New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography

Hao Zhang, Neil Jethani, Aahlad Manas Puli, Leonid Garber, Lior Jankelon, Yin Aphinyanaphongs, Rajesh Ranganath

Keywords: Cardiovascular Medicine, Diabetes, Electrocardiography, Deep Learning, Explainability & Interpretability, Algorithmic Fairness & Bias

Track: Proceedings

Abstract: Diabetes has a long asymptomatic period which can often remain undiagnosed for multiple years. In this study, we trained a deep learning model to detect new-onset diabetes using 12-lead ECG and readily available demographic information. To do so, we used retrospective data where patients have both a hemoglobin A1c and ECG measured. However, such patients may not be representative of the complete patient population. As part of the study, we proposed a methodology to evaluate our model in the target population by estimating the probability of receiving an A1c test and reweight the retrospective population to represent the general population. We also adapted an efficient algorithm to generate Shapley values for both ECG signals and demographic features at the same time for model interpretation. The model offers an automated, more accurate method for early diabetes detection compared to current screening efforts. Their potential use in wearable devices can facilitate large-scale, community-wide screening, improving healthcare outcomes.

General Area: Models and Methods

Specific Subject Areas: Algorithmic Fairness & Bias, Explainability & Interpretability, Evaluation Methods & Validity, Supervised Learning, Time Series

Data And Code Availability: Yes

Ethics Board Approval: Yes

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Code URL: https://github.com/rajesh-lab/a1c_ecg

Submission Number: 182