Stephen Pfohl

According to our database, Stephen Pfohl authored at least 29 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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

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, Yun Liu, Mike Schaekermann, Alanna Walton, Alicia Parrish, Chirag Nagpal, Preeti Singh, Akeiylah Dewitt, Philip Andrew Mansfield, Sushant Prakash, Katherine A. Heller, Alan Karthikesalingam, Christopher Semturs, Joelle K. Barral, Greg Corrado, Yossi Matias, Jamila Smith-Loud, Ivor Horn, Karan Singhal

CoRR, 2024

A Causal Perspective on Label Bias.

Vishwali Mhasawade, Alexander D'Amour, Stephen R. Pfohl

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 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

Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, 2024

Proxy Methods for Domain Adaptation.

Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

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., November, 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, Jonathan Berant

CoRR, 2023

Towards Expert-Level Medical Question Answering with Large Language Models.

Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, Darlene Neal, Mike Schaekermann, Amy Wang, Mohamed Amin, Sami Lachgar, Philip Andrew Mansfield, Sushant Prakash, Bradley Green, Ewa Dominowska, Blaise Agüera y Arcas, Nenad Tomasev, Yun Liu, Renee Wong, Christopher Semturs, S. Sara Mahdavi, Joelle K. Barral, Dale R. Webster, Gregory S. Corrado, Yossi Matias, Shekoofeh Azizi, Alan Karthikesalingam, Vivek Natarajan

CoRR, 2023

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

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, Sarah Chobot, Stephen Pfohl, Siyun Li, Kenny Shum, Nitin Parikh, Priya Desai, Briththa Seevaratnam, Melanie Hanson, Margaret Smith, Yizhe Xu, Arjun Gokhale, Steven Lin, Michael A. Pfeffer, Winifred Teuteberg, Nigam H. Shah

Frontiers Digit. Health, 2022

Large Language Models Encode Clinical Knowledge.

Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Kumar Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Nathaneal Schärli, Aakanksha Chowdhery, Philip Andrew Mansfield, Blaise Agüera y Arcas, Dale R. Webster, Gregory S. Corrado, Yossi Matias, Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Rajkomar, Joelle K. Barral, Christopher Semturs, Alan Karthikesalingam, Vivek Natarajan

CoRR, 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

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Improving the Fairness of Chest X-ray Classifiers.

Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Pfohl, Marzyeh Ghassemi

Proceedings of the Conference on Health, Inference, and Learning, 2022

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

An empirical characterization of fair machine learning for clinical risk prediction.

Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah

J. Biomed. Informatics, 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

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

A comparison of approaches to improve worst-case predictive model performance over patient subpopulations.

Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah

CoRR, 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

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

Proceedings of the Machine Learning for Health, 2021

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) 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

Proceedings of the Machine Learning for Health Workshop, 2020

2019

Federated and Differentially Private Learning for Electronic Health Records.

Stephen R. Pfohl, Andrew M. Dai, Katherine A. Heller

CoRR, 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

Proceedings of the Biocomputing 2019: Proceedings of the Pacific Symposium, 2019

Counterfactual Reasoning for Fair Clinical Risk Prediction.

Stephen R. Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah

Proceedings of the Machine Learning for Healthcare Conference, 2019

Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk.

Stephen Pfohl, Ben J. Marafino, Adrien Coulet, Fátima Rodriguez, Latha Palaniappan, Nigam H. Shah

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

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

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

Transfer learning to adapt predictive models for pediatric patients in the EHR.

Stephen R. Pfohl, Nigam Shah

Proceedings of the AMIA 2018, 2018