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

[1]  Jose Salinas,et al.  Utility of Vital Signs, Heart Rate Variability and Complexity, and Machine Learning for Identifying the Need for Lifesaving Interventions in Trauma Patients , 2014, Shock.

[2]  Isaac R Galatzer-Levy,et al.  636,120 Ways to Have Posttraumatic Stress Disorder , 2013, Perspectives on psychological science : a journal of the Association for Psychological Science.

[3]  A. Shalev,et al.  Early PTSD Symptom Trajectories: Persistence, Recovery, and Response to Treatment: Results from the Jerusalem Trauma Outreach and Prevention Study (J-TOPS) , 2013, PloS one.

[4]  D. Silove,et al.  A multisite analysis of the fluctuating course of posttraumatic stress disorder. , 2013, JAMA psychiatry.

[5]  C. Bryan,et al.  Repetitive traumatic brain injury, psychological symptoms, and suicide risk in a clinical sample of deployed military personnel. , 2013, JAMA psychiatry.

[6]  S. Lui,et al.  Using structural neuroanatomy to identify trauma survivors with and without post-traumatic stress disorder at the individual level , 2013, Psychological Medicine.

[7]  Eric V. Strobl,et al.  Predicting the risk of psychosis onset: advances and prospects , 2012, Early intervention in psychiatry.

[8]  Max A. Little,et al.  Forecasting Depression in Bipolar Disorder , 2012, IEEE Transactions on Biomedical Engineering.

[9]  A. Mechelli,et al.  Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.

[10]  J. Boscarino,et al.  Higher FKBP5, COMT, CHRNA5, and CRHR1 allele burdens are associated with PTSD and interact with trauma exposure: implications for neuropsychiatric research and treatment , 2012, Neuropsychiatric disease and treatment.

[11]  A. Shalev,et al.  Prevention of posttraumatic stress disorder by early treatment: results from the Jerusalem Trauma Outreach And Prevention study. , 2012, Archives of general psychiatry.

[12]  Constantin F. Aliferis,et al.  A Gentle Introduction to Support Vector Machines in Biomedicine: Case Studies , 2011 .

[13]  B. Bradley,et al.  Post-traumatic stress disorder is associated with PACAP and the PAC1 receptor , 2011, Nature.

[14]  Constantin F. Aliferis,et al.  A gentle introduction to support vector machines in biomedicine: Volume 1: Theory and methods , 2011 .

[15]  B. Bradley,et al.  Post-traumatic stress disorder is associated with PACAP and the PAC1 receptor , 2011, Nature.

[16]  Constantin F. Aliferis,et al.  Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation , 2010, J. Mach. Learn. Res..

[17]  G. Bonanno,et al.  Psychopathology and resilience following traumatic injury: a latent growth mixture model analysis. , 2010, Rehabilitation psychology.

[18]  M. Stein,et al.  Posttraumatic stress disorder and obesity: evidence for a risk association. , 2009, American journal of preventive medicine.

[19]  A. Etkin,et al.  Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. , 2007, The American journal of psychiatry.

[20]  David Madigan,et al.  Large-Scale Bayesian Logistic Regression for Text Categorization , 2007, Technometrics.

[21]  F. Supek,et al.  Posttraumatic stress disorder: diagnostic data analysis by data mining methodology. , 2007, Croatian medical journal.

[22]  David S. Wishart,et al.  Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.

[23]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[24]  Loretta S. Malta,et al.  A meta-analysis of structural brain abnormalities in PTSD , 2006, Neuroscience & Biobehavioral Reviews.

[25]  A. Hald A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713-1935 , 2006 .

[26]  A. Shalev,et al.  Longitudinal Studies of PTSD: Overview of Findings and Methods , 2006, CNS Spectrums.

[27]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[28]  Yih-Lon Lin,et al.  Forecasting Violent Behaviors for Schizophrenic Outpatients Using Their Disease Insights : Development of a Binary Logistic Regression Model and a Support Vector Model , 2004 .

[29]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[30]  Suzanne R. Best,et al.  Predictors of posttraumatic stress disorder and symptoms in adults: a meta-analysis. , 2008, Psychological bulletin.

[31]  David Bourbonnaud André Antoine, diffuseur et traducteur ? , 2003 .

[32]  R. Bryant Early predictors of posttraumatic stress disorder , 2003, Biological Psychiatry.

[33]  Gustavo E. A. P. A. Batista,et al.  An analysis of four missing data treatment methods for supervised learning , 2003, Appl. Artif. Intell..

[34]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[35]  R. Kessler,et al.  Short screening scales to monitor population prevalences and trends in non-specific psychological distress , 2002, Psychological Medicine.

[36]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[37]  C. Brewin,et al.  Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults. , 2000, Journal of consulting and clinical psychology.

[38]  R. Kessler,et al.  Posttraumatic stress disorder: the burden to the individual and to society. , 2000, The Journal of clinical psychiatry.

[39]  E. Foa,et al.  Comparison of the PTSD Symptom Scale–Interview Version and the Clinician-Administered PTSD Scale , 2000, Journal of traumatic stress.

[40]  R A Bryant,et al.  Acute Stress Disorder Scale: a self-report measure of acute stress disorder. , 2000, Psychological assessment.

[41]  Edna B. Foa,et al.  The Posttraumatic Cognitions Inventory (PTCI): Development and validation. , 1999 .

[42]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[43]  A. Shalev,et al.  Predicting PTSD in trauma survivors: prospective evaluation of self-report and clinician-administered instruments , 1997, British Journal of Psychiatry.

[44]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[45]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[46]  R. Kessler,et al.  Posttraumatic stress disorder in the National Comorbidity Survey. , 1995, Archives of general psychiatry.

[47]  Jacob Cohen The earth is round (p < .05) , 1994 .

[48]  B. Rothbaum,et al.  A prospective examination of post‐traumatic stress disorder in rape victims , 1992 .

[49]  Stephen M. Stigler,et al.  The History of Statistics: The Measurement of Uncertainty before 1900 by Stephen M. Stigler (review) , 1986, Technology and Culture.