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J Am Med Inform Assoc. 2019 Mar 1;26(3):254-261. doi: 10.1093/jamia/ocy166.

Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.

Author information

Health Sciences Division, Burn and Shock Trauma Research Institute, Stritch School of Medicine, Loyola University, Maywood, Illinois, USA.
Health Sciences Division, Center for Health Outcomes and Informatics Research, Loyola University, Maywood, Illinois, USA.
Department of Public Health Sciences, Stritch School of Medicine, Loyola University, Maywood, Illinois, USA.
Department of Computer Science, Loyola University, Chicago, Illinois, USA.
Department of Psychiatry, Rush University Medical Center, Chicago, Illinois, USA.
Department of Surgery, Loyola University Medical Center, Maywood, Illinois, USA.



Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse.

Materials and Methods:

An observational cohort of 1422 adult patients admitted to a trauma center between April 2013 and November 2016. Linguistic processing of clinical notes was performed using the clinical Text Analysis and Knowledge Extraction System. The primary analysis was the binary classification of alcohol misuse. The Alcohol Use Disorders Identification Test served as the reference standard.


The data corpus comprised 91 045 electronic health record notes and 16 091 features. In the final machine learning classifier, 16 features were selected from the first 24 hours of notes for identifying alcohol misuse. The classifier's performance in the validation cohort had an area under the receiver-operating characteristic curve of 0.78 (95% confidence interval [CI], 0.72 to 0.85). Sensitivity and specificity were at 56.0% (95% CI, 44.1% to 68.0%) and 88.9% (95% CI, 84.4% to 92.8%). The Hosmer-Lemeshow goodness-of-fit test demonstrates the classifier fits the data well (P = .17). A simpler rule-based keyword approach had a decrease in sensitivity when compared with the NLP classifier from 56.0% to 18.2%.


The NLP classifier has adequate predictive validity for identifying alcohol misuse in trauma centers. External validation is needed before its application to augment screening.

[Available on 2020-01-02]

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