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BMC Psychiatry

, 17:223

Child, adolescent and developmental psychiatry

Abstract

BackgroundThe care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder PTSD, using information available around the time of trauma. Machine Learning ML computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods – as applied in other fields – produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified – from the aforementioned predictive classification models - with putative causal relations to PTSD.

MethodsML predictive classification methods – with causal discovery feature selection – were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains.

ResultsSeven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable.

ConclusionsIn this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

KeywordsTraumatic stress PTSD Machine learning Informatics Child and Adolescent psychiatry AbbreviationsAUCArea under the curve in this study under the ROC curve

GLMGeneralized Linear Model

MBMarkov Boundary and less technically strictly: Markov Blanket

MLMachine learning

PCParents and Children set

PTSD RIPosttraumatic Stress Disorder Reaction Index

RFRandom Forest

RNNCVRepeated Nested N-fold Cross Validation

ROCReceiver Operating Characteristic curve

SNPSingle-Nucleotide Polymorphism

SVMSupport Vector Machine

Electronic supplementary materialThe online version of this article doi:10.1186-s12888-017-1384-1 contains supplementary material, which is available to authorized users.

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Autor: Glenn N. Saxe - Sisi Ma - Jiwen Ren - Constantin Aliferis

Fuente: https://link.springer.com/







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