Planning treatment of ischemic heart disease with partially observable Markov decision processes

By Milos Hauskrecht, Hamish S. F. Fraser. 2000

196 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Benchmarks for Deep Off-Policy Evaluation (2021, background): “However, this sort of active online interaction is often impractical for real-world problems, where active data collection can be costly (Li et al., 2010), dangerous (Hauskrecht & Fraser, 2000; Kendall et al., 2019), or time consuming (Gu et al., 2017).”
Optimization of anemia treatment in hemodialysis patients via reinforcement learning (2014, unknown): “[6,10] (10,11) [11,12] (12,13) [13,16] 0 0.”
LEAD: a methodology for learning efficient approaches to medical diagnosis (2006, background): “Several papers exist in the literature that exploit MDP to model problems related to medial diagnosis [19]–[21].”
Modeling Medical Treatment Using Markov Decision Processes (2005, methodology): “Treatment of Ischemic Heart Disease Hauskrecht and Fraser applied a POMDP formulation to the problem of treating patients with ischemic heart disease (IHD) [36].”
Reinforcement Learning of POMDPs using Spectral Methods (2016, background): “This is common in applications such as spoken-dialogue systems (Atrash and Pineau, 2006; Png et al., 2012) and medical applications (Hauskrecht and Fraser, 2000).”
DESPOT: Online POMDP Planning with Regularization (2013, background): “It has a wide range of applications ranging from robot control (Roy, Burgard, Fox, & Thrun, 1999), resource management (Chad ` es, Carwardine, Martin, Nicol, Sabbadin, & Buffet, 2012) to medical diagnosis (Hauskrecht & Fraser, 2000).”
From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis (2015, unknown): “A notable example of POMDP adoption in medical decision support is that of Hauskrecht and Fraser [26,27] who modeled Ischemic Heart Disease with a POMDP model that included both treatment and test actions.”
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning (2020, methodology): “POMDPs have been applied to heart disease management (Hauskrecht and Fraser, 2000), sepsis treatment in off-policy or simulated settings (Li et al.”
Modeling Medical Records of Diabetes using Markov Decision Processes (2013, methodology): “The research that directly motivated this study was the work done by Hausklecht and Fraser (Hauskrecht & Fraser, 2000).”