Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study.

ABSTRACT:

Background

Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression.

Objective

The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression.

Methods

Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were colle

SUBMITTER: Opoku Asare K 

PROVIDER: S-EPMC8314163 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature