Patient Phenotyping for Atopic Dermatitis With Transformers and Machine Learning: Algorithm Development and Validation Study

• Published in: JMIR formative research Vol. 8; p. e52200
• Main Authors: Wang, Andrew, Fulton, Rachel, Hwang, Sy, Margolis, David J, Mowery, Danielle
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 26.01.2024
• Subjects: phenotype; transformers; natural language processing; skin; transformer; atopic dermatitis; health; health records; informatics; classification; machine learning; EHR; NLP; dermatitis; dermatology; classifier; electronic health record; patient phenotyping
• ISSN: 2561-326X; 2561-326X
• DOI: 10.2196/52200

Atopic dermatitis (AD) is a chronic skin condition that millions of people around the world live with each day. Performing research into identifying the causes and treatment for this disease has great potential to provide benefits for these individuals. However, AD clinical trial recruitment is not a trivial task due to the variance in diagnostic precision and phenotypic definitions leveraged by different clinicians, as well as the time spent finding, recruiting, and enrolling patients by clinicians to become study participants. Thus, there is a need for automatic and effective patient phenotyping for cohort recruitment. This study aims to present an approach for identifying patients whose electronic health records suggest that they may have AD. We created a vectorized representation of each patient and trained various supervised machine learning methods to classify when a patient has AD. Each patient is represented by a vector of either probabilities or binary values, where each value indicates whether they meet a different criteria for AD diagnosis. The most accurate AD classifier performed with a class-balanced accuracy of 0.8036, a precision of 0.8400, and a recall of 0.7500 when using XGBoost (Extreme Gradient Boosting). Creating an automated approach for identifying patient cohorts has the potential to accelerate, standardize, and automate the process of patient recruitment for AD studies; therefore, reducing clinician burden and informing the discovery of better treatment options for AD.