A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

scientific article published on 3 November 2016

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial is …
instance of (P31):
scholarly articleQ13442814

External links are
P356DOI10.1111/SLTB.12312
P698PubMed publication ID27813129

P50authorLouis-Philippe MorencyQ6686513
Kevin Bretonnel CohenQ57335098
P2093author name stringBrian Connolly
John P Pestian
Michael Sorter
Cheryl McCullumsmith
Stefan Scherer
Jeffry T Gee
Lesley Rohlfs
STM Research Group
P2860cites workAssessment of suicidal intention: The Scale for Suicide IdeationQ28244530
Discovery and validation of blood biomarkers for suicidalityQ28296874
Hopelessness and eventual suicide: a 10-year prospective study of patients hospitalized with suicidal ideationQ28307938
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Prediction of suicidal behavior in clinical research by lifetime suicidal ideation and behavior ascertained by the electronic Columbia-Suicide Severity Rating ScaleQ34376444
Ten-year review of rating scales. III: scales assessing suicidality, cognitive style, and self-esteemQ34938861
A systematic review of suicide rating scales in schizophreniaQ36399683
Predicting and preventing suicide: do we know enough to do either?Q36600646
Neurobiology of suicide: do biomarkers exist?Q38083578
Lying words: predicting deception from linguistic stylesQ38420908
Prediction error estimation: a comparison of resampling methodsQ40421222
Prediction of suicide in psychiatric patients. Report of a prospective studyQ43859960
Suicide risk scales: do they help to predict suicidal behaviour?Q44899510
A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department.Q45953775
A Conversation with Edwin ShneidmanQ45962485
Tracking suicide risk factors through Twitter in the US.Q46429869
Suicide risk assessment: myth and realityQ48106255
Suicidal ideation and later suicideQ48664567
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Standardized rater training for the Hamilton Depression Rating Scale (HAMD-17) in psychiatric novicesQ52955808
The prediction of suicide. Sensitivity, specificity, and predictive value of a multivariate model applied to suicide among 1906 patients with affective disorders.Q53773025
Machine learning in automated text categorizationQ57771154
P433issue1
P921main subjectsuicidal ideationQ944142
machine learningQ2539
suicideQ10737
suicide preventionQ3298118
multicenter clinical trialQ6934595
suicide riskQ47319077
P304page(s)112-121
P577publication date2016-11-03
P1433published inSuicide and Life-Threatening BehaviorQ15716384
P1476titleA Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial
P478volume47

Reverse relations

cites work (P2860)
Q38636577A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support
Q47631957Annual Research Review: Suicide among youth - epidemiology, (potential) etiology, and treatment
Q98292695Assessment of supervised classifiers for the task of detecting messages with suicidal ideation
Q94558942Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative
Q90024152Can acute suicidality be predicted by Instagram data? Results from qualitative and quantitative language analyses
Q57299960Ecologically assessed affect and suicidal ideation following psychiatric inpatient hospitalization
Q47114432Epilepsy Treatment: A Futurist View
Q91834532Evidence-Based Assessment from Simple Clinical Judgments to Statistical Learning: Evaluating a Range of Options Using Pediatric Bipolar Disorder as a Diagnostic Challenge
Q47590460Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing
Q58701145Natural Language Processing of Social Media as Screening for Suicide Risk
Q90390540Suicide prediction models: a critical review of recent research with recommendations for the way forward
Q39431192Using New and Emerging Technologies to Identify and Respond to Suicidality Among Help-Seeking Young People: A Cross-Sectional Study

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