no code implementations • 27 Oct 2023 • Andrew C. Miller, Joseph Futoma
We consider the problem of estimating the mean of a random variable Y subject to non-ignorable missingness, i. e., where the missingness mechanism depends on Y .
no code implementations • 25 Apr 2021 • Andrew C. Miller, Leon A. Gatys, Joseph Futoma, Emily B. Fox
We propose using an evaluation model $-$ a model that describes the conditional distribution of the predictive model score $-$ to form model-based metric (MBM) estimates.
1 code implementation • NeurIPS 2020 • Jianzhun Du, Joseph Futoma, Finale Doshi-Velez
We present two elegant solutions for modeling continuous-time dynamics, in a novel model-based reinforcement learning (RL) framework for semi-Markov decision processes (SMDPs), using neural ordinary differential equations (ODEs).
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • ICML 2020 • Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity.
1 code implementation • 13 Jan 2020 • Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez
Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs).
no code implementations • 9 Jan 2020 • Joseph Futoma, Muhammad A. Masood, Finale Doshi-Velez
Hypotension in critical care settings is a life-threatening emergency that must be recognized and treated early.
no code implementations • 19 Nov 2019 • Mark Sendak, Madeleine Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, Cara O'Brien
Our work underscores the limits of model interpretability as a solution to ensure transparency, accuracy, and accountability in practice.
no code implementations • ICLR 2018 • Joseph Futoma, Anthony Lin, Mark Sendak, Armando Bedoya, Meredith Clement, Cara O'Brien, Katherine Heller
We evaluate our approach on a heterogeneous dataset of septic spanning 15 months from our university health system, and find that our learned policy could reduce patient mortality by as much as 8. 2\% from an overall baseline mortality rate of 13. 3\%.
no code implementations • 19 Aug 2017 • Joseph Futoma, Sanjay Hariharan, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, Cara O'Brien, Katherine Heller
Latent function values from the Gaussian process are then fed into a deep recurrent neural network to classify patient encounters as septic or not, and the overall model is trained end-to-end using back-propagation.
2 code implementations • ICML 2017 • Joseph Futoma, Sanjay Hariharan, Katherine Heller
We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity.
no code implementations • 16 Aug 2016 • Joseph Futoma, Mark Sendak, C. Blake Cameron, Katherine Heller
Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management.