| Analyzing the role of model uncertainty for electronic health records MW Dusenberry, D Tran, E Choi, J Kemp, J Nixon, G Jerfel, K Heller, ... Proceedings of the ACM Conference on Health, Inference, and Learning, 204-213, 2020 | 104 | 2020 |
| Learning an adaptive learning rate schedule Z Xu, AM Dai, J Kemp, L Metz arXiv preprint arXiv:1909.09712, 2019 | 62 | 2019 |
| Modelled effects of prawn aquaculture on poverty alleviation and schistosomiasis control CM Hoover, SH Sokolow, J Kemp, JN Sanchirico, AJ Lund, IJ Jones, ... Nature Sustainability 2 (7), 611-620, 2019 | 37 | 2019 |
| Improved hierarchical patient classification with language model pretraining over clinical notes J Kemp, A Rajkomar, AM Dai arXiv preprint arXiv:1909.03039, 2019 | 18* | 2019 |
| Deciphering clinical abbreviations with a privacy protecting machine learning system A Rajkomar, E Loreaux, Y Liu, J Kemp, B Li, MJ Chen, Y Zhang, ... Nature Communications 13 (1), 7456, 2022 | 9 | 2022 |
| User-centred design for machine learning in health care: a case study from care management MG Seneviratne, RC Li, M Schreier, D Lopez-Martinez, BS Patel, ... BMJ Health & Care Informatics 29 (1), 2022 | 9 | 2022 |
| Instability in clinical risk stratification models using deep learning D Lopez-Martinez, A Yakubovich, M Seneviratne, AD Lelkes, A Tyagi, ... Machine Learning for Health, 552-565, 2022 | 1 | 2022 |
| Boosting the interpretability of clinical risk scores with intervention predictions E Loreaux, K Yu, J Kemp, M Seneviratne, C Chen, S Roy, I Protsyuk, ... arXiv preprint arXiv:2207.02941, 2022 | 1 | 2022 |
