Prediction of severe chest injury using natural language processing from the electronic health record.

ABSTRACT:

Introduction

Trauma injury severity scores are currently calculated retrospectively from the electronic health record (EHR) using manual annotation by certified trauma coders. Natural language processing (NLP) of clinical documents in the EHR may enable automated injury scoring. We hypothesize that NLP with machine learning can discriminate between cases of severe and non-severe injury to the thorax after trauma.

Methods

Clinical documents from a trauma center were examined between 2014 and 2018. Severe chest injury was defined as a thorax abbreviated injury score (AIS) >2 and served as the reference standard for supervised learning. Free text unigrams and concept unique identifiers (CUIs) from the Unified Medical Language Systems (UMLS) were extracted from clinical document

SUBMITTER: Kulshrestha S 

PROVIDER: S-EPMC7856032 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature