Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach

Journal of Affective Disorders

Volumn 193, Issue , 2016, Pages 109-116

  Passos, Ives Cavalcante ^a,b   Mwangi, Benson ^a   Cao, Bo ^a   Hamilton, Jane E ^a   Wu, Mon Ju ^a   Zhang, Xiang Yang ^a,c   Zunta Soares, Giovana B ^a   Quevedo, Joao ^a   Kauer Sant'Anna, Marcia ^b   Kapczinski, Flávio ^b   Soares, Jair C ^a  

^a UNIVERSITY OF TEXAS MEDICAL SCHOOL   (United States)

^b FEDERAL UNIVERSITY OF RIO GRANDE DO SUL   (Brazil)

^c PEKING UNIVERSITY   (China)

Author keywords

Big data; Bipolar disorder; Depression; Machine learning; Personalized medicine; Suicide

Indexed keywords

ACCURACY; ADULT; ALGORITHM; AREA UNDER THE CURVE; ARTICLE; COCAINE DEPENDENCE; DEMOGRAPHY; DEPRESSION; FEMALE; HOSPITALIZATION; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MOOD DISORDER; PILOT STUDY; POSTTRAUMATIC STRESS DISORDER; PREDICTION; PREDICTOR VARIABLE; PRIORITY JOURNAL; PSYCHOSIS; RELEVANCE VECTOR MACHINE; SENSITIVITY AND SPECIFICITY; SUICIDE ATTEMPT; ADOLESCENT; AGED; COMORBIDITY; MIDDLE AGED; MOOD DISORDERS; PROCEDURES; PSYCHOLOGY; REPRODUCIBILITY; RISK ASSESSMENT; RISK FACTOR; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; SUICIDE; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; ALGORITHMS; COMORBIDITY; FEMALE; HUMANS; MACHINE LEARNING; MALE; MIDDLE AGED; MODELS, STATISTICAL; MOOD DISORDERS; PILOT PROJECTS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; RISK FACTORS; SUICIDE; SUICIDE, ATTEMPTED; YOUNG ADULT;

EID: 84953455442     PISSN: 01650327     EISSN: 15732517     Source Type: Journal    
DOI: 10.1016/j.jad.2015.12.066     Document Type: Article

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