Transfer learning to adapt predictive models for pediatric patients in the EHR. - Stephen R. Pfohl; Nigam Shah (AMIA, 2018)

Predicting Inpatient Discharge Prioritization With Electronic Health Records. - Anand Avati; Stephen Pfohl; Chris Lin; Thao Nguyen; Meng Zhang; Philip Hwang; Jessica Wetstone; Kenneth Jung; Andrew Y. Ng; Nigam H. Shah (CoRR, 2018)

Unraveling the Complexity of Amyotrophic Lateral Sclerosis Survival Prediction. - Stephen R. Pfohl; Renaid B. Kim; Grant S. Coan; Cassie S. Mitchell (Frontiers Neuroinformatics, 2018)

Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk. - Stephen Pfohl; Ben J. Marafino; Adrien Coulet; Fátima Rodriguez; Latha Palaniappan; Nigam H. Shah (AIES, 2019)

Counterfactual Reasoning for Fair Clinical Risk Prediction. - Stephen R. Pfohl; Tony Duan; Daisy Yi Ding; Nigam H. Shah (MLHC, 2019)

The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data. - Daisy Yi Ding; Chloé Simpson; Stephen Pfohl; Dave C. Kale; Kenneth Jung; Nigam H. Shah (PSB, 2019)

Federated and Differentially Private Learning for Electronic Health Records. - Stephen R. Pfohl; Andrew M. Dai; Katherine A. Heller (CoRR, 2019)

Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All. - Suproteem K. Sarkar; Subhrajit Roy; Emily Alsentzer; Matthew B. A. McDermott; Fabian Falck; Ioana Bica; Griffin Adams; Stephen Pfohl; Stephanie L. Hyland (ML4H@NeurIPS, 2020)

Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data. - Ethan Steinberg; Kenneth Jung; Jason A. Fries; Conor K. Corbin; Stephen R. Pfohl; Nigam H. Shah (CoRR, 2020)

Machine Learning for Health (ML4H) 2021. - Subhrajit Roy; Stephen Pfohl; Girmaw Abebe Tadesse; Luis Oala; Fabian Falck; Yuyin Zhou; Liyue Shen; Ghada Zamzmi; Purity Mugambi; Ayah Zirikly; Matthew B. A. McDermott; Emily Alsentzer (ML4H@NeurIPS, 2021)

Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine. - Lin Lawrence Guo; Stephen R. Pfohl; Jason Alan Fries; José D. Posada; Scott L. Fleming; Catherine Aftandilian; Nigam Shah; Lillian Sung (Appl. Clin. Inform., 2021)

A comparison of approaches to improve worst-case predictive model performance over patient subpopulations. - Stephen R. Pfohl; Haoran Zhang 003; Yizhe Xu; Agata Foryciarz; Marzyeh Ghassemi; Nigam H. Shah (CoRR, 2021)

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021. - Fabian Falck; Yuyin Zhou; Emma Rocheteau; Liyue Shen; Luis Oala; Girmaw Abebe; Subhrajit Roy; Stephen Pfohl; Emily Alsentzer; Matthew B. A. McDermott (CoRR, 2021)

Learning decision thresholds for risk stratification models from aggregate clinician behavior. - Birju S. Patel; Ethan Steinberg; Stephen R. Pfohl; Nigam H. Shah (J. Am. Medical Informatics Assoc., 2021)

An empirical characterization of fair machine learning for clinical risk prediction. - Stephen R. Pfohl; Agata Foryciarz; Nigam H. Shah (J. Biomed. Informatics, 2021)

Language models are an effective representation learning technique for electronic health record data. - Ethan Steinberg; Kenneth Jung; Jason A. Fries; Conor K. Corbin; Stephen R. Pfohl; Nigam H. Shah (J. Biomed. Informatics, 2021)

Improving the Fairness of Chest X-ray Classifiers. - Haoran Zhang 003; Natalie Dullerud; Karsten Roth; Lauren Oakden-Rayner; Stephen Pfohl; Marzyeh Ghassemi (CHIL, 2022)

Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare. - Stephen Pfohl; Yizhe Xu; Agata Foryciarz; Nikolaos Ignatiadis; Julian Genkins; Nigam Shah (FAccT, 2022)

Large Language Models Encode Clinical Knowledge. - Karan Singhal; Shekoofeh Azizi; Tao Tu 001; S. Sara Mahdavi; Jason Wei; Hyung Won Chung; Nathan Scales; Ajay Kumar Tanwani; Heather Cole-Lewis; Stephen Pfohl; Perry Payne; Martin Seneviratne; et al. (CoRR, 2022)

