the resulting lo centroids are used as fixed prototypes for distribution matching during test-time training.
17

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84
NUC
Site
MIMIC-IV
78
12
Size of prototypes
Figure 8: Effect of prototype size on AUC performance across different sites.
The results 5 that models using learned prototypes consistently outperform their post hoc counterparts across all datasets, which highlights the benefit of joint prototype learning and alignment in capturing richer, task-relevant training distributions.
Table 5: Performance Comparison with and without Prototype Learning.
Method
Site A
Site B
MIMIC
AdaTTT
85.02 + 0.05
84.10 + 0.05
77.17 + 0.08
DynTTT
84.54 + 0.10
83.84 + 0.12
76.79 + 0.05
AdaTTT w/o proto-learn
84.57 + 0.04
84.05 + 0.06
76.85 + 0.07
Dy w/o proto-learn
84.35 + 0.07
83.69 + 0.08
77.05 0.10
Table Ablation on masking strategy and SSL. objectives (AUC %, higher better).
Dataset
TEST
Random Masking
Reconstruction Only
Dynamic Mask Only
AdaTTT (Full)
Site
84.01
0.05
83.0
84.71
85.02 :
Site
83.75
8.060:10.06
8.047:10.05 75.00-+:.00
83.18110.04
84.10 0.05
MIMIC-IV
75.28
74.21 0.004
76.24-1:.02
77.17 0.08
Dynamic masking and SSL objectives.
We ablate the masking strategy and the auxiliary objectives. Replacing dynamic, task-aware masking with random masking degrades AUC by ~1.5-3.0 points across sites (e.g., Site A: 85.02+0.05 + 82.45 MIMIC-IV: 77.1740.08 + 74.211:0004. Using single objective alone (reconstruucton-onnyy or masked-featuur-onnt underperforms the full setup, indicating com- plementary roles: reconstruction regularizes representations, while masked feature modeling courages uncertainty-awar recovery of informative variables (Table 6
Unified hyperparameter sensitivity analysis.
As shown in Table varying Arccns Aprotos Areg. Aat and warm-up epochs produces only small fluctuations (< 0.1 AUC) across all cohorts, confirming that AdaTTT is stable these hyper- parameter choices.
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Table Unified hyperparameter sensitivity analysis. All hyperparameters produce small variations (0.1-0.3 AUC), confirming robustness of AdaTTT.
Hyperparameter Setting
Site A
Site B
MIMIC
Arcun 0.1
85.01 + 0.05
84.12 0.06
77.16 0.08
Arcmn = 0.5
85.03 + 0.05
84.11 0.05
77.18 + 0.08
Awcan 1.0
85.00 + 0.06
84.09 0.06
77.14 0.09
Aproto 0.1
85.00 +: 0.05
84.08 0.06
77.15 + 0.08
Aproio 0.5
85.02 = 0.05
84.10
77.17 0.08
Apcoto
84.98 0.06
84.07
77.12 0.08
Aug 0.1
85.01 = 0.05
84.11
77.16 0.08
Asg 0.5
85.00 0.05
77.15 0.08
Asg 1.0
84.97 = 0.06
+4.05+0.00
77.13 0.09
A 0.1
85.02 0.05
84.11
77.18 0.08
d 0.5
85.04 = 0.05
84.12
77.20 0.08
Aa 1.0
85.01 = 0.06
$4.09
77.16 = 0.09
Warm-up = 0 epochs
84.98 + 0.06
$4.07
77.14 0.09
Warm-up = 5 epochs
85.02 0.05
84.10
77.17 + 0.08
Warm-up 10 epochs
85.03 + 0.05
84.11
77.18 0.08
Warm-up up 20 epochs
85.00 +
84.09
77.15 0.08
19


Reviews:
[{"review_id": "AGJveV7nNM", "reviewer": "Reviewer_yhj2", "summary": "This paper introduces AdaTTT (Adaptive Test-Time Training), a framework for predicting the need for invasive mechanical ventilation (IMV)  across multiple ICU centers. The method addresses domain shift challenges in EHR data through two key innovations: (1) dynamic self-supervised learning with feature-aware masking that prioritizes clinically relevant features, and (2) prototype-guided adaptation using Partial Optimal Transport (POT) for flexible feature alignment. The authors provide information-theoretic bounds showing test-time prediction error is constrained by uncertainty between main and auxiliary tasks. Experiments across multi-center ICU cohorts (Sites A, B, and MIMIC-IV) demonstrate consistent improvements over existing test-time adaptation baselines.", "questions": "1. Does the framework also address conditional distribution shifts (i.e., shifts in ( P(Y|X) ))? Under what assumptions does the framework operate effectively, and in which scenarios might those assumptions break down?\n\n2. How strong is the assumption that auxiliary task labels $ Y_s' $ influence the main task labels $Y_m'$ only through the shared representation $Z'$? What are the implications if this assumption does not hold fully in practice?\n\n3. The framework introduces several hyperparameters — such as  $\\lambda_{\\text{recon}}$, $ \\lambda_{\\text{proto}} $, $\\lambda_{\\text{reg}} $, $ \\lambda_{\\text{ot}} $, masking warm-up epochs, and prototype size ( k ).  How should these hyperparameters be selected or tuned?\n\n4. Why were only five gradient steps used during test-time adaptation in the experiments? What is the rationale behind this choice, and what are the effects of using more than five steps?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Following are the strengths of the paper:\n\n1. The paper addresses an important problem—predicting IMV need across hospitals with different EHR systems, patient populations, and clinical practices.  \n\n2. The novel dynamic masking strategy that adapts based on feature importance is an important contribution. Also, POT-based alignment is more flexible than rigid full-transport approaches and better suited to partial distribution shifts.\n\n3. The information-theoretic bounds provide valuable insights into why auxiliary task alignment matters for test-time adaptation.", "weaknesses": "Following are the main weakenesses of the paper:\n\n1. Paper presentation and writing needs improvement. Specifically:\n   - The prototype-guided adaptation component (Section 3.3.2) is not well-motivated. The rationale behind its inclusion is unclear, and the implementation details are insufficiently explained.\n   - The methodology as a whole lacks clarity, largely due to the number of interconnected components. It would be helpful to include a comprehensive diagram or a formal algorithm to clearly illustrate the roles of each component and how they interact within the overall framework.\n\n2. Although the proposed approach consistently outperforms existing baselines, the improvements are relatively modest. For instance, Site A achieves an AUC of 85.02% compared to SAR's 84.30%, and Site B achieves 84.10% versus CoTTA's 83.81%. While these results are favorable, the clinical relevance of a $\\approx$1% AUC improvement remains uncertain. A discussion on the practical impact of such performance gains in real-world clinical settings would add value to the paper.\n\n3. Figure 5 shows the sequential update mechanism degrades over time, but the paper doesn't investigate why or propose solutions. This also limits applicability in streaming scenarios where resetting to pretrained weights may not be desirable.", "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": "CI0ZQ8W59E", "reviewer": "Reviewer_f1HV", "summary": "This paper introduces Adaptive Test-Time Training (AdaTTT), a novel framework designed to get rid of large-scale labeling and retraining, and enhance model robustness for predicting the need for invasive mechanical ventilation (IMV) in ICU patients across multi-center EHR datasets. It builds upon traditional test-time training (TTT) by addressing two main challenges: (1) ensuring stronger alignment between main and auxiliary tasks, and (2) maintaining robustness under domain shifts in clinical data.", "questions": "* The paper mentions that Site B includes ICU admissions between January 2023–August 2024. Does this dataset originate from a completely independent institution or another ICU within the same healthcare network as Site A?\n* The paper currently does not include the summary statistics. Could you include a demographics table to help assess whether differences in population structure might explain performance gaps across sites?\n* For the prototype learning, in the paper, it states that \"The prototype set size is k = 4\". Could you provide more details of how this value is chosen and if there's any exploration of how sensitive AdaTTT is to larger k?\n* Did domain experts review these feature importance trends, or do they only reflect internal gradient magnitudes?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* This paper introduces a theoretically principled and practically motivated adaptation framework combining information theory, SSL, and transport alignment.\n* This paper includes experiments across real EHR datasets with fair baseline comparisons and ablation studies.\n* This paper is structured and well-written, with figures showing dynamic risk evolution and feature importance shifts.\n* The experiment result demonstrates consistent improvement in both predictive accuracy and calibration under domain shifts — a critical problem for clinical ML deployment.", "weaknesses": "* The testing windows for Site A (Jan–Jun 2024) and Site B (Jan 2023–Aug 2024) partially overlap, which raises the possibility that similar patient populations, care protocols, or even duplicated encounters could appear in both test sets. Such overlap could inflate generalization performance by reducing the effective distribution shift the model faces.\n* Although Appendix B.1 outlines inclusion criteria (≥ 5 hours ICU stay, no prior IMV, etc.), it does not explain how cohorts were sampled from each institution, whether patient IDs were cross-checked for duplication across sites, or how temporal leakage was prevented between development and test periods. It would be helpful if the author could provide a clear population selection process figure for further reference.\n* While the author provides plots for feature importance through multiple iterations and risk trajectories, the paper does not include any clinical human evaluation to verify that the learned feature relevance aligns with actual clinical reasoning.", "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": "eUVfbrw0FK", "reviewer": "Reviewer_qxk8", "summary": "The paper proposes Adaptive Test-Time Training (AdaTTT) for predicting 24-hour invasive mechanical ventilation (IMV) need from multi-center EHR data under distribution shift. AdaTTT adapts an IMV predictor at inference time on multi-site EHR data using  \n(i) an info-theoretic bound to guide auxiliary SSL,  \n(ii) dynamic feature-aware masking, and  \n(iii) prototype alignment via partial optimal transport (POT).  \nAcross three cohorts (incl. MIMIC-IV), it shows consistent but modest AUROC gains (~1–2%) better calibration (Brier, reliability plots). Authors added stronger baselines (CoTTA, SAR, T3A), runtime analysis, and ablations; some concerns remain.", "questions": "1. What exactly is novel about AdaTTT compared to existing TTT, TTT++, ClusT3, or contrastive/temporal SSL frameworks?\n\n2. Are the vision-based TTT baselines fairly adapted to structured EHR data?\n\n3. Why choose reconstruction and masked-feature SSL tasks?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Info-theoretic error bound motivates SSL + prototype/POT.  \n- Source-free, per-patient test-time adaptation with bounded updates and reset-to-clean safeguard.  \n-  Consistent multi-site improvements; improved calibration; expanded baselines and ablations.", "weaknesses": "-  Gains (~1%) may fall within noise; lacks decision-curve or threshold-level analysis.  \n-  Vision-based baselines may not adapt fairly to tabular EHR; no EHR-transformer baseline.  \n-  No subgroup audit; only tested on IMV/EHR; reproducibility details partly scattered.", "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": "EZ1fJlVeGL", "reviewer": "Reviewer_dW9F", "summary": "The paper proposes a variant of test-time training for tabular data in which they predict invasive mechanical ventilation in the intensive care.  They derive an information-theoretic bound on the prediction error, which suggests that the prediction error is bounded by the entropy between the main task labels and the auxiliary task labels at test time. The authors use this insight to propose an adaptive self-supervised task that masks features with probabilities depending on their importance for the main task. The authors additionally a variant of prototype-guided adaptation. The method and baselines from the image domain are trained on data from Site A and evaluate on a temporal holdout (Site A) and two geographical hold-outs (Site B and MIMIC).", "questions": "- Could the authors comment on how realistic their assumption of $Y'_s \\rightarrow Z' \\rightarrow Y'_m$ is for the main and auxiliary task they are investigating? What is the implication if it doesn't hold? \n- I was unable to find details on how cluster assignment $\\mathcal{A}(z_i)$ was performed. Please add details on this in the manuscript.   \n- Could the authors comment on why they used the specific version of OT rather than more conventional POT like the referenced version by Chapel? \n- All baselines are NN-based and were adapted from the image domain. There seem to be recent works on test-time adaptation that were specifically created for tabular data, such as TabLog presented at ICML by Ren et al. (2024). Could the authors comment on why no dedicated methods for tabular data were included as baselines?\n- The conclusions seem too strong. While the method proposed by the authors did outperform the baselines (most of which performed worse than doing nothing), its improvement over doing nothing was marginal and seems to highlight a limited benefit of test-time training over leaving the model unchanged at test-time. If the authors think their method does provide a meaningful practical benefit for their use case, it would be good to demonstrate this with additional evaluations.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The paper derives bounds on the prediction error that provide an information-theoretic perspective on how to choose a suitable SSL objective, which they use to propose an SSL task suitable for longitudinal, tabular ICU data. \n- The authors perform several ablation studies showing the contributions of individual components.", "weaknesses": "- The theoretical insight that the SSL task should align with the main task is of limited novelty and already discussed, albeit from another angle, in the original work on test-time training by Sun et al. \n- I understand how the theoretical insights motivate using an adaptive masking strategy for SSL. The motivation for using both SSL *and* prototypes is less clear to me, though. The result is a complicated method with six different loss terms. Despite this complexity, the method barely outperforms doing no test-time training. \n- The experiments are limited to a single disease area in a single setting. It would be relatively easy to include other outcomes in the ICU (e.g., acute kidney injury or the classic sepsis example from the PhysioNet Challenge 2019).", "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)