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Instability in clinical risk stratification models using deep learning


Nov 20, 2022
Daniel Lopez-Martinez, Alex Yakubovich, Martin Seneviratne, Adam D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen

* Accepted for publication in Machine Learning for Health (ML4H) 2022 

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Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare


Feb 03, 2022
Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam H. Shah


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


Aug 27, 2021
Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah


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Trove: Ontology-driven weak supervision for medical entity classification


Aug 05, 2020
Jason A. Fries, Ethan Steinberg, Saelig Khattar, Scott L. Fleming, Jose Posada, Alison Callahan, Nigam H. Shah


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An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction


Jul 20, 2020
Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah


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Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data


Jan 06, 2020
Ethan Steinberg, Ken Jung, Jason A. Fries, Conor K. Corbin, Stephen R. Pfohl, Nigam H. Shah


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Counterfactual Reasoning for Fair Clinical Risk Prediction


Jul 14, 2019
Stephen Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah

* Machine Learning for Healthcare 2019 

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A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data


Jan 17, 2019
Albee Y. Ling, Allison W. Kurian, Jennifer L. Caswell-Jin, George W. Sledge Jr., Nigam H. Shah, Suzanne R. Tamang


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Predicting Inpatient Discharge Prioritization With Electronic Health Records


Dec 02, 2018
Anand Avati, Stephen Pfohl, Chris Lin, Thao Nguyen, Meng Zhang, Philip Hwang, Jessica Wetstone, Kenneth Jung, Andrew Ng, Nigam H. Shah


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The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data


Oct 04, 2018
Daisy Yi Ding, Chloé Simpson, Stephen Pfohl, Dave C. Kale, Kenneth Jung, Nigam H. Shah

* Pacific Symposium on Biocomputing (PSB) 2019, Hawaii, https://psb.stanford.edu/psb-online/; 13 pages, 7 figures; updated with the camera-ready version of the manuscript 

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