Individualized post-crisis monitoring of psychiatric patients via Hidden Markov models.

ABSTRACT:

Introduction

Individuals in the midst of a mental health crisis frequently exhibit instability and face an elevated risk of recurring crises in the subsequent weeks, which underscores the importance of timely intervention in mental healthcare. This work presents a data-driven method to infer the mental state of a patient during the weeks following a mental health crisis by leveraging their historical data. Additionally, we propose a policy that determines the necessary duration for closely monitoring a patient after a mental health crisis before considering them stable.

Methods

We model the patient's mental state as a Hidden Markov Process, partially observed through mental health crisis events. We introduce a closed-form solution that leverages the model parameters to optim

SUBMITTER: Garriga R 

PROVIDER: S-EPMC10869627 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature