Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population - a machine learning approach.

ABSTRACT:

Background

The proportion of patients with post-traumatic stress disorder (PTSD) that remain undiagnosed may be substantial. Without an accurate diagnosis, these patients may lack PTSD-targeted treatments and experience adverse health outcomes. This study used a machine learning approach to identify and describe civilian patients likely to have undiagnosed PTSD in the US commercial population.

Methods

The IBM® MarketScan® Commercial Subset (10/01/2015-12/31/2018) was used. A random forest machine learning model was developed and trained to differentiate between patients with and without PTSD using non-trauma-based features. The model was applied to patients for whom PTSD status could not be confirmed to identify individuals likely and unlikely to have undiagnosed PTSD. Patie

SUBMITTER: Gagnon-Sanschagrin P 

PROVIDER: S-EPMC9519190 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature