Applying Adaptive Bayesian Learning Model in the Fastest Change Detection of Mental Health Patients’ Category

In customized healtchare systems, the service providers tailor the health services based on the different types of patients. However, the actual type of a patient is hidden from the decision-maker. The observed signals form a partial picture that is an estimate of the actual state of the subject. The decision maker’s knowledge about the actual type of the patients can play an essential role in the performance of the tailored treatment and the patient’s satisfaction. We propose a stochastic control system to model the situation. We consider the joint monitoing and learning system in which all parameters are unknown. The objective is to detect a random change in the patient’s state while simultaneously learning about the system properties and parameters. The partner will benefit from participating in this project because they will be able to improve the participants satisfaction by implementing new algorithms and comparing it to benchmark results.

Faculty Supervisor:

Jue Wang

Student:

Partner:

OPTT

Discipline:

Business

Sector:

Health and Related Sciences & Technology; Information and cultural industries

University:

Queen's University

Program: