Citation

Grendas LN, Chiapella L, Rodante DE, Daray FM. J. Psychiatr. Res. 2021; 145: 85-91.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.jpsychires.2021.11.029

PMID

34883411

Abstract

BACKGROUND: Despite considerable research efforts during the last five decades, the prediction of suicidal behaviour (SB) using traditional model-based statistical has been weak. This marks the need to explore new statistical methods.

OBJECTIVE: To compare the performance of Cox regression models versus Random Survival Forest (RSF) to predict SB.

METHODS: Using a data set of more than 300 high-risk suicidal patients from a multicenter prospective cohort study, we compare Cox regression models with RSF to address predictors of time to suicide reattempt. Cross-validation was used to assess model prediction performance, including the area under the receiver operator curve (AUC), precision, Integrated Brier Score (IBS), sensitivity, and specificity.

RESULTS: A variant of the RSF denominated the RSFElimin, in which irrelevant predictor variables were eliminated from the model, presented the best accuracy, sensitivity, AUC and IBS. At the same time, the sensitivity of this method was slightly lower than that obtained with the Cox regression model with all predictor variables (CoxComp).

CONCLUSION: The RSF, a machine learning model, seems more sensitive and precise than the traditional Cox regression model in predicting suicidal behaviour.

Language: en

Keywords

Machine learning; Prediction; Random survival forest; Suicidal behaviour