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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-05-29 · via Proceedings of Machine Learning Research

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Volume 287: Conference on Health, Inference, and Learning, 25-27 June 2025, Pauley Ballroom, Martin Luther King Jr. Building at UC Berkeley, Berkeley, USA

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Editors: Xuhai Orson Xu, Edward Choi, Pankhuri Singhal, Walter Gerych, Shengpu Tang, Monica Agrawal, Adarsh Subbaswamy, Elena Sizikova, Jessilyn Dunn, Roxana Daneshjou, Tasmie Sarker, Matthew McDermott, Irene Chen

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Learning Disease Progression Models That Capture Health Disparities

Erica Chiang, Divya M Shanmugam, Ashley Beecy, Gabriel Sayer, Deborah Estrin, Nikhil Garg, Emma Pierson; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:1-29

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Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs

; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:30-42

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KEEP: Integrating Medical Ontologies with Clinical Data for Robust Code Embeddings

Ahmed Elhussein, Paul Meddeb, Abigail Newbury, Jeanne Mirone, Martin Stoll, Gamze Gursoy; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:43-62

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Benchmarking ECG Delineation using Deep Neural Network-based Semantic Segmentation Models

Jaeho Park, TaeJun Park, Joon-myoung Kwon, Yong-Yeon Jo; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:63-88

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Electrocardiogram–Language Model for Few-Shot Question Answering with Meta Learning

Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo, Aaqib Saeed; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:89-104

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A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs

Yihan Lin, Zhirong Yu, Simon A. Lee; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:105-129

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Multiaccuracy for Subpopulation Calibration Over Distribution Shift in Medical Prediction Models

Daniel Kapash, Noam Barda, Omer Reingold, Noa Dagan, Ran Balicer; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:130-144

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WatchSleepNet: A Novel Model and Pretraining Approach for Advancing Sleep Staging with Smartwatches

Will Ke Wang, Bill Chen, Jiamu Yang, Hayoung Jeong, Leeor Hershkovich, Shekh Md Mahmudul Islam, Mengde Liu, Ali R Roghanizad, Md Mobashir Hasan Shandhi, Andrew R Spector, Jessilyn Dunn; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:145-165

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Contrastive Pretraining for Stress Detection with Multimodal Wearable Sensor Data and Surveys

Zeyu Yang, Han Yu, Akane Sano; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:166-178

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Causal considerations can deterimine the utility of machine learning assisted GWAS

Sumit Mukherjee, ZACHARY R MCCAW, David Amar, Rounak Dey, Thomas W Soare, Hari Somineni, Nicholas Eriksson, Colm O’Dushlaine; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:179-193

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Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-compliance

Winston Chen, Trenton Chang, Jenna Wiens; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:194-230

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CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning

Tsai-Ning Wang, Lin-Lin Chen, Neil Zeghidour, Aaqib Saeed; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:231-246

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Towards Predicting Temporal Changes in a Patient’s Chest X-ray Images based on Electronic Health Records

Daeun Kyung, Junu Kim, Tackeun Kim, Edward Choi; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:247-267

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Beyond Prompting: Time2Lang - Bridging Time-Series Foundation Models and Large Language Models for Health Sensing

Arvind Pillai, Dimitris Spathis, Subigya Nepal, Amanda C. Collins, Daniel M Mackin, Michael V. Heinz, Tess Z Griffin, Nicholas C. Jacobson, Andrew Campbell; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:268-288

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When Attention Fails: Pitfalls of Attention-based Model Interpretability for High-dimensional Clinical Time-Series

Shashank Yadav, Vignesh Subbian; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:289-305

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Global Deep Forecasting with Patient-Specific Pharmacokinetics

Willa Potosnak, Cristian Ignacio Challu, Kin G. Olivares, Keith A Dufendach, Artur Dubrawski; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:306-336

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ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orders

Zongliang Ji, Andre Carlos Kajdacsy-Balla Amaral, Anna Goldenberg, Rahul G Krishnan; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:337-368

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Distributionally Robust Learning in Survival Analysis

Yeping Jin, Lauren Wise, Ioannis Paschalidis; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:369-380

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Test-Time Calibration: A Framework for Personalized Test-Time Adaptation in Real-World Biosignals

Yong-Yeon Jo, Byeong Tak Lee, Jeong-Ho Hong, Hak Seung Lee, Joon-myoung Kwon, Beom Joon Kim; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:381-394

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ALPEC: A Comprehensive Evaluation Framework and Dataset for Machine Learning-Based Arousal Detection in Clinical Practice

Stefan Kraft, Andreas Theissler, Dr. Vera Wienhausen-Wilke, Philipp Walter, Gjergji Kasneci, Hendrik Lensch; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:395-429

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Treatment Non-Adherence Bias in Clinical Machine Learning: A Real-World Study on Hypertension Medication

Zhongyuan Liang, Arvind Suresh, Irene Y. Chen; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:430-442

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Predicting Health States of Patients with Chronic Pain from Cellphone Usage Data

Maya Stemmer, Lior Ungar, Talia Friedman, Lihi Bik, Yotam Hadari, Itamar Efrati, Yarden Rachamim, Lior Carmi, Shai Fine; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:443-457

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Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?

Hye Sun Yun, Karen Y.C. Zhang, Ramez Kouzy, Iain James Marshall, Junyi Jessy Li, Byron C Wallace; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:458-479

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Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases

Adedolapo Aishat Toye, Asuman Celik, Samantha Kleinberg; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:480-501

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Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis

YongKyung Oh, Alex Bui; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:502-526

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CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports

Xiao Yu Cindy Zhang, Carlos R. Ferreira, Francis Rossignol, Raymond T. Ng, Wyeth Wasserman, Jian Zhu; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:527-542

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Feasibility of Immersive Virtual Reality and Customized Robotics with Wearable Sensors for Upper Extremity Training

Behdokht Kiafar, Pinar Kullu, Rakshith Lokesh, Amit Chaudhari, Qile Wang, Shayla Sharmin, Sagar M. Doshi, Elham Bakhshipour, Erik Thostenson, Joshua Cashaback, Roghayeh Leila Barmaki; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:543-556

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Bridging the utility gap between MALDI-TOF and WGS for affordable outbreak cluster detection

Chang Liu, Jieshi Chen, Lee H Harrison, Artur Dubrawski; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:557-572

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The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis

Shahriar Noroozizadeh, Pim Welle, Jeremy Weiss, George H. Chen; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:573-609

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Investigating Primary Care Indications to Improve the Quality of Electronic Health Record Data in Target Trial Emulation for Dementia

Max I Sunog, Colin Magdamo, Marie-Laure Charpignon, Mark W. Albers; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:610-648

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HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation

Julian Nicolas Acosta, Xiaoman Zhang, Siddhant Dogra, Hong-Yu Zhou, Seyedmehdi Payabvash, Guido J. Falcone, Eric Karl Oermann, Pranav Rajpurkar; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:649-671

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Predicting Partially Observed Long-Term Outcomes with Adversarial Positive-Unlabeled Domain Adaptation

Mengying Yan, Meng Xia, Wei Angel Huang, Chuan Hong, Benjamin Goldstein, Matthew M. Engelhard; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:672-690

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Learning Interactions Between Continuous Treatments and Covariates with a Semiparametric Model

Muyan Jiang, Yunkai Zhang, Anil Aswani; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:691-707

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The Latentverse: An Open-Source Benchmarking Toolkit for Evaluating Latent Representations

Yoanna Turura, Sam Freesun Friedman, Aurora Cremer, Mahnaz Maddah, Sana Tonekaboni; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:708-719

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How does my language model understand clinical text?

Furong Jia, David Sontag, Monica Agrawal; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:720-743

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Multi-Objective Fine-Tuning of Clinical Scoring Tables: Adapting to Variations in Demography and Data

Kei Sen Fong, Mehul Motani; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:744-780

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MedMod: Multimodal Benchmark for Medical Prediction Tasks with Electronic Health Records and Chest X-Ray Scans

Shaza Elsharief, Saeed Shurrab, Baraa Al Jorf, Leopoldo Julian Lechuga Lopez, Krzysztof J. Geras, Farah E. Shamout; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:781-803

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Transformer Model for Alzheimer’s Disease Progression Prediction Using Longitudinal Visit Sequences

Mahdi Moghaddami, Clayton Schubring, Mohammad Siadat; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:804-816

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LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records

Sujeong Im, Jungwoo Oh, Edward Choi; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:817-843

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A Study of Artifacts on Melanoma Classification under Diffusion-Based Perturbations

Qixuan Jin, Marzyeh Ghassemi; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:844-861

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Uncertainty Quantification for Machine Learning in Healthcare: A Survey

Leopoldo Julian Lechuga Lopez, Shaza Elsharief, Dhiyaa Al Jorf, Firas Darwish, Congbo Ma, Farah E. Shamout; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:862-907

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Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction

Leisheng Yu, Yanxiao Cai, Minxing Zhang, Xia Hu; Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:908-924

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