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Machine learning methods to predict child posttraumatic stress: a proof of concept study
Glenn N Saxe1, Sisi Ma2, Jiwen Ren3
1Department of Child and Adolescent Psychiatry, New York University School of Medicine, One Park Avenue, New York, NY, 10016, USA. Glenn.Saxe@nyumc.org.
Insights
Machine Learning (ML) models can predict childhood Posttraumatic Stress Disorder (PTSD) using early risk factors. This study identifies key variables, offering new tools for early intervention in traumatized children.
Area of Science:
- Computational psychiatry
- Pediatric psychology
- Machine learning applications in healthcare
Background:
- Accurate prediction of childhood Posttraumatic Stress Disorder (PTSD) is crucial for effective intervention.
- Machine Learning (ML) has shown promise in disease prediction but has not been applied to childhood PTSD.
- This study explores the feasibility of using ML for predicting childhood PTSD.
Purpose of the Study:
- To determine if ML methods can create accurate predictive classification models for childhood PTSD.
- To identify specific variables with potential causal links to PTSD from these models.
Main Methods:
- Applied ML predictive classification with causal discovery feature selection to a dataset of 163 children hospitalized with injuries.
- Collected 105 biopsychosocial risk factor variables at hospitalization.
- Assessed PTSD symptoms three months post-discharge.
Main Results:
- A predictive classification model for childhood PTSD was successfully developed with significant accuracy.
- Models using subsets of causally relevant features performed comparably to models using all variables.
- Causal Discovery identified 58 variables, with 10 being the most stable predictors.
Conclusions:
- This proof-of-concept study demonstrates ML's capability in predicting childhood PTSD and identifying causal variables.
- The applied techniques offer a valuable addition to the methodological tools for PTSD research.
- Future research should focus on replicating, refining, and expanding these findings.
Background:
The 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.
Methods:
ML 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.
Results:
Seven 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.
Conclusions:
In 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.
