A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction

DOI10.1287/ijoc.2022.1176OpenAlexW4220793930MaRDI QIDQ5106404

Robert L. Davis, Rishikesan Kamaleswaran, Zeyu Liu, Akram S. Mohammed, Anahita Khojandi, Xue-Ping Li

Publication date: 19 September 2022

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/ijoc.2022.1176

zbMATH Keywords

hierarchical modelingmedical decision makingpartially observable Markov decision processessepsisreal-time predictive analytics

Mathematics Subject Classification ID

Mathematical programming (90Cxx)

Uses Software

• Scikit
• ElemStatLearn
• Python
• Adam

Cites Work

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• Python
• Partially Observed Markov Decision Processes
• A Hybrid Genetic/Optimization Algorithm for Finite-Horizon, Partially Observed Markov Decision Processes
• Testing the Nearest Kronecker Product Preconditioner on Markov Chains and Stochastic Automata Networks
• Exploiting the Structural Properties of the Underlying Markov Decision Problem in the Q-Learning Algorithm
• Maintaining Secure and Reliable Distributed Control Systems
• The optimal value of markov stopping problems with one-step look ahead policy
• Bayesian Sequential Detection With Phase-Distributed Change Time and Nonlinear Penalty—A POMDP Lattice Programming Approach
• Random forests

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