Predicting acute suicidal ideation on Instagram using ensemble machine learning models.

ABSTRACT:

Introduction

Online social networking data (SN) is a contextually and temporally rich data stream that has shown promise in the prediction of suicidal thought and behavior. Despite the clear advantages of this digital medium, predictive modeling of acute suicidal ideation (SI) currently remains underdeveloped. SN data, in conjunction with robust machine learning algorithms, may offer a promising way forward.

Methods

We applied an ensemble machine learning model on a previously published dataset of adolescents on Instagram with a prior history of lifetime SI (N = 52) to predict SI within the past month. Using predictors that capture language use and activity within this SN, we evaluated the performance of our out-of-sample, cross-validated model against previous effort

SUBMITTER: Lekkas D 

PROVIDER: S-EPMC8350610 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature