The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review - Somé, Nibene H.; Noormohammadpour, Pardis; Lange, Shannon (Frontiers in Psychiatry, 2024)
Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature. Our study aimed to identify studies that applied machine learning techniques to administrative and survey data and enumerate the important risk factors of suicidal thoughts and behaviors identified. A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. Mean area under the receiver operating characteristic curve (AUC) values with 95% confidence intervals were computed for each suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide) by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed. The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study. The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data.