| 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 | 1705 | 2023 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research 23 (226), 1-61, 2022 | 753 | 2022 |
| Advances in electronic phenotyping: from rule-based definitions to machine learning models JM Banda, M Seneviratne, T Hernandez-Boussard, NH Shah Annual review of biomedical data science 1 (1), 53-68, 2018 | 216 | 2018 |
| Bridging the implementation gap of machine learning in healthcare MG Seneviratne, NH Shah, L Chu Bmj Innovations 6 (2), 2020 | 125 | 2020 |
| Subfertile female androgen receptor knockout mice exhibit defects in neuroendocrine signaling, intraovarian function, and uterine development but not uterine function KA Walters, KJ McTavish, MG Seneviratne, M Jimenez, AC McMahon, ... Endocrinology 150 (7), 3274-3282, 2009 | 114 | 2009 |
| Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records N Tomašev, N Harris, S Baur, A Mottram, X Glorot, JW Rae, M Zielinski, ... Nature Protocols 16 (6), 2765-2787, 2021 | 74 | 2021 |
| Predicting the incidence of pressure ulcers in the intensive care unit using machine learning EM Cramer, MG Seneviratne, H Sharifi, A Ozturk, T Hernandez-Boussard eGEMs 7 (1), 2019 | 70 | 2019 |
| Reporting of demographic data and representativeness in machine learning models using electronic health records S Bozkurt, EM Cahan, MG Seneviratne, R Sun, JA Lossio-Ventura, ... Journal of the American Medical Informatics Association 27 (12), 1878-1884, 2020 | 42 | 2020 |
| Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment I Banerjee, K Li, M Seneviratne, M Ferrari, T Seto, JD Brooks, DL Rubin, ... JAMIA open 2 (1), 150-159, 2019 | 41 | 2019 |
| Architecture and implementation of a clinical research data warehouse for prostate cancer MG Seneviratne, T Seto, DW Blayney, JD Brooks, T Hernandez-Boussard eGEMs 6 (1), 2018 | 40 | 2018 |
| Prevalence, distribution and clinical correlates of myocardial fibrosis in systemic lupus erythematosus: a cardiac magnetic resonance study MG Seneviratne, SM Grieve, GA Figtree, R Garsia, DS Celermajer, ... Lupus 25 (6), 573-581, 2016 | 35 | 2016 |
| Effects of rotation interval on sleepiness and circadian dynamics on forward rotating 3-shift systems S Postnova, DD Postnov, M Seneviratne, PA Robinson Journal of biological rhythms 29 (1), 60-70, 2014 | 30 | 2014 |
| Concept-based model explanations for electronic health records D Mincu, E Loreaux, S Hou, S Baur, I Protsyuk, M Seneviratne, A Mottram, ... Proceedings of the Conference on Health, Inference, and Learning, 36-46, 2021 | 28 | 2021 |
| Distribution of global health measures from routinely collected PROMIS surveys in patients with breast cancer or prostate cancer MG Seneviratne, S Bozkurt, MI Patel, T Seto, JD Brooks, DW Blayney, ... Cancer 125 (6), 943-951, 2019 | 24 | 2019 |
| Merging heterogeneous clinical data to enable knowledge discovery MG Seneviratne, MG Kahn, T Hernandez-Boussard BIOCOMPUTING 2019: Proceedings of the pacific symposium, 439-443, 2018 | 23 | 2018 |
| Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network M Kashyap, M Seneviratne, JM Banda, T Falconer, B Ryu, S Yoo, ... Journal of the American Medical Informatics Association 27 (6), 877-883, 2020 | 22 | 2020 |
| Identifying cases of metastatic prostate cancer using machine learning on electronic health records MG Seneviratne, JM Banda, JD Brooks, NH Shah, ... AMIA Annual Symposium Proceedings 2018, 1498, 2018 | 22 | 2018 |
| Underspecification presents challenges for credibility in modern machine learning, 2020 A D’Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... arXiv preprint arXiv:2011.03395, 2011 | 17 | 2011 |
| Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing S Roy, D Mincu, E Loreaux, A Mottram, I Protsyuk, N Harris, Y Xue, ... Journal of the American Medical Informatics Association 28 (9), 1936-1946, 2021 | 16 | 2021 |
| Machine learning approaches for extracting stage from pathology reports in prostate cancer R Lenain, MG Seneviratne, S Bozkurt, DW Blayney, JD Brooks, ... MEDINFO 2019: Health and Wellbeing e-Networks for All, 1522-1523, 2019 | 16 | 2019 |
