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Journal Articles
- 2023
[j7]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:
Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks. J. Am. Medical Informatics Assoc. 30(12): 2004-2011 (2023)- 2022
- [j6]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:
Considerations in the reliability and fairness audits of predictive models for advance care planning. Frontiers Digit. Health 4 (2022) - 2021
- [j5]Lin Lawrence Guo, Stephen R. Pfohl, Jason Alan Fries, José D. Posada, Scott L. Fleming, Catherine Aftandilian, Nigam Shah, Lillian Sung:
Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine. Appl. Clin. Inform. 12(4): 808-815 (2021) - [j4]Birju S. Patel, Ethan Steinberg, Stephen R. Pfohl, Nigam H. Shah:
Learning decision thresholds for risk stratification models from aggregate clinician behavior. J. Am. Medical Informatics Assoc. 28(10): 2258-2264 (2021) - [j3]Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah:
An empirical characterization of fair machine learning for clinical risk prediction. J. Biomed. Informatics 113: 103621 (2021) - [j2]Ethan Steinberg, Kenneth Jung, Jason A. Fries, Conor K. Corbin, Stephen R. Pfohl, Nigam H. Shah:
Language models are an effective representation learning technique for electronic health record data. J. Biomed. Informatics 113: 103637 (2021) - 2018
- [j1]Stephen R. Pfohl, Renaid B. Kim, Grant S. Coan, Cassie S. Mitchell:
Unraveling the Complexity of Amyotrophic Lateral Sclerosis Survival Prediction. Frontiers Neuroinformatics 12: 36 (2018)
Conference and Workshop Papers
- 2024
- [c11]Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton:
Proxy Methods for Domain Adaptation. AISTATS 2024: 3961-3969 - [c10]Vishwali Mhasawade, Alexander D'Amour, Stephen R. Pfohl:
A Causal Perspective on Label Bias. FAccT 2024: 1282-1294 - 2023
- [c9]Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai:
Adapting to Latent Subgroup Shifts via Concepts and Proxies. AISTATS 2023: 9637-9661 - 2022
- [c8]Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Pfohl, Marzyeh Ghassemi:
Improving the Fairness of Chest X-ray Classifiers. CHIL 2022: 204-233 - [c7]Stephen Pfohl, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam Shah:
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare. FAccT 2022: 1039-1052 - 2021
- [c6]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:
Machine Learning for Health (ML4H) 2021. ML4H@NeurIPS 2021: 1-12 - 2020
- [c5]Suproteem K. Sarkar, Subhrajit Roy, Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Ioana Bica, Griffin Adams, Stephen Pfohl, Stephanie L. Hyland:
Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All. ML4H@NeurIPS 2020: 1-11 - 2019
- [c4]Stephen Pfohl, Ben J. Marafino, Adrien Coulet, Fátima Rodriguez, Latha Palaniappan, Nigam H. Shah:
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk. AIES 2019: 271-278 - [c3]Stephen R. Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah:
Counterfactual Reasoning for Fair Clinical Risk Prediction. MLHC 2019: 325-358 - [c2]Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah:
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data. PSB 2019: 18-29 - 2018
- [c1]Stephen R. Pfohl, Nigam Shah:
Transfer learning to adapt predictive models for pediatric patients in the EHR. AMIA 2018
Editorship
- 2021
- [e1]Subhrajit Roy, Stephen Pfohl, Emma Rocheteau, Girmaw Abebe Tadesse, Luis Oala, Fabian Falck, Yuyin Zhou, Liyue Shen, Ghada Zamzmi, Purity Mugambi, Ayah Zirikly, Matthew B. A. McDermott, Emily Alsentzer:
Machine Learning for Health, ML4H@NeurIPS 2021, 04 December 2021, Virtual Event. Proceedings of Machine Learning Research 158, PMLR 2021 [contents]
Informal and Other Publications
- 2024
- [i18]Mercy Nyamewaa Asiedu, Awa Dieng, Iskandar Haykel, Negar Rostamzadeh, Stephen Pfohl, Chirag Nagpal, Maria Nagawa, Abigail Oppong, Sanmi Koyejo, Katherine A. Heller:
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa. CoRR abs/2403.03357 (2024) - [i17]Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton:
Proxy Methods for Domain Adaptation. CoRR abs/2403.07442 (2024) - [i16]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:
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models. CoRR abs/2403.12025 (2024) - 2023
- [i15]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:
Towards Expert-Level Medical Question Answering with Large Language Models. CoRR abs/2305.09617 (2023) - [i14]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:
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking. CoRR abs/2312.09244 (2023) - 2022
- [i13]Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam H. Shah:
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare. CoRR abs/2202.01906 (2022) - [i12]Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Robert Pfohl, Marzyeh Ghassemi:
Improving the Fairness of Chest X-ray Classifiers. CoRR abs/2203.12609 (2022) - [i11]Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai:
Adapting to Latent Subgroup Shifts via Concepts and Proxies. CoRR abs/2212.11254 (2022) - [i10]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:
Large Language Models Encode Clinical Knowledge. CoRR abs/2212.13138 (2022) - 2021
- [i9]Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah:
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations. CoRR abs/2108.12250 (2021) - [i8]Fabian Falck, Yuyin Zhou, Emma Rocheteau, Liyue Shen, Luis Oala, Girmaw Abebe, Subhrajit Roy, Stephen Pfohl, Emily Alsentzer, Matthew B. A. McDermott:
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021. CoRR abs/2112.00179 (2021) - 2020
- [i7]Ethan Steinberg, Kenneth Jung, Jason A. Fries, Conor K. Corbin, Stephen R. Pfohl, Nigam H. Shah:
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data. CoRR abs/2001.05295 (2020) - [i6]Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah:
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction. CoRR abs/2007.10306 (2020) - 2019
- [i5]Stephen Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah:
Counterfactual Reasoning for Fair Clinical Risk Prediction. CoRR abs/1907.06260 (2019) - [i4]Stephen R. Pfohl, Andrew M. Dai, Katherine A. Heller:
Federated and Differentially Private Learning for Electronic Health Records. CoRR abs/1911.05861 (2019) - 2018
- [i3]Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah:
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data. CoRR abs/1808.03331 (2018) - [i2]Stephen Pfohl, Ben J. Marafino, Adrien Coulet, Fátima Rodriguez, Latha Palaniappan, Nigam H. Shah:
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk. CoRR abs/1809.04663 (2018) - [i1]Anand Avati, Stephen Pfohl, Chris Lin, Thao Nguyen, Meng Zhang, Philip Hwang, Jessica Wetstone, Kenneth Jung, Andrew Y. Ng, Nigam H. Shah:
Predicting Inpatient Discharge Prioritization With Electronic Health Records. CoRR abs/1812.00371 (2018)
Coauthor Index
aka: Nigam Shah
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