• Sujay Kulshrestha, Dmitriy Dligach, Cara Joyce, Marshall S Baker, Richard Gonzalez, Ann P O'Rourke, Joshua M Glazer, Anne Stey, Jacqueline M Kruser, Matthew M Churpek, and Majid Afshar.
• Burn and Shock Trauma Research Institute, Loyola University Chicago, CTRE Building 115, Room 315, 2160 South 1st Avenue, Maywood, IL, United States; Department of Surgery, Loyola University Medical Center, EMS Building 110, Room 3210, 2160 South 1st Avenue, Maywood, IL, United States. Electronic address: sujay.kulshrestha@lumc.edu.
• Injury. 2021 Feb 1; 52 (2): 205-212.

IntroductionTrauma 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.MethodsClinical 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 documents collected at one hour, four hours, and eight hours after patient arrival to the emergency department. Logistic regression models with elastic net regularization were tuned to maximize area under the receiver operating characteristic curve (AUROC) using 10-fold cross-validation on the training dataset (80%) and tested on a hold-out 20% dataset.ResultsThere were 6,891 traumas that met inclusion criteria. The complete data corpus consisted of 473,694 documents. Models trained using the first hour of data had a mean AUROC of 0.88 (95%CI [0.86, 0.89]); model discrimination and reclassification from the first hour significantly improved after eight hours with a mean AUROC of 0.94 (95%CI [0.93, 0.95]). Performance of models using CUIs were similar to unigrams (p>0.05). Models demonstrated excellent clinical face validity.ConclusionsBoth CUIs and unigrams demonstrated excellent discrimination in predicting severity of chest injury using the first eight hours of clinical documents. Our model demonstrates that automated anatomical injury scoring is feasible and may be used for aggregation of data for trauma research and quality programs.Copyright © 2020 Elsevier Ltd. All rights reserved.

9 keywords...

hide…