Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.

ABSTRACT: Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records)

SUBMITTER: Hopkins D 

PROVIDER: S-EPMC9378826 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature