title: Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.
abstract: OBJECTIVE
  A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide.
  
  
  METHODS
  A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to 'train' a machine learning algorithm. The resulting algorithm was utilized in identifying novel or 'unseen' individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated.
  
  
  RESULTS
  All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65% and 72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity.
  
  
  CONCLUSIONS
  Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide.
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA; Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
authors.forename: Ives Cavalcante
authors.initials: IC
authors.lastname: Passos
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA. Electronic address: benson.irungu@uth.tmc.edu.
authors.forename: Benson
authors.initials: B
authors.lastname: Mwangi
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Cao
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Jane E
authors.initials: JE
authors.lastname: Hamilton
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Mon-Ju
authors.initials: MJ
authors.lastname: Wu
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA; Beijing HuiLongGuan Hospital, Peking University, Beijing, PR China.
authors.forename: Xiang Yang
authors.initials: XY
authors.lastname: Zhang
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Giovana B
authors.initials: GB
authors.lastname: Zunta-Soares
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Joao
authors.initials: J
authors.lastname: Quevedo
authors.affiliation: Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
authors.forename: Marcia
authors.initials: M
authors.lastname: Kauer-Sant'Anna
authors.affiliation: Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
authors.forename: Flávio
authors.initials: F
authors.lastname: Kapczinski
authors.affiliation: Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Jair C
authors.initials: JC
authors.lastname: Soares
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D015897:Comorbidity; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D019964:Mood Disorders / Q000453:epidemiology / Q000523:psychology*; D010865:Pilot Projects; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D013406:Suicide, Attempted / Q000706:statistics & numerical data*; D055815:Young Adult; D000092864:Suicide Prevention
keywords: Big data; Bipolar disorder; Depression; Machine learning; Personalized medicine; Suicide