Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach.

ABSTRACT:

Background

Bipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days.

Objective

This study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial.

Methods

We applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual

SUBMITTER: Busk J 

PROVIDER: S-EPMC7367518 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature