ArticleFrontiers in psychiatry2019
Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior.
Article in Frontiers in psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study.JMIR medical informatics · 2026Article
- Use of data-driven decision-making among agencies serving individuals with criminal-legal system involvement: a qualitative study.BMC health services research · 2025Article
- Review
- Social connection and suicidal thoughts and behaviors in the Million Veteran Program cohort.Journal of psychiatric research · 2024Article
- How, Why and When: Nursing Staff's Experiences of Working With Suicide Risk Assessment Instruments.Nursing open · 2024Article
- Recognising and Responding to Suicide-Risk Factors in Primary Care: A Scoping Review.Journal of prevention (2022) · 2024Article
- Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs.NPJ digital medicine · 2024Article
- Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.PeerJ. Computer science · 2024Article
- Building an Integrated Data Infrastructure to Examine the Spectrum of Suicide Risk Factors in Philadelphia Medicaid.Administration and policy in mental health · 2023Article
- Toward objective characterizations of suicide risk: A narrative review of laboratory-based cognitive and behavioral tasks.Neuroscience and biobehavioral reviews · 2023Review
- Article
- Should suicide risk assessment be embedded in undergraduate dental curricula?British dental journal · 2023Article
- Identifying Military Service Status in Electronic Healthcare Records from Psychiatric Secondary Healthcare Services: A Validation Exercise Using the Military Service Identification Tool.Healthcare (Basel, Switzerland) · 2023Article
- How is suicide risk assessed in healthcare settings in the UK? A systematic scoping review.PloS one · 2023Article
- Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.Frontiers in psychiatry · 2023Article
- Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors.The Journal of clinical psychiatry · 2022Article
- Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study.JMIR formative research · 2022Article
- Bootstrapping semi-supervised annotation method for potential suicidal messages.Internet interventions · 2022Review
- Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology.BMC psychiatry · 2021Article
- Neurophysiological Markers Related to Negative Self-referential Processing Differentiate Adolescent Suicide Ideators and Attempters.Biological psychiatry global open science · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.
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