ArticleNPJ digital medicine2020
A machine learning approach predicts future risk to suicidal ideation from social media data.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
58 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic Review.JMIR mental health · 2024Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Social disconnection integrates genetic and proteomic risks in suicidal ideation and depression.Translational psychiatry · 2026Article
- Predicting suicidal ideation in academic communities using machine learning methods: a cross-sectional study.Lancet regional health. Americas · 2026Article
- Digital social contacts predict reduced loneliness in chronic depression: an exploratory longitudinal study.European archives of psychiatry and clinical neuroscience · 2026Observational
- Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.PsyCh journal · 2026Article
- Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach.PloS one · 2026Article
- Digital Detection of Suicidal Ideation: A Scoping Review to Inform Prevention and Psychological Well-Being.Behavioral sciences (Basel, Switzerland) · 2025Review
- Forewarned Is Forearmed: The Single- and Dual-Brain Mechanisms in Detectors from Dyads of Varying Social Distance during Deceptive Outcome Evaluation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- The relationship between social network addiction, online gaming addiction, and suicide ideation: mediating and moderating role of loneliness.BMC psychology · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique.Scientific reports · 2025Article
- Artificial intelligence-assisted psychological intervention mechanisms for university students in the context of new media technologies: an analysis based on data from the national institute of mental health.Frontiers in psychology · 2025Article
- Review
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.Journal of medical Internet research · 2024Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Predicting public mental health needs in a crisis using social media indicators: a Singapore big data study.Scientific reports · 2024Article
- The Association between Suicidal Ideation and Subtypes of Comorbid Insomnia Disorder in Apneic Individuals.Journal of clinical medicine · 2024Article
- Emerging Themes and Research Frontiers in Suicide Prediction: A Scientometric Analysis.Cureus · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from
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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.