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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 209: Conference on Health, Inference, and Learning, , 415 Main Street, Cambridge, MA USA 02142

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Editors: Bobak J. Mortazavi, Tasmie Sarker, Andrew Beam, Joyce C. Ho

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Conference on Health, Inference, and Learning (CHIL) 2023

; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:1-5

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Virus2Vec: Viral Sequence Classification Using Machine Learning

Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Pin-Yu Chen, Imdad Ullah Khan, Murray Patterson; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:6-18

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Adaptive Weighted Multi-View Clustering

Shuo Shuo Liu, Lin Lin; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:19-36

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Bayesian Active Questionnaire Design for Cause-of-Death Assignment Using Verbal Autopsies

Toshiya Yoshida, Trinity Shuxian Fan, Tyler McCormick, Wu Zhenke, Zehang Richard Li; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:37-49

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Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models

Siyi Tang, Jared A Dunnmon, Qu Liangqiong, Khaled K Saab, Tina Baykaner, Christopher Lee-Messer, Daniel L Rubin; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:50-71

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Token Imbalance Adaptation for Radiology Report Generation

Yuexin Wu, I-Chan Huang, Xiaolei Huang; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:72-85

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Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruna; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:86-99

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Revisiting Machine-Learning based Drug Repurposing: Drug Indications Are Not a Right Prediction Target

Siun Kim, Jung-Hyun Won, David Seung U Lee, Renqian Luo, Lijun Wu, Yingce Xia, Tao Qin, Howard Lee; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:100-116

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Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark

Li Xu, Bo Liu, Ameer Hamza Khan, Lu Fan, Xiao-Ming Wu; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:117-132

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SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder

Mingyu Tang, Jiechao Gao, Guimin Dong, Carl Yang, Bradford Campbell, Brendan Bowman, Jamie Marie Zoellner, Emaad Abdel-Rahman, Mehdi Boukhechba; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:133-146

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Federated Multilingual Models for Medical Transcript Analysis

Andrea Manoel, Mirian del Carmen Hipolito Garcia, Tal Baumel, Shize Su, Jialei Chen, Robert Sim, Dan Miller, Danny Karmon, Dimitrios Dimitriadis; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:147-162

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Towards the Practical Utility of Federated Learning in the Medical Domain

Hyeonji Hwang, Seongjun Yang, Daeyoung Kim, Radhika Dua, Jong-Yeup Kim, Eunho Yang, Edward Choi; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:163-181

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Semantic match: Debugging feature attribution methods \titlebreak in XAI for healthcare

Giovanni Cina, Tabea E Rober, Rob Goedhard, S Ilker Birbil; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:182-190

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Self-Supervised Pretraining and Transfer Learning Enable\titlebreak Flu and COVID-19 Predictions in Small Mobile Sensing Datasets

Mika A Merrill, Tim Althoff; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:191-206

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Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections

Mike A Merrill, Esteban Safranchik, Arinbjörn Kolbeinsson, Piyusha Gade, Ernesto Ramirez, Ludwig Schmidt, Luca Foschini, Tim Althoff; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:207-228

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Collecting data when missingness is unknown: a method for improving model performance given under-reporting in patient populations

Kevin Wu, Dominik Dahlem, Christopher Hane, Eran Halperin, James Zou; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:229-242

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Large-Scale Study of Temporal Shift in Health Insurance Claims

Christina X Ji, Ahmed M Alaa, David Sontag; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:243-278

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Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning

Arvind Pillai, Subigya Nepal, Andrew Campbell; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:279-293

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Rediscovery of CNN’s Versatility for Text-based Encoding of Raw Electronic Health Records

Eunbyeol Cho, Minjae Lee, Kyunghoon Hur, Jiyoun Kim, Jinsung Yoon, Edward Choi; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:294-313

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Clinical Relevance Score for Guided Trauma Injury Pattern Discovery with Weakly Supervised $β$-VAE

Qixuan Jin, Jacobien HF Oosterhoff, Yepeng Huang, Marzyeh Ghassemi, Gabriel A Brat; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:314-339

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Who Controlled the Evidence? Question Answering for Disclosure Information Extraction

Hardy Hardy, Derek Ruths, Nicholas B. King; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:340-349

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Fair admission risk prediction with proportional multicalibration

William G La Cava, Elle Lett, Guangya Wan; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:350-378

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Neural Fine-Gray: Monotonic neural networks for competing risks

Vincent Jeanselme, Chang Ho Yoon, Brian Tom, Jessica Barrett; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:379-392

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Denoising Autoencoders for Learning from Noisy Patient-Reported Data

Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:393-409

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Contrastive Learning of Electrodermal Activity Representations for Stress Detection

Katie Matton, Robert Lewis, John Guttag, Rosalind Picard; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:410-426

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Machine Learning for Arterial Blood Pressure Prediction

Jessica Zheng, Hanrui Wang, Anand Chandrasekhar, Aaron D Aguirre, Song Han, Hae-Seung Lee, Charles G Sodini; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:427-439

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A General Framework for Visualizing Embedding Spaces of\titlebreak Neural Survival Analysis Models Based on Angular Information

George H Chen; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:440-476

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Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise

Donna Tjandra, Jenna Wiens; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:477-497

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Evaluating Model Performance in Medical Datasets Over Time

Helen Zhou, Yuwen Chen, Zachary Lipton; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:498-508

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MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction

Iman Deznabi, Madalina Fiterau; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:509-525

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PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis

Yi Yang, Hejie Cui, Carl Yang; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:526-544

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Understanding and Predicting the Effect of Environmental Factors on People with Type 2 Diabetes

Kailas Vodrahalli, Gregory D Lyng, Brian L Hill, Kimmo Karkkainen, Jeffrey Hertzberg, James Zou, Eran Halperin; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:545-555

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Explaining a machine learning decision to physicians via counterfactuals

Supriya Nagesh, Nina Mishra, Yonatan Naamad, James M Rehg, Mehul A Shah, Alexei Wagner; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:556-577

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Do We Still Need Clinical Language Models?

Eric Lehman, Evan Hernandez, Diwakar Mahajan, Jonas Wulff, Micah J Smith, Zachary Ziegler, Daniel Nadler, Peter Szolovits, Alistair Johnson, Emily Alsentzer; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:578-597

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