Machine learning methods to predict child posttraumatic stress: a proof of concept study

BMC Psychiatry

Research Article

Jiwen Ren¹  Glenn N. Saxe²  Sisi Ma³  Constantin Aliferis⁴ 

[1] Department of Child and Adolescent Psychiatry and Center for Health Informatics and Bioinformatics, New York University School of Medicine, One Park Avenue, 10016, New York, NY, USA;Department of Child and Adolescent Psychiatry, New York University School of Medicine, One Park Avenue, 10016, New York, NY, USA;Institute for Health Informatics and Department of Medicine, University of Minnesota, 330 Diehl Hall, MMC912, 420 Delaware Street S.E, 55455, Minneapolis, Minnesota, Mpls, MN, USA;Institute for Health Informatics, Department of Medicine, and Data Science Program, University of Minnesota, Minneapolis, MN, USA;Department of Biostatistics, Vanderbilt University, 330 Diehl Hall, MMC912, 420 Delaware Street S.E., Mpls, MN, 55455, Nashville, TN, USA;

关键词: Traumatic stress;    PTSD;    Machine learning;    Informatics;    Child & Adolescent psychiatry;   

DOI  :  10.1186/s12888-017-1384-1

 received in 2016-05-16, accepted in 2017-06-09,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2017