While several symptoms/comorbidities were similar between the Actual Positive and Likely PTSD cohorts, others, including depression and anxiety disorders, suicidal thoughts/actions, and substance use, were more common in the Likely PTSD cohort, suggesting that certain symptoms may be exacerbated among those without a formal diagnosis. ResultsĪ total of 44,342 patients were classified in the Actual Positive PTSD cohort, 5683 in the Likely PTSD cohort, and 2,074,471 in the Without PTSD cohort. Patient characteristics, symptoms and complications potentially related to PTSD, treatments received, healthcare costs, and healthcare resource utilization were described separately for patients with PTSD (Actual Positive PTSD cohort), patients likely to have PTSD (Likely PTSD cohort), and patients without PTSD (Without PTSD cohort). The model was applied to patients for whom PTSD status could not be confirmed to identify individuals likely and unlikely to have undiagnosed PTSD. A random forest machine learning model was developed and trained to differentiate between patients with and without PTSD using non–trauma-based features. The IBM® MarketScan® Commercial Subset (–) was used. This study used a machine learning approach to identify and describe civilian patients likely to have undiagnosed PTSD in the US commercial population. Without an accurate diagnosis, these patients may lack PTSD-targeted treatments and experience adverse health outcomes. The proportion of patients with post-traumatic stress disorder (PTSD) that remain undiagnosed may be substantial.
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