Virtually screening adults for depression, anxiety, and suicide risk using machine learning and language from an open-ended interview.

ABSTRACT:

Background

Current depression, anxiety, and suicide screening techniques rely on retrospective patient reported symptoms to standardized scales. A qualitative approach to screening combined with the innovation of natural language processing (NLP) and machine learning (ML) methods have shown promise to enhance person-centeredness while detecting depression, anxiety, and suicide risk from in-the-moment patient language derived from an open-ended brief interview.

Objective

To evaluate the performance of NLP/ML models to identify depression, anxiety, and suicide risk from a single 5-10-min semi-structured interview with a large, national sample.

Method

Two thousand four hundred sixteen interviews were conducted with 1,433 participants over a teleconference platform, with

SUBMITTER: Wright-Berryman J 

PROVIDER: S-EPMC10291825 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature