ArticleInternet interventions2021
Predicting acute suicidal ideation on Instagram using ensemble machine learning models.
Article in Internet interventions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Characterizing Family Abuse in Suicidal Ideation Posts on Reddit: Large Language Model-Assisted Content Analysis.JMIR infodemiology · 2026Article
- A Dynamic Architecture Linking Autonomic Activity to Emotional Dimensions: Real-Time Estimation From Photoplethysmography.Psychophysiology · 2026Article
- Acute suicidal ideation in context: highlighting sentiment-based markers through the diary entries of a clinically depressed sample.BMC psychiatry · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors.Npj mental health research · 2024Article
- Detecting suicide risk among U.S. servicemembers and veterans: a deep learning approach using social media data.Psychological medicine · 2024Article
- Building a Natural Language Processing Artificial Intelligence to Predict Suicide-Related Events Based on Patient Portal Message Data.Mayo Clinic proceedings. Digital health · 2023Article
- Ecological Momentary Assessment and Machine Learning for Predicting Suicidal Ideation Among Sexual and Gender Minority Individuals.JAMA network open · 2023Article
- Linguistic correlates of suicidal ideation in youth at clinical high-risk for psychosis.Schizophrenia research · 2023Article
- Social Media is Addictive and Influences Behavior: Should it Be Regulated as a Digital Therapeutic?Journal of medical Internet research · 2023Article
- Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention.Journal of affective disorders · 2023Article
- Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk.Frontiers in psychiatry · 2023Article
- Accelerating the impact of artificial intelligence in mental healthcare through implementation science.Implementation research and practiceArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
introductionOnline 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.
methodsWe applied an ensemble machine learning model on a previously published dataset of adolescents on Instagram with a prior history of lifetime SI (
resultsLinguistic and SN data predicted acute SI with an accuracy of 0.702 (sensitivity = 0.769, specificity = 0.654, AUC = 0.775). Model introspection showed a higher proportion of SN-derived predictors with substantial impact on prediction compared with linguistic predictors from structured interviews. Further analysis of subject-specific predictor importance uncovered potentially informative trends for future acute SI risk prediction.
conclusionApplication of ensemble learning methodologies to SN data for the prediction of acute SI may mitigate the complexities and modeling challenges of SI that exist within these time scales. Future work is needed on larger, more heterogeneous populations to fine-tune digital biomarkers and more robustly test external validity.
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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.