Training and Performance of an Electrocardiogram-Enabled Machine Learning Model for Detection of Advanced Chronic
Puru Rattan1,2, Joseph C Ahn1, Beatriz Sordi Chara1
1Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
Introduction:
Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD).
Methods:
A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier.
Results:
There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850-0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16-13.63).
Discussion:
An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.
Related Concept Videos
01:25Ultrasound II: Endoscopic Ultrasound and FibroScan
Endoscopic Ultrasound (EUS):
01:29Electrocardiogram
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...