Using machine learning to classify suicide attempt history among youth in medical care settings.
Background: The current study aimed to classify recent and lifetime suicide attempt history among youth presenting to medical settings using machine learning (ML) as applied to a behavioral health screen self-report survey.Methods: In the current study, 13,325 (mean age = 17.06, SD = 2.61) pediatric...
| Publicado en: | Journal of Affective Disorders Vol. 268; pp. 206 - 215 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142318314&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142318314 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01650327 3M9 jtl: Journal of Affective Disorders issn: 01650327 maglogo: N pubinfo: dt: May2020 vid: 268 pid: 1004 pub: Elsevier B.V. artinfo: ui: 142318314 142318314 NLM32174479 142318314 10.1016/j.jad.2020.02.048 NLM32174479 142318314 ppf: 206 ppct: 9 formats: tig: atl: Using machine learning to classify suicide attempt history among youth in medical care settings. aug: au: Burke, Taylor A. Jacobucci, Ross Ammerman, Brooke A. Alloy, Lauren B. Diamond, Guy affil: Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA sug: subj: Suicide, Attempted Male Adolescence Pennsylvania Suicidal Ideation Self-Injurious Behavior Emergency Service Female Cross Sectional Studies Suicide, Attempted Psychosocial Factors Human Risk Factors Algorithms Validation Studies Comparative Studies Evaluation Research Multicenter Studies Adolescent: 13-18 years Male Female ab: Background: The current study aimed to classify recent and lifetime suicide attempt history among youth presenting to medical settings using machine learning (ML) as applied to a behavioral health screen self-report survey.Methods: In the current study, 13,325 (mean age = 17.06, SD = 2.61) pediatric primary care patients from rural, semi-urban, and urban areas of Pennsylvania and 12,001 (mean age = 15.79, SD = 1.40) pediatric patients from an urban children's hospital emergency department were included in the analyses. We used two methods of ML (decision trees, random forests) to (a) generate algorithms to classify suicide attempt history, and (b) validate generated algorithms within and across samples to assess model performance. We also employed ridge regression to evaluate performance of the ML approaches.Results: Our findings demonstrate that ML approaches did not enhance our ability to classify lifetime or recent suicide attempt history among youth across medical care settings, suggesting that relationships may be mainly linear and non-interactive. In line with prior research, a history of suicide planning, active suicidal ideation, passive suicidal ideation, and nonsuicidal self-injury emerged as relatively important correlates of suicide attempt.Limitations: The cross-sectional nature of the current study prevents us from determining the extent to which the important variables identified confer risk for future suicidal behavior.Conclusions: The present study underscores the importance of suicide risk screenings that focus on the assessment of active and passive suicidal ideation and suicide planning, in addition to nonsuicidal self-injury, across pediatric medical settings. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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