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Related Experiment Video

Updated: May 8, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study.

Orlando Fernandes1, Liana Catarina Lima Portugal2, Priscila Maria de Oliveira da Fonseca2

  • 1Laboratório de Neurofisiologia do Comportamento, Departamento de Fisiologia e Farmacologia, Instituto Biomédico, Universidade Federal Fluminense, Rua Hernani Pires de Mello, 101, Niterói, Brazil.

Lancet Regional Health. Americas
|May 7, 2026
PubMed
Summary

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This summary is machine-generated.

Suicidal ideation (SI) can occur without depression, influenced by factors like optimism and loneliness. Screening should consider these beyond depressive symptoms, especially in academic settings.

Area of Science:

  • Psychiatry and Mental Health
  • Academic Well-being
  • Machine Learning in Healthcare

Background:

  • Suicidal ideation (SI) frequently co-occurs with depression, but can also arise independently.
  • Factors beyond depressive symptoms may contribute to SI, particularly in academic populations.
  • Understanding these additional vulnerability and protective factors is crucial for effective intervention.

Purpose of the Study:

  • To investigate protective and vulnerability factors associated with SI beyond depressive symptomatology.
  • To develop a predictive model distinguishing individuals with and without SI using machine learning.
  • To identify key contributing factors for targeted mental health strategies in academic communities.

Main Methods:

  • Employed multiple kernel learning (MKL) to analyze a large-scale online survey (N=3828) of the Brazilian academic community.
Keywords:
Academic communityChildhood emotional maltreatmentDepressionLonelinessMachine learningOptimismSuicidal ideation

Related Experiment Videos

Last Updated: May 8, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

  • Incorporated measures of depressive symptoms, optimism, loneliness, childhood maltreatment, and demographics into the MKL model.
  • Utilized MKL to assess the contribution of each psychometric instrument and item to SI prediction.

    • Main Results:

    • The MKL model achieved high accuracy in distinguishing individuals with and without SI (balanced accuracy 77.61%, AUC 0.862).
    • Depressive symptoms were significant predictors, but optimism, loneliness, childhood emotional maltreatment, and demographics collectively accounted for 50% of the model's predictive weight.
    • This highlights the multifactorial nature of SI in this population.

    Conclusions:

    • SI screening protocols should extend beyond depressive symptoms to include a broader range of emotional and behavioral factors.
    • Findings can inform the development of targeted interventions to enhance mental well-being within academic settings.
    • Recognizing diverse contributing factors is essential for comprehensive mental health support in academia.