ArticleJournal of personalized medicine2022
The Effectiveness of Predicting Suicidal Ideation through Depressive Symptoms and Social Isolation Using Machine Learning Techniques.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.Annals of general psychiatry · 2026Article
- Predicting Suicidal Ideation in Community-based Older Adults Using Self-report Questionnaires with Machine Learning.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025Article
- Subgroups of suicidal ideation and simulated intervention responses among left-behind children with depression risk: an Ising computational network model.BMC psychiatry · 2025Article
- Article
- Explainable machine learning models predicting the risk of social isolation in older adults: a prospective cohort study.BMC public health · 2025Article
- Assessment and analysis of factors influencing suicidal ideation in young adults: a large cohort study using an elastic network logistic regression model.BMC psychiatry · 2025Article
- Suicide in rural agrarian culture: revealing the micro dimensions of suicidal behavior in Gunungkidul Regency, Indonesia.Frontiers in sociology · 2025Article
- Exploring the Role of First-Person Singular Pronouns in Detecting Suicidal Ideation: A Machine Learning Analysis of Clinical Transcripts.Behavioral sciences (Basel, Switzerland) · 2024Article
- Target-based fusion using social determinants of health to enhance suicide prediction with electronic health records.PloS one · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
(1) Background: Social isolation is a major risk factor for suicidal ideation. In this study, we investigated whether the evaluation of both depression and social isolation in combination could effectively predict suicidal ideation; (2) Methods: A total of 7994 data collected from community residents were analyzed. Statistical analysis was performed using age, the Patient Health Questionnaire-9, and the Lubben Social Network Scale as predictors as the dependent variables for suicidal ideation; machine learning (ML) methods K-Nearest Neighbors, Random Forest, and Neural Network Classification were used; (3) Results: The prediction of suicidal ideation using depression and social isolation showed high area under the curve (0.643-0.836) and specificity (0.959-0.987) in all ML techniques. In the predictor model (model 2) that additionally evaluated social isolation, the validation accuracy consistently increased compared to the depression-only model (model 1); (4) Conclusions: It is confirmed that the machine learning technique using depression and social isolation can be an effective method when predicting suicidal ideation.
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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.