期刊
FRONTIERS IN PSYCHIATRY
卷 10, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fpsyt.2019.00036
关键词
suicide risk prediction; suicidality; suicide risk assessment; clinical informatics; machine learning; natural language processing
类别
资金
- European Science Foundation (ESF) Research Networking Programme Evaluating Information Access Systems
- Swedish Research Council [2015-00359]
- Marie Sklodowska Curie Actions [INCA 600398]
- Instituto de Salud Carlos III [ISCIII PI13/02200, PI16/01852]
- Delegacion del Gobierno para el Plan Nacional de Drogas [20151073]
- American Foundation for Suicide Prevention (AFSP) [LSRG-1-005-16]
- UCLH NIHR Biomedical Research Centre
- Alan Turing Institute under the EPSRC [EP/N510129/1]
- Alan Turing Institute Fellowship [TU/A/000006]
- Medical Research Council (MRC) Health Data Research UK Fellowship [MR/S003118/1]
- Academy of Medical Sciences [SGL015/1020]
- Wellcome Trust
- MRC
- British Heart Foundation
- Arthritis Research UK
- Royal College of Physicians
- Diabetes UK
- UK Medical Research Council [MR/N028244/2]
- King's Centre for Military Health Research
- Medical Research Council (MRC) Clinical Research Training Fellowship [MR/L017105/1]
- Clinician Scientist Fellowship (research project e-HOST-IT) from the Health Foundation
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London
- Academy of Medical Sciences
- MRC [MR/L017105/1, MR/S003118/1] Funding Source: UKRI
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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