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Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-4178-2980
Karolinska Inst, Natl Ctr Suicide Res & Prevent NASP, Dept Learning Informat Management & Eth LIME, Stockholm, Sweden.;Stockholm Hlth Care Serv SLSO, Natl Ctr Suicide Res & Prevent NASP, Ctr Hlth Econ Informat & Hlth Serv Res CHIS, Stockholm, Sweden..
IIS Jimenez Diaz Fdn, Dept Psychiat, Madrid, Spain.;Univ Autonoma Madrid, Dept Psychiat, Madrid, Spain.;Gen Hosp Villalba, Dept Psychiat, Madrid, Spain.;Carlos III Inst Hlth, CIBERSAM, Madrid, Spain.;Univ Hosp Rey Juan Carlos, Dept Psychiat, Mostoles, Spain.;Univ Hosp Infanta Elena, Dept Psychiat, Valdemoro, Spain.;Univ Catolica Maule, Dept Psychiat, Talca, Chile..
Univ Sheffield, Dept Comp Sci, Sheffield, S Yorkshire, England..
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2019 (English)In: Frontiers in Psychiatry, ISSN 1664-0640, E-ISSN 1664-0640, Vol. 10, article id 36Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA , 2019. Vol. 10, article id 36
Keywords [en]
suicide risk prediction, suicidality, suicide risk assessment, clinical informatics, machine learning, natural language processing
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
URN: urn:nbn:se:kth:diva-245137DOI: 10.3389/fpsyt.2019.00036ISI: 000458715600001PubMedID: 30814958OAI: oai:DiVA.org:kth-245137DiVA, id: diva2:1296010
Note

QC 20190313

Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-13Bibliographically approved

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Velupillai, Sumithra

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