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@article{Skandari:2021:10.1287/opre.2020.2011,
author = {Skandari, MR and Shechter, SM},
doi = {10.1287/opre.2020.2011},
journal = {Operations Research},
pages = {574--598},
title = {Patient-Type Bayes-Adaptive Treatment Plans},
url = {http://dx.doi.org/10.1287/opre.2020.2011},
volume = {69},
year = {2021}
}

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TY  - JOUR
AB  - <jats:p> Treatment decisions that explicitly consider patient heterogeneity can lower the cost of care and improve outcomes by providing the right care for the right patient at the right time. “Patient-Type Bayes-Adaptive Treatment Plans” analyzes the problem of designing ongoing treatment plans for a population with heterogeneity in disease progression and response to medical interventions. The authors create a model that learns the patient type by monitoring patient health over time and updates a patient's treatment plan according to the information gathered. The authors formulate the problem as a multivariate state space partially observable Markov decision process (POMDP). They provide structural properties of the optimal policy and develop several approximate policies and heuristics to solve the problem. As a case study, they develop a data-driven decision-analytic model to study the optimal timing of vascular access surgery for patients with progressive chronic kidney disease. They provide further policy insights that sharpen existing guidelines. </jats:p>
AU  - Skandari,MR
AU  - Shechter,SM
DO  - 10.1287/opre.2020.2011
EP  - 598
PY  - 2021///
SN  - 0030-364X
SP  - 574
TI  - Patient-Type Bayes-Adaptive Treatment Plans
T2  - Operations Research
UR  - http://dx.doi.org/10.1287/opre.2020.2011
UR  - http://hdl.handle.net/10044/1/77999
VL  - 69
ER  -

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Dr M. Reza Skandari

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