83 citations · 152 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…
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)…