| Do no harm: a roadmap for responsible machine learning for health care J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, ... Nature medicine 25 (9), 1337-1340, 2019 | 1150 | 2019 |
| " The human body is a black box" supporting clinical decision-making with deep learning M Sendak, MC Elish, M Gao, J Futoma, W Ratliff, M Nichols, A Bedoya, ... Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 277 | 2020 |
| A Path for Translation of Machine Learning Products into Healthcare Delivery MP Sendak, J D’Arcy, S Kashyap, M Gao, M Nichols, K Corey, W Ratliff, ... EMJ Innovations, 2020 | 246 | 2020 |
| Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study MP Sendak, W Ratliff, D Sarro, E Alderton, J Futoma, M Gao, M Nichols, ... JMIR medical informatics 8 (7), e15182, 2020 | 233 | 2020 |
| Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a … KM Corey, S Kashyap, E Lorenzi, SA Lagoo-Deenadayalan, K Heller, ... PLoS medicine 15 (11), e1002701, 2018 | 223 | 2018 |
| Presenting machine learning model information to clinical end users with model facts labels MP Sendak, M Gao, N Brajer, S Balu NPJ digital medicine 3 (1), 41, 2020 | 215 | 2020 |
| An improved multi-output gaussian process rnn with real-time validation for early sepsis detection J Futoma, S Hariharan, K Heller, M Sendak, N Brajer, M Clement, ... Machine learning for healthcare conference, 243-254, 2017 | 192 | 2017 |
| Prospective and external evaluation of a machine learning model to predict in-hospital mortality of adults at time of admission N Brajer, B Cozzi, M Gao, M Nichols, M Revoir, S Balu, J Futoma, J Bae, ... JAMA network open 3 (2), e1920733-e1920733, 2020 | 163 | 2020 |
| Integrating a machine learning system into clinical workflows: qualitative study S Sandhu, AL Lin, N Brajer, J Sperling, W Ratliff, AD Bedoya, S Balu, ... Journal of Medical Internet Research 22 (11), e22421, 2020 | 155 | 2020 |
| Machine learning for early detection of sepsis: an internal and temporal validation study AD Bedoya, J Futoma, ME Clement, K Corey, N Brajer, A Lin, MG Simons, ... JAMIA open 3 (2), 252-260, 2020 | 118 | 2020 |
| Meeting the moment: addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis J Adler-Milstein, N Aggarwal, M Ahmed, J Castner, BJ Evans, ... NAM perspectives 2022, 10.31478/202209c, 2022 | 109 | 2022 |
| Advancing artificial intelligence in health settings outside the hospital and clinic N Aggarwal, M Ahmed, S Basu, JJ Curtin, BJ Evans, ME Matheny, ... NAM perspectives 2020, 10.31478/202011f, 2020 | 88 | 2020 |
| Machine learning in health care: a critical appraisal of challenges and opportunities M Sendak, M Gao, M Nichols, A Lin, S Balu EGEMs 7 (1), 1, 2019 | 78 | 2019 |
| Organizational governance of emerging technologies: AI adoption in healthcare JY Kim, W Boag, F Gulamali, A Hasan, HDJ Hogg, M Lifson, D Mulligan, ... proceedings of the 2023 ACM conference on fairness, accountability, and …, 2023 | 54 | 2023 |
| Barriers to achieving economies of scale in analysis of EHR data MP Sendak, S Balu, KA Schulman Applied clinical informatics 8 (03), 826-831, 2017 | 54 | 2017 |
| Predicting disease progression with a model for multivariate longitudinal clinical data J Futoma, M Sendak, B Cameron, K Heller Machine Learning for Healthcare Conference, 42-54, 2016 | 51 | 2016 |
| Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions … JY Kim, A Hasan, KC Kellogg, W Ratliff, SG Murray, H Suresh, ... PLOS Digital Health 3 (5), e0000390, 2024 | 50 | 2024 |
| Advancing primary care with artificial intelligence and machine learning Z Yang, C Silcox, M Sendak, S Rose, D Rehkopf, R Phillips, L Peterson, ... Healthcare 10 (1), 100594, 2022 | 47 | 2022 |
| Development and validation of machine learning models to predict admission from emergency department to inpatient and intensive care units A Fenn, C Davis, DM Buckland, N Kapadia, M Nichols, M Gao, ... Annals of emergency medicine 78 (2), 290-302, 2021 | 38 | 2021 |
| AI on the Front Lines KC Kellogg, M Sendak, S Balu MIT Sloan Management Review 63 (4), 2022 | 35 | 2022 |
Michael GaoDuke UniversityVerified email at duke.edu
