Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study To Assess Risk and rEsilience in Servicemembers (Army STARRS).
about
Sentinel events preceding youth firearm violence: an investigation of administrative data in DelawareDigital Suicide Prevention: Can Technology Become a Game-changer?Big data are coming to psychiatry: a general introductionUsing self-report surveys at the beginning of service to develop multi-outcome risk models for new soldiers in the U.S. Army.Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers.Predicting non-familial major physical violent crime perpetration in the US Army from administrative data.Machine learning and systems genomics approaches for multi-omics data.Assessment of psychological pain in suicidal veterans.Multiple risk factors predict recurrence of major depressive disorder in women.Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time.Association Between Social Integration and Suicide Among Women in the United States.Rational Risk-Benefit Decision-Making in the Setting of Military Mefloquine PolicyPredicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine LearningSelf-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: a meta-analysis of longitudinal studiesNovel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid.Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).Pokorny's complaint: the insoluble problem of the overwhelming number of false positives generated by suicide risk assessment.Psychosis as a risk factor for suicidal thoughts and behaviors: a meta-analysis of longitudinal studies.Predicting suicide with the SAD PERSONS scale.A Risk Algorithm for the Persistence of Suicidal Thoughts and Behaviors During College.Suicide Rates After Discharge From Psychiatric Facilities: A Systematic Review and Meta-analysis.The use of electronic health records for psychiatric phenotyping and genomics.Neurocognitive Function and Suicide in U.S. Army Soldiers.Between-visit changes in suicidal ideation and risk of subsequent suicide attempt.Suicidal ideation and subsequent completed suicide in both psychiatric and non-psychiatric populations: a meta-analysis.Medically Documented Suicide Ideation Among U.S. Army Soldiers.Health care contact and suicide risk documentation prior to suicide death: Results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).Mental health, migration stressors and suicidal ideation among Latino immigrants in Spain and the United States.Developing a practical suicide risk prediction model for targeting high-risk patients in the Veterans health Administration.Spatial Proximity to Incidents of Community Violence Is Associated with Fewer Suicides in Urban California.Dissociative, depressive, and PTSD symptom severity as correlates of nonsuicidal self-injury and suicidality in dissociative disorder patients.Suicidal thoughts and emotion competence.Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales.Suicide mortality among male veterans discharged from Veterans Health Administration acute psychiatric units from 2005 to 2010.Letter to the Editor: Suicide as a complex classification problem: machine learning and related techniques can advance suicide prediction - a reply to Roaldset (2016).Understanding suicide risk within the Research Domain Criteria (RDoC) framework: A meta-analytic review.Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth.Annual Research Review: Suicide among youth - epidemiology, (potential) etiology, and treatment.The Opioid Abuse Risk Screener predicts aberrant same-day urine drug tests and 1-year controlled substance database checks: A brief report.Suicide risk assessment among psychiatric inpatients: a systematic review and meta-analysis of high-risk categories.
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P2860
Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study To Assess Risk and rEsilience in Servicemembers (Army STARRS).
description
2015 nî lūn-bûn
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2015 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2015年の論文
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2015年学术文章
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2015年学术文章
@zh-cn
2015年学术文章
@zh-hans
2015年学术文章
@zh-my
2015年学术文章
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2015年學術文章
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name
Predicting suicides after psyc ...... Servicemembers (Army STARRS).
@ast
Predicting suicides after psyc ...... Servicemembers (Army STARRS).
@en
Predicting suicides after psyc ...... d rEsilience in Servicemembers
@nl
type
label
Predicting suicides after psyc ...... Servicemembers (Army STARRS).
@ast
Predicting suicides after psyc ...... Servicemembers (Army STARRS).
@en
Predicting suicides after psyc ...... d rEsilience in Servicemembers
@nl
prefLabel
Predicting suicides after psyc ...... Servicemembers (Army STARRS).
@ast
Predicting suicides after psyc ...... Servicemembers (Army STARRS).
@en
Predicting suicides after psyc ...... d rEsilience in Servicemembers
@nl
P2093
P2860
P50
P921
P1433
P1476
Predicting suicides after psyc ...... Servicemembers (Army STARRS).
@en
P2093
Alan M Zaslavsky
Amy M Millikan-Bell
Anthony J Rosellini
Army STARRS Collaborators
Carol S Fullerton
Christopher H Warner
Christopher Ivany
Evelyn J Bromet
James A Naifeh
Junlong Li
P2860
P356
10.1001/JAMAPSYCHIATRY.2014.1754
P407
P50
P577
2015-01-01T00:00:00Z