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). Online databases and gray 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, structured data showed AUC = 0.873, and unstructured 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 the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.

Keywords: meta-analysis; structured data; suicide prediction; suicide prevention; systematic review; unstructured data.

Figure 1

PRISMA diagram of literature search.

Figure 2

Forest plot of sensitivity and CI for 31 studies.

Figure 3

Forest plot of specificity and CI for all 31 studies.

Figure 4

SROC curve for all studies (n = 31).

Figure 5

SROC curve for structured data (n = 15).

Figure 6

SROC curve for unstructured data (n = 16).