Cargando…

Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning

Background: Markov decision process (MDP) models are powerful tools. They enable the derivation of optimal treatment policies but may incur long computational times and generate decision rules that are challenging to interpret by physicians. Methods: In an effort to improve usability and interpretab...

Descripción completa

Detalles Bibliográficos
Autores principales:	Schell, Greggory J., Marrero, Wesley J., Lavieri, Mariel S., Sussman, Jeremy B., Hayward, Rodney A.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	SAGE Publications 2016
Materias:	Original Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124941/ https://www.ncbi.nlm.nih.gov/pubmed/30288409 http://dx.doi.org/10.1177/2381468316674214

Ejemplares similares

Implications of True and Perceived Treatment Burden on Cardiovascular Medication Use
por: Sussman, Jeremy B., et al.
Publicado: (2017)

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
por: Schell, Greggory J, et al.
Publicado: (2013)

Approximating countable Markov chains
por: Freedman, David
Publicado: (1983)

Approximate quantum Markov chains
por: Sutter, David
Publicado: (2018)

Markov operators, positive semigroups and approximation processes
por: Altomare, Francesco, et al.
Publicado: (2015)

Approximating Hidden Gaussian Markov Random Fields
por: Rue, H, et al.
Publicado: (2003)

Universal recovery map for approximate Markov chains
por: Sutter, David, et al.
Publicado: (2016)

Fluctuations in Markov Processes: Time Symmetry and Martingale Approximation
por: Komorowski, Tomasz, et al.
Publicado: (2012)

Approximation methods for piecewise deterministic Markov processes and their costs
por: Kritzer, Peter, et al.
Publicado: (2019)

The Magnitude of the Health and Economic Impact of Increased Organ Donation on Patients With End-Stage Renal Disease
por: Chen, Huey-Fen, et al.
Publicado: (2021)

Asymptotic analyses for complex evolutionary systems with Markov and semi-Markov switching using approximation schemes
por: Chabanyuk, Yaroslav, et al.
Publicado: (2020)

Maximizing the Value of Mobile Health Monitoring by Avoiding Redundant Patient Reports: Prediction of Depression-Related Symptoms and Adherence Problems in Automated Health Assessment Services
por: Piette, John D, et al.
Publicado: (2013)

Black-White Cardiovascular Disease Disparities After Target-Based Versus Personalized Benefit–Based Lipid and Blood Pressure Treatment
por: Basu, Sanjay, et al.
Publicado: (2017)

Approximate Inference for Hierarchical Gaussian Markov Random Fields Models
por: Rue, H, et al.
Publicado: (2005)

Markov decision processes in practice
por: Boucherie, Richard J, et al.
Publicado: (2017)

Risk-Sensitive Markov Decision Processes of USV Trajectory Planning with Time-Limited Budget
por: Ding, Yi, et al.
Publicado: (2023)

Estimated Association Between Organ Availability and Presumed Consent in Solid Organ Transplant
por: DeRoos, Luke J., et al.
Publicado: (2019)

Markov decision processes with their applications /
por: Hu, Qiying
Publicado: (2008)

Competitive Markov decision processes
por: Filar, Jerzy A., 1949-
Publicado: (1997)

Markov decision processes with their applications
por: Hu, Qiying, et al.
Publicado: (2007)

Research on Hybrid Feature Selection Method Based on Iterative Approximation Markov Blanket
por: Huang, Canyi, et al.
Publicado: (2020)

Markov decision processes : discrete stochastic dynamic programming /
por: Puterman, Martin L.
Publicado: (1994)

Markov decision processes with applications to finance
por: Bäuerle, Nicole
Publicado: (2011)

Markov Decision Processes with Applications to Finance
por: Bäuerle, Nicole, et al.
Publicado: (2011)

Markov decision processes in artificial intelligence
por: Sigaud, Olivier, et al.
Publicado: (2013)

Entropic Regularization of Markov Decision Processes
por: Belousov, Boris, et al.
Publicado: (2019)

An IV for the RCT: using instrumental variables to adjust for treatment contamination in randomised controlled trials
por: Sussman, Jeremy B, et al.
Publicado: (2010)

Interpretation and approximation tools for big, dense Markov chain transition matrices in population genetics
por: Reichel, Katja, et al.
Publicado: (2015)

Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior
por: Jia, Xiaoli, et al.
Publicado: (2020)

Continuous-time Markov decision processes : theory and applications /
por: Guo, Xianping
Publicado: (2009)

Simulation-based algorithms for Markov decision processes
por: Chang, Hyeong Soo, et al.
Publicado: (2013)

Handbook of Markov decision processes: methods and applications
por: Feinberg, Eugene A, et al.
Publicado: (2001)

STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning
por: Kappel, David, et al.
Publicado: (2014)

Approximate Decision Tree Induction over Approximately Engineered Data Features
por: Ślęzak, Dominik, et al.
Publicado: (2020)

Neural approximations for optimal control and decision
por: Zoppoli, Riccardo, et al.
Publicado: (2019)

An Approximation Framework for Solvers and Decision Procedures
por: Zeljić, Aleksandar, et al.
Publicado: (2016)

Inference of Kinetic Parameters of Delayed Stochastic Models of Gene Expression Using a Markov Chain Approximation
por: Mannerstrom, Henrik, et al.
Publicado: (2010)

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation
por: Duan, Jinli, et al.
Publicado: (2017)

Markov Decision Processes: Discrete Stochastic Dynamic Programming
por: Puterman, Martin L
Publicado: (2005)

Continuous-time Markov decision processes: theory and applications
por: Guo, Xianping, et al.
Publicado: (2009)

Cannot write session to /tmp/vufind_sessions/sess_bmi2qg8mqgsecp0vfrqhvh5f8n