ArticleJournal of affective disorders2016
Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.
Article in Journal of affective disorders, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 4 of them syntheses that pooled it.
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
54 citing papers in PubMed, 4 syntheses or guidelines pooled it, 189 citations in OpenAlex.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.Frontiers in digital health · 2022Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Optimizing Suicide Risk Prediction in Korea: A Comparison of Model Performance Using Resampling Methods and Machine Learning Algorithms.Psychiatry investigation · 2025Article
- Prediction of suicidal attempts among Chinese adolescents with mood disorders: a clinical study using a machine learning approach.BMC psychiatry · 2025Article
- Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach.Journal of medical Internet research · 2025Article
- Geography and risk of suicidal ideation and attempts post outpatient psychiatric visit in commercially insured US adults.Journal of psychiatric research · 2025Article
- Plasma and Platelet Brain-Derived Neurotrophic Factor (BDNF) Levels in Bipolar Disorder Patients with Post-Traumatic Stress Disorder (PTSD) or in a Major Depressive Episode Compared to Healthy Controls.International journal of molecular sciences · 2024Article
- Blood epigenome-wide association studies of suicide attempt in adults with bipolar disorder.Translational psychiatry · 2024Article
- Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec.PloS one · 2024Article
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.PeerJ. Computer science · 2024Article
- An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges.Iranian journal of psychiatry · 2023Article
- Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2022Article
- Integrating biobehavioral information to predict mood disorder suicide risk.Brain, behavior, & immunity - health · 2022Review
- Effectiveness of Artificial Intelligence Methods in Personalized Aggression Risk Prediction within Inpatient Psychiatric Treatment Settings-A Systematic Review.Journal of personalized medicine · 2022Review
- Machine learning prediction of suicidal ideation, planning, and attempt among Korean adults: A population-based study.SSM - population health · 2022Article
- Social cognition and bipolar disorder: pending questions and unexplored topics.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2022Article
- [Cross-sectoral therapeutic concepts and innovative technologies: new opportunities for the treatment of patients with mental disorders].Der Nervenarzt · 2022Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 3 countries.
Funding
Abstract
objectiveA growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide.
methodA total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to 'train' a machine learning algorithm. The resulting algorithm was utilized in identifying novel or 'unseen' individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated.
resultsAll algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65% and 72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity.
conclusionRisk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide.
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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.