| The myth of generalisability in clinical research and machine learning in health care J Futoma, M Simons, T Panch, F Doshi-Velez, LA Celi The Lancet Digital Health 2 (9), e489-e492, 2020 | 360 | 2020 |
| A comparison of models for predicting early hospital readmissions J Futoma, J Morris, J Lucas Journal of biomedical informatics 56, 229-238, 2015 | 357 | 2015 |
| " 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 | 222 | 2020 |
| Learning to detect sepsis with a multitask Gaussian process RNN classifier J Futoma, S Hariharan, K Heller International conference on machine learning, 1174-1182, 2017 | 221 | 2017 |
| 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 | 181 | 2017 |
| 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 | 144 | 2020 |
| 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 | 134 | 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 | 86 | 2020 |
| Model-based reinforcement learning for semi-markov decision processes with neural odes J Du, J Futoma, F Doshi-Velez Advances in Neural Information Processing Systems 33, 19805-19816, 2020 | 71 | 2020 |
| Interpretable off-policy evaluation in reinforcement learning by highlighting influential transitions O Gottesman, J Futoma, Y Liu, S Parbhoo, L Celi, E Brunskill, ... International Conference on Machine Learning, 3658-3667, 2020 | 70 | 2020 |
| Popcorn: Partially observed prediction constrained reinforcement learning J Futoma, MC Hughes, F Doshi-Velez arXiv preprint arXiv:2001.04032, 2020 | 52 | 2020 |
| 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 | 44 | 2016 |
| It’s complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US S Jewell, J Futoma, L Hannah, AC Miller, NJ Foti, EB Fox NPJ digital medicine 4 (1), 152, 2021 | 30 | 2021 |
| Statistical deconvolution for inference of infection time series AC Miller, LA Hannah, J Futoma, NJ Foti, EB Fox, A D’Amour, M Sandler, ... Epidemiology 33 (4), 470-479, 2022 | 25 | 2022 |
| A unifying representation for a class of dependent random measures N Foti, J Futoma, D Rockmore, S Williamson Artificial Intelligence and Statistics, 20-28, 2013 | 20 | 2013 |
| Gaussian process-based models for clinical time series in healthcare J Futoma Duke University, 2018 | 19 | 2018 |
| Generalization in clinical prediction models: the blessing and curse of measurement indicator variables J Futoma, M Simons, F Doshi-Velez, R Kamaleswaran Critical Care Explorations 3 (7), e0453, 2021 | 15 | 2021 |
| Identifying distinct, effective treatments for acute hypotension with SODA-RL: safely optimized diverse accurate reinforcement learning J Futoma, MA Masood, F Doshi-Velez AMIA summits on translational science proceedings 2020, 181, 2020 | 15 | 2020 |
| Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease. J Futoma, MP Sendak, B Cameron, KA Heller UAI, 2016 | 15 | 2016 |
| Learning to treat sepsis with multi-output gaussian process deep recurrent q-networks J Futoma, A Lin, M Sendak, A Bedoya, M Clement, C O'Brien, K Heller | 11 | 2018 |
Mark SendakPopulation Health & Data Science Lead, Duke Institute for Health InnovationPreverjeni e-poštni naslov na duke.edu