Considerations in the reliability and fairness audits of predictive models for advance care planning. - Jonathan Lu; Amelia Sattler; Samantha Wang; Ali Raza Khaki; Alison Callahan; Scott L. Fleming; Rebecca Fong; Benjamin Ehlert; Ron C. Li; Lisa Shieh; Kavitha Ramchandran; Michael Gensheimer 001; et al. (Frontiers Digit. Health, 2022)

Adapting to Latent Subgroup Shifts via Concepts and Proxies. - Ibrahim Alabdulmohsin; Nicole Chiou; Alexander D'Amour; Arthur Gretton; Sanmi Koyejo; Matt J. Kusner; Stephen R. Pfohl; Olawale Salaudeen; Jessica Schrouff; Katherine Tsai (AISTATS, 2023)

Towards Expert-Level Medical Question Answering with Large Language Models. - Karan Singhal; Tao Tu 001; Juraj Gottweis; Rory Sayres; Ellery Wulczyn; Le Hou; Kevin Clark; Stephen Pfohl; Heather Cole-Lewis; Darlene Neal; Mike Schaekermann; Amy Wang; et al. (CoRR, 2023)

Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking. - Jacob Eisenstein; Chirag Nagpal; Alekh Agarwal; Ahmad Beirami; Alex D'Amour; Dj Dvijotham; Adam Fisch; Katherine A. Heller; Stephen Pfohl; Deepak Ramachandran; Peter Shaw 004; Jonathan Berant (CoRR, 2023)

Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks. - Joshua Lemmon; Lin Lawrence Guo; Ethan Steinberg; Keith E. Morse; Scott Lanyon Fleming; Catherine Aftandilian; Stephen R. Pfohl; José D. Posada; Nigam Shah; Jason Alan Fries; Lillian Sung (J. Am. Medical Informatics Assoc., 2023)

Proxy Methods for Domain Adaptation. - Katherine Tsai; Stephen R. Pfohl; Olawale Salaudeen; Nicole Chiou; Matt J. Kusner; Alexander D'Amour; Sanmi Koyejo; Arthur Gretton (AISTATS, 2024)

The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa. - Mercy Nyamewaa Asiedu; Awa Dieng; Iskandar Haykel; Negar Rostamzadeh; Stephen Pfohl; Chirag Nagpal; Maria Nagawa; Abigail Oppong; Sanmi Koyejo; Katherine A. Heller (EAAMO, 2024)

A Causal Perspective on Label Bias. - Vishwali Mhasawade; Alexander D'Amour; Stephen R. Pfohl (FAccT, 2024)

A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models. - Stephen R. Pfohl; Heather Cole-Lewis; Rory Sayres; Darlene Neal; Mercy Nyamewaa Asiedu; Awa Dieng; Nenad Tomasev; Qazi Mamunur Rashid; Shekoofeh Azizi; Negar Rostamzadeh; Liam G. McCoy; Leo Anthony Celi; et al. (CoRR, 2024)

Nteasee: A mixed methods study of expert and general population perspectives on deploying AI for health in African countries. - Mercy Nyamewaa Asiedu; Iskandar Haykel; Awa Dieng; Kerrie Kauer; Tousif Ahmed; Florence Ofori; Charisma Chan; Stephen Pfohl; Negar Rostamzadeh; Katherine A. Heller (CoRR, 2024)

Nteasee: Understanding Needs in AI for Health in Africa - A Mixed-Methods Study of Expert and General Population Perspectives. - Mercy Asiedu; Iskandar Haykel; Awa Dieng; Kerrie Kauer; Tousif Ahmed; Florence Ofori; Charisma Chan; Stephen Pfohl; Negar Rostamzadeh; Katherine A. Heller (FAccT, 2025)

Nuanced Safety for Generative AI: How Demographics Shape Responsiveness to Severity. - Pushkar Mishra; Charvi Rastogi; Stephen R. Pfohl; Alicia Parrish; Roma Patel; Mark Diaz; Ding Wang; Michela Paganini; Vinodkumar Prabhakaran; Lora Aroyo; Verena Rieser (CoRR, 2025)

Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness. - Stephen R. Pfohl; Natalie Harris; Chirag Nagpal; David Madras; Vishwali Mhasawade; Olawale Salaudeen; Awa Dieng; Shannon Sequeira; Santiago Eduardo Arciniegas; Lillian Sung; Nnamdi Ezeanochie; Heather Cole-Lewis; et al. (CoRR, 2025)