Classifying Firearm Injury Intent in Electronic Hospital Records Using Natural Language Processing.

Importance: International Classification of Diseases-coded hospital discharge data do not accurately reflect whether firearm injuries were caused by assault, unintentional injury, self-harm, legal intervention, or were of undetermined intent. Applying natural language processing (NLP) and machine learning (ML) techniques to electronic health record (EHR) narrative text could be associated with improved accuracy of firearm injury intent data.

Objective: To assess the accuracy with which an ML model identified firearm injury intent.

Assessment of the Accuracy of Firearm Injury Intent Coding at 3 US Hospitals.

Importance: The absence of reliable hospital discharge data regarding the intent of firearm injuries (ie, whether caused by assault, accident, self-harm, legal intervention, or an act of unknown intent) has been characterized as a glaring gap in the US firearms data infrastructure.

Objective: To use incident-level information to assess the accuracy of intent coding in hospital data used for firearm injury surveillance.

Design, Setting, And Participants: This cross-sectional retrospective medical review study was conducted using case-level data from 3 level I US trauma centers (for 2008-2019) for patients presenting to the emergency department with an incident firearm injury of any severity.

PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes.

Objective: To develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon (PASCLex) from clinical notes to support PASC symptom identification and research.

Methods: We identified 26,117 COVID-19 positive patients from the Mass General Brigham's electronic health records (EHR) and extracted 328,879 clinical notes from their post-acute infection period (day 51-110 from first positive COVID-19 test). PASCLex incorporated Unified Medical Language System® (UMLS) Metathesaurus concepts and synonyms based on selected semantic types.

Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.

Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening methods are resource-intensive, patients with OUD are significantly under-detected. An automated and accurate approach is needed to improve OUD identification so that appropriate care can be provided to these patients in a timely fashion.