Artif Intell Med - Scalable approximate policies for Markov decision process models of hospital elective admissions.

Tópicos

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Resumo

JECTIVE: To demonstrate the feasibility of using stochastic simulation methods for the solution of a large-scale Markov decision process model of on-line patient admissions scheduling.METHODS: The problem of admissions scheduling is modeled as a Markov decision process in which the states represent numbers of patients using each of a number of resources. We investigate current state-of-the-art real time planning methods to compute solutions to this Markov decision process. Due to the complexity of the model, traditional model-based planners are limited in scalability since they require an explicit enumeration of the model dynamics. To overcome this challenge, we apply sample-based planners along with efficient simulation techniques that given an initial start state, generate an action on-demand while avoiding portions of the model that are irrelevant to the start state. We also propose a novel variant of a popular sample-based planner that is particularly well suited to the elective admissions problem.RESULTS: Results show that the stochastic simulation methods allow for the problem size to be scaled by a factor of almost 10 in the action space, and exponentially in the state space. We have demonstrated our approach on a problem with 81 actions, four specialities and four treatment patterns, and shown that we can generate solutions that are near-optimal in about 100s.CONCLUSION: Sample-based planners are a viable alternative to state-based planners for large Markov decision process models of elective admissions scheduling.

Resumo Limpo

jectiv demonstr feasibl use stochast simul method solut largescal markov decis process model onlin patient admiss schedulingmethod problem admiss schedul model markov decis process state repres number patient use number resourc investig current stateoftheart real time plan method comput solut markov decis process due complex model tradit modelbas planner limit scalabl sinc requir explicit enumer model dynam overcom challeng appli samplebas planner along effici simul techniqu given initi start state generat action ondemand avoid portion model irrelev start state also propos novel variant popular samplebas planner particular well suit elect admiss problemresult result show stochast simul method allow problem size scale factor almost action space exponenti state space demonstr approach problem action four special four treatment pattern shown can generat solut nearoptim sconclus samplebas planner viabl altern statebas planner larg markov decis process model elect admiss schedul

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