| Multitask prompted training enables zero-shot task generalization V Sanh, A Webson, C Raffel, SH Bach, L Sutawika, Z Alyafeai, A Chaffin, ... arXiv preprint arXiv:2110.08207, 2021 | 1370 | 2021 |
| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1322 | 2023 |
| Snorkel: Rapid training data creation with weak supervision A Ratner, SH Bach, H Ehrenberg, J Fries, S Wu, C Ré Proceedings of the VLDB endowment. International conference on very large …, 2017 | 1243 | 2017 |
| Promptsource: An integrated development environment and repository for natural language prompts SH Bach, V Sanh, ZX Yong, A Webson, C Raffel, NV Nayak, A Sharma, ... arXiv preprint arXiv:2202.01279, 2022 | 265 | 2022 |
| Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences JA Fries, P Varma, VS Chen, K Xiao, H Tejeda, P Saha, J Dunnmon, ... Nature communications 10 (1), 3111, 2019 | 131 | 2019 |
| Swellshark: A generative model for biomedical named entity recognition without labeled data J Fries, S Wu, A Ratner, C Ré arXiv preprint arXiv:1704.06360, 2017 | 116 | 2017 |
| 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 | 99 | 2021 |
| Ontology-driven weak supervision for clinical entity classification in electronic health records JA Fries, E Steinberg, S Khattar, SL Fleming, J Posada, A Callahan, ... Nature communications 12 (1), 2017, 2021 | 89* | 2021 |
| The shaky foundations of large language models and foundation models for electronic health records M Wornow, Y Xu, R Thapa, B Patel, E Steinberg, S Fleming, MA Pfeffer, ... npj Digital Medicine 6 (1), 135, 2023 | 82* | 2023 |
| Monitoring hand hygiene via human observers: how should we be sampling? J Fries, AM Segre, G Thomas, T Herman, K Ellingson, PM Polgreen Infection Control & Hospital Epidemiology 33 (7), 689-695, 2012 | 68 | 2012 |
| Brundlefly at SemEval-2016 Task 12: Recurrent neural networks vs. joint inference for clinical temporal information extraction JA Fries arXiv preprint arXiv:1606.01433, 2016 | 59 | 2016 |
| Medical device surveillance with electronic health records A Callahan, JA Fries, C Ré, JI Huddleston III, NJ Giori, S Delp, NH Shah NPJ digital medicine 2 (1), 94, 2019 | 56 | 2019 |
| 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), 2726, 2022 | 55 | 2022 |
| Assessing the accuracy of automatic speech recognition for psychotherapy AS Miner, A Haque, JA Fries, SL Fleming, DE Wilfley, G Terence Wilson, ... NPJ digital medicine 3 (1), 82, 2020 | 52 | 2020 |
| Estimating the efficacy of symptom-based screening for COVID-19 A Callahan, E Steinberg, JA Fries, S Gombar, B Patel, CK Corbin, ... NPJ digital medicine 3 (1), 95, 2020 | 50 | 2020 |
| Multi-resolution weak supervision for sequential data P Varma, F Sala, S Sagawa, J Fries, D Fu, S Khattar, A Ramamoorthy, ... Advances in Neural Information Processing Systems 32, 2019 | 39 | 2019 |
| Multitask prompted training enables zero-shot task generalization S Victor, W Albert, R Colin, B Stephen, S Lintang, A Zaid, C Antoine, ... International Conference on Learning Representations, 2022 | 38 | 2022 |
| Bigbio: A framework for data-centric biomedical natural language processing J Fries, L Weber, N Seelam, G Altay, D Datta, S Garda, S Kang, R Su, ... Advances in Neural Information Processing Systems 35, 25792-25806, 2022 | 37 | 2022 |
| 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 | 37 | 2021 |
| Shortfuse: Biomedical time series representations in the presence of structured information M Fiterau, S Bhooshan, J Fries, C Bournhonesque, J Hicks, E Halilaj, ... Machine Learning for Healthcare Conference, 59-74, 2017 | 33 | 2017 |
Nigam ShahProfessor of Medicine, and Biomedical Data Science, Stanford University在 stanford.edu 的电子邮件经过验证
