Searching for Cerebrovascular Disease Optimal Treatment Recommendations Applying Partially Observable Markov Decision Processes

Hermilo Victorio-Meza, Manuel Mejía-Lavalle, Alicia Martínez Rebollar, Andrés Blanco Ortega, Obdulia Pichardo Lagunas, Grigori Sidorov

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Partially observable Markov decision processes (POMDPs) are mathematical models for the planning of action sequences under conditions of uncertainty. Uncertainty in POMDPs is manifested in two ways: uncertainty in the perception of model states and uncertainty in the effects of actions on states. The diagnosis and treatment of cerebral vascular diseases (CVD) present this double condition of uncertainty, so we think that POMDP is the most suitable method to model them. In this paper, we propose a model of CVD that is based on observations obtained from neuroimaging studies such as computed tomography, magnetic resonance and ultrasound. The model is designed as a POMDP because the health status of the patient is not directly observable, and only can be deduced, with some probability, from the observations in the cerebral images. The components of the model (states, observations, actions, etc.) were defined based on specialized literature. A diagnosis of the patient's health status is made and the most appropriate action for the recovery of health is recommended after introducing the observations when operating the model. Consultation of the probable state of health of the patient and alternative actions is also allowed.

Original languageEnglish
Article number1860015
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume32
Issue number1
DOIs
StatePublished - 1 Jan 2018

Keywords

  • POMDP
  • Partially observable Markov decision processes
  • cerebrovascular diseases

Fingerprint

Dive into the research topics of 'Searching for Cerebrovascular Disease Optimal Treatment Recommendations Applying Partially Observable Markov Decision Processes'. Together they form a unique fingerprint.

Cite this