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

Andrew Wang¹, Rachel Fulton², Sy Hwang¹

¹University of Pennsylvania, Philadelphia, PA, United States.

JMIR Formative Research

|January 26, 2024

Summary

This summary is machine-generated.

This study developed an automated method using machine learning to identify patients with atopic dermatitis (AD) from electronic health records, improving clinical trial recruitment.

Keywords:

EHR NLP atopic dermatitis classification classifier dermatitis dermatology electronic health record health health records informatics machine learning natural language processing patient phenotyping phenotype skin transformer transformers

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Area of Science:

Medical Informatics
Computational Biology
Clinical Research

Background:

Atopic dermatitis (AD) is a prevalent chronic skin condition impacting millions globally.
Current AD research faces challenges in patient recruitment due to diagnostic variability and time-intensive manual processes.
Automated patient phenotyping is crucial for efficient cohort identification in AD studies.

Purpose of the Study:

To develop and present an automated approach for identifying potential atopic dermatitis (AD) patients using electronic health records (EHRs).
To streamline the process of patient identification for clinical trial recruitment in AD research.

Main Methods:

Patients were represented using vectorized data, incorporating diagnostic criteria.
Supervised machine learning models were trained to classify patients with atopic dermatitis (AD).
XGBoost (Extreme Gradient Boosting) was utilized as a primary classification algorithm.

Main Results:

The XGBoost classifier achieved a class-balanced accuracy of 0.8036.
The model demonstrated a precision of 0.8400 and a recall of 0.7500 for identifying AD patients.
The developed approach shows significant potential for accurate patient phenotyping.

Conclusions:

Automated patient cohort identification can accelerate and standardize recruitment for atopic dermatitis (AD) studies.
This approach has the potential to reduce clinician burden and facilitate the discovery of improved AD treatments.
Effective patient phenotyping is key to advancing AD research and therapeutic development.