Literature DB >> 28269880

A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.

Sisi Ma1, Isaac R Galatzer-Levy1, Xuya Wang1, David Fenyö1, Arieh Y Shalev1.   

Abstract

PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

Entities:  

Mesh:

Year:  2017        PMID: 28269880      PMCID: PMC5333324     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  17 in total

1.  Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults.

Authors:  C R Brewin; B Andrews; J D Valentine
Journal:  J Consult Clin Psychol       Date:  2000-10

2.  Predictors of posttraumatic stress disorder and symptoms in adults: a meta-analysis.

Authors:  Emily J Ozer; Suzanne R Best; Tami L Lipsey; Daniel S Weiss
Journal:  Psychol Bull       Date:  2003-01       Impact factor: 17.737

Review 3.  Longitudinal studies of PTSD: overview of findings and methods.

Authors:  Tamar Peleg; Arieh Y Shalev
Journal:  CNS Spectr       Date:  2006-08       Impact factor: 3.790

4.  Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application.

Authors:  Isaac R Galatzer-Levy; Karen-Inge Karstoft; Alexander Statnikov; Arieh Y Shalev
Journal:  J Psychiatr Res       Date:  2014-09-16       Impact factor: 4.791

Review 5.  A meta-analysis of structural brain abnormalities in PTSD.

Authors:  Anke Karl; Michael Schaefer; Loretta S Malta; Denise Dörfel; Nicolas Rohleder; Annett Werner
Journal:  Neurosci Biobehav Rev       Date:  2006-05-26       Impact factor: 8.989

6.  Prevention of posttraumatic stress disorder by early treatment: results from the Jerusalem Trauma Outreach And Prevention study.

Authors:  Arieh Y Shalev; Yael Ankri; Yossi Israeli-Shalev; Tamar Peleg; Rhonda Adessky; Sara Freedman
Journal:  Arch Gen Psychiatry       Date:  2011-10-03

7.  Post-traumatic stress disorder is associated with PACAP and the PAC1 receptor.

Authors:  Kerry J Ressler; Kristina B Mercer; Bekh Bradley; Tanja Jovanovic; Amy Mahan; Kimberly Kerley; Seth D Norrholm; Varun Kilaru; Alicia K Smith; Amanda J Myers; Manuel Ramirez; Anzhelika Engel; Sayamwong E Hammack; Donna Toufexis; Karen M Braas; Elisabeth B Binder; Victor May
Journal:  Nature       Date:  2011-02-24       Impact factor: 49.962

8.  Higher FKBP5, COMT, CHRNA5, and CRHR1 allele burdens are associated with PTSD and interact with trauma exposure: implications for neuropsychiatric research and treatment.

Authors:  Joseph A Boscarino; Porat M Erlich; Stuart N Hoffman; Xiaopeng Zhang
Journal:  Neuropsychiatr Dis Treat       Date:  2012-03-23       Impact factor: 2.570

9.  Dealing with missing data in a multi-question depression scale: a comparison of imputation methods.

Authors:  Fiona M Shrive; Heather Stuart; Hude Quan; William A Ghali
Journal:  BMC Med Res Methodol       Date:  2006-12-13       Impact factor: 4.615

10.  Early PTSD symptom trajectories: persistence, recovery, and response to treatment: results from the Jerusalem Trauma Outreach and Prevention Study (J-TOPS).

Authors:  Isaac R Galatzer-Levy; Yael Ankri; Sara Freedman; Yossi Israeli-Shalev; Pablo Roitman; Moran Gilad; Arieh Y Shalev
Journal:  PLoS One       Date:  2013-08-22       Impact factor: 3.240

View more
  5 in total

Review 1.  Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016.

Authors:  R A Jenders
Journal:  Yearb Med Inform       Date:  2017-09-11

Review 2.  Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey.

Authors:  Stefania Montani; Manuel Striani
Journal:  Yearb Med Inform       Date:  2019-08-16

3.  A systematic literature review of AI-based digital decision support systems for post-traumatic stress disorder.

Authors:  Markus Bertl; Janek Metsallik; Peeter Ross
Journal:  Front Psychiatry       Date:  2022-08-09       Impact factor: 5.435

4.  Developing a data-driven algorithm for guiding selection between cognitive behavioral therapy, fluoxetine, and combination treatment for adolescent depression.

Authors:  Meredith Gunlicks-Stoessel; Bonnie Klimes-Dougan; Adrienne VanZomeren; Sisi Ma
Journal:  Transl Psychiatry       Date:  2020-09-21       Impact factor: 6.222

5.  Targeting Cognition and Motivation in Coordinated Specialty Care for Early Psychosis: A Grant Report.

Authors:  Rachel Roisum; Danielle Jenkins; Melissa Fisher; Ariel Currie; Sisi Ma; Christopher Lindgren; Piper Meyer-Kalos; Sophia Vinogradov
Journal:  J Psychiatr Brain Sci       Date:  2020-10-16
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.