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Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

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Natural Language Processing to Build a Multicenter Computable Phenotype Library for Adults with Congenital Heart

Spencer Thomas, Angus Dawson, Hifsa Chaudhry

Medrxiv : the Preprint Server for Health Sciences

|September 2, 2025

Summary

Classifiers were developed to identify adult congenital heart disease (ACHD) phenotypes for biobank data. Six of eight phenotypes were classified with high accuracy, supporting quality improvement and data population efforts.

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A Pipeline to Characterize Structural Heart Defects in the Fetal Mouse

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Published on: December 16, 2022

Area of Science:

Cardiology
Bioinformatics
Machine Learning

Background:

Adult congenital heart disease (ACHD) requires precise phenotyping for effective management and research.
Biobanks are crucial for storing patient data, but require accurate variable population.

Purpose of the Study:

To develop and validate automated classifiers for multiple ACHD phenotypes.
To enable efficient population of biobank variables using machine learning.

Main Methods:

Trained classifiers on a labeled dataset of 1492 ACHD patients for eight phenotypes.
Utilized a larger unlabeled dataset (15869 patients) for pre-training and validation.
Employed three different classifier architectures and evaluated performance using F1 scores and positive predictive values.

Main Results:

Achieved F1 scores ranging from 0.66 to 1 on held-out labeled data for eight phenotypes.
Validated six phenotypes on unlabeled data with positive predictive values from 81.5% to 100%.
Identified cyanosis and NYHA functional class as challenging phenotypes due to variability and observer agreement issues.

Conclusions:

Successfully classified six out of eight ACHD phenotypes with satisfactory performance.
Demonstrated the utility of Natural Language Processing (NLP)-based classifiers for ACHD phenotyping.
The developed classifiers are suitable for quality improvement initiatives and populating ACHD registries.