| Large language models encode clinical knowledge K Singhal, S Azizi, T Tu, SS Mahdavi, J Wei, HW Chung, N Scales, ... Nature 620 (7972), 172-180, 2023 | 2344 | 2023 |
| Toward expert-level medical question answering with large language models K Singhal, T Tu, J Gottweis, R Sayres, E Wulczyn, M Amin, L Hou, K Clark, ... Nature Medicine, 1-8, 2025 | 707 | 2025 |
| An empirical characterization of fair machine learning for clinical risk prediction SR Pfohl, A Foryciarz, NH Shah Journal of biomedical informatics 113, 103621, 2021 | 124 | 2021 |
| Language models are an effective representation learning technique for electronic health record data E Steinberg, K Jung, JA Fries, CK Corbin, SR Pfohl, NH Shah Journal of Biomedical Informatics 113, 103637, 2021 | 122 | 2021 |
| The value of standards for health datasets in artificial intelligence-based applications A Arora, JE Alderman, J Palmer, S Ganapathi, E Laws, MD McCradden, ... Nature Medicine 29 (11), 2929-2938, 2023 | 108 | 2023 |
| Characterization of the contribution of genetic background and gender to disease progression in the SOD1 G93A mouse model of amyotrophic lateral sclerosis: a meta-analysis SR Pfohl, MT Halicek, CS Mitchell Journal of neuromuscular diseases 2 (2), 137-150, 2015 | 108 | 2015 |
| Creating fair models of atherosclerotic cardiovascular disease risk S Pfohl, B Marafino, A Coulet, F Rodriguez, L Palaniappan, NH Shah Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 271-278, 2019 | 82 | 2019 |
| Federated and differentially private learning for electronic health records SR Pfohl, AM Dai, K Heller arXiv preprint arXiv:1911.05861, 2019 | 80 | 2019 |
| Counterfactual Reasoning for Fair Clinical Risk Prediction SR Pfohl, T Duan, DY Ding, NH Shah Proceedings of the 4th Machine Learning for Healthcare Conference 106, 325-358, 2019 | 80 | 2019 |
| Improving the Fairness of Chest X-ray Classifiers H Zhang, N Dullerud, K Roth, L Oakden-Rayner, S Pfohl, M Ghassemi Conference on Health, Inference, and Learning, 204-233, 2022 | 75 | 2022 |
| Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine LL Guo, SR Pfohl, J Fries, AEW Johnson, J Posada, C Aftandilian, N Shah, ... Scientific Reports 12 (1), 1-10, 2022 | 73 | 2022 |
| Tackling bias in AI health datasets through the STANDING Together initiative S Ganapathi, J Palmer, JE Alderman, M Calvert, C Espinoza, J Gath, ... Nature Medicine 28 (11), 2232-2233, 2022 | 64 | 2022 |
| Unraveling the complexity of amyotrophic lateral sclerosis survival prediction SR Pfohl, RB Kim, GS Coan, CS Mitchell Frontiers in neuroinformatics 12, 36, 2018 | 47 | 2018 |
| Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking J Eisenstein, C Nagpal, A Agarwal, A Beirami, A D'Amour, DJ Dvijotham, ... arXiv preprint arXiv:2312.09244, 2023 | 46 | 2023 |
| Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine LL Guo, SR Pfohl, J Fries, J Posada, SL Fleming, C Aftandilian, N Shah, ... Applied Clinical Informatics 12 (04), 808-815, 2021 | 46 | 2021 |
| EHR foundation models improve robustness in the presence of temporal distribution shift LL Guo, E Steinberg, SL Fleming, J Posada, J Lemmon, SR Pfohl, N Shah, ... Scientific Reports 13 (1), 3767, 2023 | 43 | 2023 |
| A comparison of approaches to improve worst-case predictive model performance over patient subpopulations SR Pfohl, H Zhang, Y Xu, A Foryciarz, M Ghassemi, NH Shah Scientific Reports 12 (1), 1-13, 2022 | 37 | 2022 |
| Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network Q Wang, JM Reps, KF Kostka, PB Ryan, Y Zou, EA Voss, PR Rijnbeek, ... PloS one 15 (1), e0226718, 2020 | 33 | 2020 |
| Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare S Pfohl, Y Xu, A Foryciarz, N Ignatiadis, J Genkins, N Shah Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 32 | 2022 |
| The effectiveness of multitask learning for phenotyping with electronic health records data DY Ding, C Simpson, S Pfohl, DC Kale, K Jung, NH Shah Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 24, 18, 2019 | 32 | 2019 |
Nigam ShahProfessor of Medicine, and Biomedical Data Science, Stanford UniversityAdresse e-mail validée de stanford.edu