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Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

Jun 29, 2020
Jianzhun Du, Joseph Futoma, Finale Doshi-Velez

* 20 pages, 7 figures 

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Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions

Feb 14, 2020
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez

* Change: Correction of typo in meta-data author names 

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POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning

Jan 13, 2020
Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez

* Accepted, to appear at AISTATS 2020, Palermo. Note that this version is not the final camera-ready; that will appear in a few weeks 

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Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning

Jan 09, 2020
Joseph Futoma, Muhammad A. Masood, Finale Doshi-Velez

* Accepted for publication at the AMIA 2020 Informatics Summit. This version contains an updated appendix with additional figures not found in the page-constrained AMIA version, so treat this version as the most up-to-date 

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"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning

Dec 07, 2019
Mark Sendak, Madeleine Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, Cara O'Brien

* To appear at ACM FAT* 2020, Barcelona. Updated to camera-ready version 

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An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection

Aug 19, 2017
Joseph Futoma, Sanjay Hariharan, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, Cara O'Brien, Katherine Heller

* Presented at Machine Learning for Healthcare 2017, Boston, MA 

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Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier

Jun 13, 2017
Joseph Futoma, Sanjay Hariharan, Katherine Heller

* Presented at 34th International Conference on Machine Learning (ICML 2017), Sydney, Australia 

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Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression

Aug 16, 2016
Joseph Futoma, Mark Sendak, C. Blake Cameron, Katherine Heller

* Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, CA 

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