ArticleJournal of the American Medical Informatics Association : JAMIA2021
Temporally informed random forests for suicide risk prediction.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Using smartphone surveys to predict next-week suicide attempts.Journal of psychopathology and clinical science · 2026Article
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.The annals of applied statistics · 2026Article
- Comparing patterns of recent mental health service use for predicting suicidal events following emergency department mental health visits in the United States: A national cohort study.Social psychiatry and psychiatric epidemiology · 2026Article
- Article
- Machine learning models predict mortality risk in diabetic neuropathy patients using MIMIC-IV data.Scientific reports · 2025Article
- Decoding vital variables in predicting different phases of suicide among young adults with childhood sexual abuse: a machine learning approach.Translational psychiatry · 2025Article
- Continuous time and dynamic suicide attempt risk prediction with neural ordinary differential equations.NPJ digital medicine · 2025Article
- Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Review
- 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
- Importance of variables from different time frames for predicting self-harm using health system data.Journal of biomedical informatics · 2024Article
- Importance of variables from different time frames for predicting self-harm using health system data.medRxiv : the preprint server for health sciences · 2024Article
- Construction and evaluation of a liver cancer risk prediction model based on machine learning.World journal of gastrointestinal oncology · 2024Article
- Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.Psychiatry research · 2024Article
- Machine learning methods for adult OSAHS risk prediction.BMC health services research · 2024Article
- Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction.NPJ digital medicine · 2023Article
- Predictive structured-unstructured interactions in EHR models: A case study of suicide prediction.NPJ digital medicine · 2022Article
- Predictive models: important problems and innovative methods.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Dynamic suicide topic modelling: Deriving population-specific, psychosocial and time-sensitive suicide risk variables from Electronic Health Record psychotherapy notes.Clinical psychology & psychotherapyArticle
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Authors and funding
7 authors.
Funding
Abstract
objectiveSuicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions. MATERIALS AND
methodsWe propose a temporally enhanced variant of the random forest (RF) model-Omni-Temporal Balanced Random Forests (OT-BRFs)-that incorporates temporal information in every tree within the forest. We develop and validate this model using longitudinal EHRs and clinician notes from the Mass General Brigham Health System recorded between 1998 and 2018, and compare its performance to a baseline Naive Bayes Classifier and 2 standard versions of balanced RFs.
resultsTemporal variables were found to be associated with suicide risk: Elevated suicide risk was observed in individuals with a higher total number of visits as well as those with a low rate of visits over time, while lower suicide risk was observed in individuals with a longer period of EHR coverage. RF models were more accurate than Naive Bayesian classifiers at predicting suicide risk in advance (area under the receiver operating curve = 0.824 vs. 0.754, respectively). The proposed OT-BRF model performed best among all RF approaches, yielding a sensitivity of 0.339 at 95% specificity, compared to 0.290 and 0.286 for the other 2 RF models. Temporal variables were assigned high importance by the models that incorporated them. DISCUSSION: We demonstrate that temporal variables have an important role to play in suicide risk detection and that requiring their inclusion in all RF trees leads to increased predictive performance. Integrating temporal information into risk prediction models helps the models interpret patient data in temporal context, improving predictive performance.
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