TY - JOUR
T1 - Searching for Cerebrovascular Disease Optimal Treatment Recommendations Applying Partially Observable Markov Decision Processes
AU - Victorio-Meza, Hermilo
AU - Mejía-Lavalle, Manuel
AU - Martínez Rebollar, Alicia
AU - Ortega, Andrés Blanco
AU - Lagunas, Obdulia Pichardo
AU - Sidorov, Grigori
N1 - Publisher Copyright:
© 2018 World Scientific Publishing Company.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - POMDP
KW - Partially observable Markov decision processes
KW - cerebrovascular diseases
UR - http://www.scopus.com/inward/record.url?scp=85030861653&partnerID=8YFLogxK
U2 - 10.1142/S0218001418600157
DO - 10.1142/S0218001418600157
M3 - Artículo
SN - 0218-0014
VL - 32
JO - International Journal of Pattern Recognition and Artificial Intelligence
JF - International Journal of Pattern Recognition and Artificial Intelligence
IS - 1
M1 - 1860015
ER -