Joseph Futoma
Title
Cited by
Cited by
Year
A comparison of models for predicting early hospital readmissions
J Futoma, J Morris, J Lucas
Journal of biomedical informatics 56, 229-238, 2015
2462015
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
1082017
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
1012017
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
542020
" 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
502020
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
352020
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
222016
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
212013
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
212013
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
182020
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
172020
Popcorn: Partially observed prediction constrained reinforcement learning
J Futoma, MC Hughes, F Doshi-Velez
arXiv preprint arXiv:2001.04032, 2020
162020
Model-based reinforcement learning for semi-markov decision processes with neural odes
J Du, J Futoma, F Doshi-Velez
arXiv preprint arXiv:2006.16210, 2020
142020
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
122020
Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease.
J Futoma, M Sendak, B Cameron, KA Heller
UAI, 2016
112016
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
62018
Learning to treat sepsis with multi-output gaussian process deep recurrent q-networks, 2018
J Futoma, A Lin, M Sendak, A Bedoya, M Clement, C O’Brien, K Heller
URL https://openreview. net/forum, 0
6
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
52020
Sepsis watch: A real-world integration of deep learning into routine clinical care
MP Sendak, W Ratliff, D Sarro, E Alderton, J Futoma, M Gao, M Nichols, ...
JMIR Preprints 15182, 2019
52019
Gaussian process-based models for clinical time series in healthcare
J Futoma
Duke University, 2018
52018
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Articles 1–20