ArticleInjury2021
Prediction of severe chest injury using natural language processing from the electronic health record.
Article in Injury, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.BMC health services research · 2022Pooled it
- Automated Annotation of Pain Chronicity in Patients With Back Pain by Using Electronic Health Records: Retrospective Study.JMIR formative research · 2026Article
- Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification.Scientific reports · 2025Article
- Automated detection of cervical spondylotic myelopathy: harnessing the power of natural language processing.Frontiers in neuroscience · 2025Article
- Using Large Language Models to Extract Core Injury Information From Emergency Department Notes.Journal of Korean medical science · 2024Article
- Prediction of intra-abdominal injury using natural language processing of electronic medical record data.Surgery · 2024Observational
- Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods.Frontiers in psychiatry · 2022Article
- Decision support by machine learning systems for acute management of severely injured patients: A systematic review.Frontiers in surgery · 2022Review
- Comparison and interpretability of machine learning models to predict severity of chest injury.JAMIA open · 2021Article
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Authors and funding
11 authors.
Funding
Abstract
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.
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