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Volumn 13, Issue 3, 2014, Pages 265-274

How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys

(40)  Kessler, Ronald C a   Rose, Sherri a   Koenen, Karestan C b   Karam, Elie G c   Stang, Paul E d   Stein, Dan J e   Heeringa, Steven G f   Hill, Eric D a   Liberzon, Israel f   McLaughlin, Katie A g   McLean, Samuel A h   Pennell, Beth E f   Petukhova, Maria a   Rosellini, Anthony J a   Ruscio, Ayelet M i   Shahly, Victoria a   Shalev, Arieh Y j   Silove, Derrick k   Zaslavsky, Alan M a   Angermeyer, Matthias C l   more..


Author keywords

Machine learning; Penalized regression; Post traumatic stress disorder; Predictive modeling; Random forests; Ridge regression

Indexed keywords

ADULT; ALGORITHM; AREA UNDER THE CURVE; ARTICLE; CONTROLLED STUDY; DSM-IV; EXPERIENCE; EXPLORATORY RESEARCH; FEMALE; HEALTH SURVEY; HUMAN; INJURY; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MENTAL HEALTH; POSTTRAUMATIC STRESS DISORDER; PREDICTION; PREDICTIVE VALUE; PREVALENCE; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; SEXUAL ASSAULT; STRUCTURED CLINICAL INTERVIEW FOR DSM DISORDERS; TRAUMATIC EXPERIENCE; WORLD HEALTH ORGANIZATION;

EID: 84908086547     PISSN: 17238617     EISSN: 20515545     Source Type: Journal    
DOI: 10.1002/wps.20150     Document Type: Article
Times cited : (186)

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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.