ArticleScientific reports2024
Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Ensemble transformer with post-hoc explanations for depression emotion and severity detection.iScience · 2026Article
- Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers.Global epidemiology · 2025Article
- An interpretable machine learning model predicts the interactive and cumulative risks of different environmental chemical exposures on depression.Translational psychiatry · 2025Article
- Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques.BMC psychiatry · 2025Article
- An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study.JMIR public health and surveillance · 2025Article
- Explainable AI for suicide risk detection: gender- and age-specific patterns from real-time crisis chats.Frontiers in medicine · 2025Article
- The Association between Suicidal Ideation and Subtypes of Comorbid Insomnia Disorder in Apneic Individuals.Journal of clinical medicine · 2024Article
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Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.