Search Results for author:
Found 11 papers, 3 papers with code
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
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.
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
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
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs).
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection
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.
Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression
Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management.
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks
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\%.
"The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning
Our work underscores the limits of model interpretability as a solution to ensure transparency, accuracy, and accountability in practice.
Identifying Distinct, Effective Treatments for Acute Hypotension with SODA-RL: Safely Optimized Diverse Accurate Reinforcement Learning
Hypotension in critical care settings is a life-threatening emergency that must be recognized and treated early.
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
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.
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance
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.
Label Shift Estimators for Non-Ignorable Missing Data
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 .
