Supervised Text Classification System Detects Fontan Patients in Electronic Records With Higher Accuracy Than ICD Codes

• Type of Material: Article
• Authors: Yuting Guo, Emory University; Mohammed A Al-Garadi, Vanderbilt University; Wendy M Book, Emory University; Lindsey C Ivey, Emory University; Fred H Rodriguez, III, Emory University; Cheryl L Raskind‐Hood, Emory University; Chad Robichaux, Emory University; Abeed Sarker, Emory University
• Language: English
• Date: 2023-07-04
• Publisher: WILEY
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Authors.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1161/JAHA.123.030046
• Title of Journal or Parent Work: JOURNAL OF THE AMERICAN HEART ASSOCIATION
• Volume: 12
• Issue: 13
• Start Page: e030046
• End Page: e030046
• Grant/Funding Information: This work was supported by the Centers for Disease Control and Prevention Cooperative Agreement, Congenital Heart Defects Surveillance Across Time and Regions grant/award number CDC‐RFA‐DD19‐1902B.
• Keywords: CLINICAL NOTES; congenital heart disease; Cardiovascular System & Cardiology; natural language processing; single ventricle; Fontan; Cardiac & Cardiovascular Systems; Science & Technology; Life Sciences & Biomedicine; IDENTIFICATION
• Research Categories: Biology, Bioinformatics; Health Sciences, Public Health; Engineering, Biomedical

Abstract

BACKGROUND: The Fontan operation is associated with significant morbidity and premature mortality. Fontan cases cannot always be identified by International Classification of Diseases (ICD) codes, making it challenging to create large Fontan patient cohorts. We sought to develop natural language processing–based machine learning models to automatically detect Fontan cases from free texts in electronic health records, and compare their performances with ICD code–based classification. METHODS AND RESULTS: We included free-text notes of 10 935 manually validated patients, 778 (7.1%) Fontan and 10 157 (92.9%) non-Fontan, from 2 health care systems. Using 80% of the patient data, we trained and optimized multiple machine learning models, support vector machines and 2 versions of RoBERTa (a robustly optimized transformer-based model for language understanding), for automatically identifying Fontan cases based on notes. For RoBERTa, we implemented a novel sliding window strategy to overcome its length limit. We evaluated the machine learning models and ICD code–based classification on 20% of the held-out patient data using the F1 score metric. The ICD classification model, support vector machine, and RoBERTa achieved F1 scores of 0.81 (95% CI, 0.79–0.83), 0.95 (95% CI, 0.92–0.97), and 0.89 (95% CI, 0.88–0.85) for the positive (Fontan) class, respectively. Support vector machines obtained the best performance (P<0.05), and both natural language processing models outperformed ICD code–based classification (P<0.05). The sliding window strategy improved performance over the base model (P<0.05) but did not outperform support vector machines. ICD code–based classification produced more false positives. CONCLUSIONS: Natural language processing models can automatically detect Fontan patients based on clinical notes with higher accuracy than ICD codes, and the former demonstrated the possibility of further improvement.