AUTHOR=Hopkins Danielle , Rickwood Debra J. , Hallford David J. , Watsford Clare 

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

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 4 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2022.945006

DOI=10.3389/fdgth.2022.945006

ISSN=2673-253X

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 small data (human interpretable such as psychometric instruments) and big data (only machine interpretable such as Electronic Health Records). Online databases and grey literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC=0.860, small data showed AUC=0.873 and big data was calculated at AUC=0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in prediction of suicide risk behaviour overall. Small data and big data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.