Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.

ABSTRACT:

Background

Previous efforts to apply machine learning-based natural language processing to longitudinally collected social media data have shown promise in predicting suicide risk.

Objective

Our primary objective was to externally validate our previous machine learning algorithm, the Suicide Artificial Intelligence Prediction Heuristic (SAIPH), against external survey data in 2 independent cohorts. A second objective was to evaluate the efficacy of SAIPH as an indicator of changing suicidal ideation (SI) over time. The tertiary objective was to use SAIPH to evaluate factors important for improving or worsening suicidal trajectory on social media following suicidal mention.

Methods

Twitter (subsequently rebranded as X) timeline data from a student survey cohort and COV

SUBMITTER: Kaminsky Z 

PROVIDER: S-EPMC11659700 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature