Identification of suicidality in adolescent major depressive disorder patients using sMRI: A machine learning approach.
Background: Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD), especially among adolescents and young adults. Machine learning models with the capability of suicide risk identification at an individual level could improve suicide prevention among high-...
| Publicado en: | Journal of Affective Disorders Vol. 280; pp. 72 - 77 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Feb2021:Part A
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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=147582191&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147582191 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01650327 3M9 jtl: Journal of Affective Disorders issn: 01650327 maglogo: N pubinfo: dt: Feb2021:Part A vid: 280 pid: 1004 pub: Elsevier B.V. artinfo: ui: 147582191 147582191 NLM33202340 147582191 10.1016/j.jad.2020.10.077 NLM33202340 147582191 ppf: 72 ppct: 5 formats: tig: atl: Identification of suicidality in adolescent major depressive disorder patients using sMRI: A machine learning approach. aug: au: Hong, Su Liu, Yang S. Cao, Bo Cao, Jun Ai, Ming Chen, Jianmei Greenshaw, Andrew Kuang, Li affil: Mobile Doctoral Station, School of Nursing, Chongqing Medical University, Chongqing, China sug: subj: Depression Suicide Young Adult Adolescence Human Cross Sectional Studies Suicide, Attempted Suicidal Ideation Comparative Studies Multicenter Studies Evaluation Research Validation Studies Adolescent: 13-18 years ab: Background: Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD), especially among adolescents and young adults. Machine learning models with the capability of suicide risk identification at an individual level could improve suicide prevention among high-risk patient population.Methods: A cross-sectional assessment was conducted on a sample of 66 adolescents/young adults diagnosed with MDD. The structural T1-weighted MRI scan of each subject was processed using the FreeSurfer software. The classification model was conducted using the Support Vector Machine - Recursive Feature Elimination (SVM-RFE) algorithm to distinguish suicide attempters and patients with suicidal ideation but without attempts.Results: The SVM model was able to correctly identify suicide attempters and patients with suicidal ideation but without attempts with a cross-validated prediction balanced accuracy of 78.59%, the sensitivity was 73.17% and the specificity was 84.0%. The positive predictive value of suicide attempt was 88.24%, and the negative predictive value was 65.63%. Right lateral orbitofrontal thickness, left caudal anterior cingulate thickness, left fusiform thickness, left temporal pole volume, right rostral anterior cingulate volume, left lateral orbitofrontal thickness, left posterior cingulate thickness, right pars orbitalis thickness, right posterior cingulate thickness, and left medial orbitofrontal thickness were the 10 top-ranked classifiers for suicide attempt.Conclusions: The findings indicated that structural MRI data can be useful for the classification of suicide risk. The algorithm developed in current study may lead to identify suicide attempt risk among MDD patients. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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