activity
20182022
most citedEvaluating Reinforcement Learning Algorithms in Observational Health Settings

83 citations · 152 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2022

Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes

Adam Dejl, Harsh Deep, Jonathan Fei +2

Sum-product networks (SPNs) have recently emerged as a novel deep learning architecture enabling highly efficient probabilistic inference. Since their introduction, SPNs have been…

eess.SP2021

Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification

Ziyu Jia, Youfang Lin, Jing Wang +5

Sleep stage classification is essential for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performanc…

cs.LG2020★ 10 cited

Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients

MingYu Lu, Zachary Shahn, Daby Sow +2

The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate t…

cs.LG2020★ 5 cited

G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes

Rui Li, Zach Shahn, Jun Li +5

Counterfactual prediction is a fundamental task in decision-making. G-computation is a method for estimating expected counterfactual outcomes under dynamic time-varying treatment s…

cs.LG2019★ 45 cited

Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning

Xuefeng Peng, Yi Ding, David Wihl +6

Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the indi…

cs.LG2018★ 9 cited

Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability

Uma M. Girkar, Ryo Uchimido, Li-wei H. Lehman +3

Determining whether hypotensive patients in intensive care units (ICUs) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physician…

cs.LG2018★ 83 cited

Evaluating Reinforcement Learning Algorithms in Observational Health Settings

Omer Gottesman, Fredrik Johansson, Joshua Meier +16

Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare. Reinforcement learning (RL)…