Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization

Abstract

Posttraumatic stress disorder (PTSD) develops in a substantial minority of emergency room admits. Inexpensive and accurate person-level assessment of PTSD risk after trauma exposure is a critical precursor to large-scale deployment of early interventions that may reduce individual suffering and societal costs. Toward this aim, we applied ensemble machine learning to predict PTSD screening status three months after severe injury using cost-effective and minimally invasive data. Participants (N = 271) were recruited at a Level 1 Trauma Center where they provided variables routinely collected at the hospital, including pulse, injury severity, and demographics, as well as psychological variables, including self-reported current depression, psychiatric history, and social support. Participant zip codes were used to extract contextual variables including population total and density, average annual income, and health insurance coverage rates from publicly available U.S. Census data. Machine learning yielded good prediction of PTSD screening status 3 months post-hospitalization, AUC = 0.85 95% CI [0.83, 0.86], and significantly outperformed all benchmark comparison models in a cross-validation procedure designed to yield an unbiased estimate of performance. These results demonstrate that good prediction can be attained from variables that individually have relatively weak predictive value, pointing to the promise of ensemble machine learning approaches that do not rely on strong isolated risk factors.

Diagnosing post-traumatic stress disorder using electronic medical record data

“…Additionally, patients' subjective and reporting biases, as well as variations in the symptoms of PTSD that can mimic other mental health conditions such as depression and anxiety, can prevent timely diagnoses. Although much work has been done on diagnosing PTSD, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] the majority of identified indicators represent group-level risk factors (e.g., injury) with less concern for personalized predictors of PTSD (e.g., patient demographics, symptom type, and severity). 1 Recent findings suggest that PTSD is associated with an array of multimodal risk indicators, which makes it unlikely that any single vulnerability factor will account for a large amount of variance in the prediction of this complex disorder.…”

“…The central idea behind computational methods in medical disease identification is to explore patients' data, perform feature engineering, and then use statistical or machine learning algorithms to process the data with the goal of designing a model that is able to assist clinicians in large scale and accurate disease identification. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] Intelligent assistive systems can provide details that can increase diagnostic accuracy, reduce human errors, and allow efficient use of patients' medical data. 17 Previous studies have explored different types of data sources such as EMR data 4,5 ; qualitative data including self-reported data 7 ; telephone-based and face-to-face interviews 1,8,9 ; surveys 9,10 ; scales of psychiatric symptoms 1,3,8,13 ; administrative data holdings 8,11 ; event and emergency department (ED) features 1,2,12 ; biochemical examination 8 ; injury etiology 10,12 ; sleep quality experiment, 14 and data collected using wearable devices.…”

Forecasting individual risk for long-term Posttraumatic Stress Disorder in emergency medical settings using biomedical data: A machine learning multicenter cohort study

“…This methodological limitation can be overcome using machine learning (ML), which uses data-driven modeling to identify computational algorithms that recognize patterns and associations in complex interrelated data. To date, several studies support the promise of ML for early PTSD prognosis ( Galatzer-Levy et al, 2014 , 2017 ; Karstoft et al, 2015 ; Papini et al, 2018 ; Schultebraucks et al, 2020a ; Wshah et al, 2019 ). These studies also showed that biomedical data, including endocrine and physiological markers and received pharmacotherapy, can provide high probabilistic information in these models.…”

Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning

“…The analytical process typically involves a feature selection process, which statistically selects a number of variables from a large data set that have the greatest influence on the outcome of interest, the creation of a regression or classification algorithm, and the validation of the algorithm in an independent dataset or in an unused subset of the original data [39]. Multiple studies have used machine learning approaches to understand the development of PTSD using smartphone data [41], neuroimaging biomarkers [42], and data from longitudinal emergency room studies in adults [43,44] and children [45].…”