New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography

Hao Zhang, Neil Jethani, Aahlad Puli, Leonid Garber, Lior Jankelson, Yindalon Aphinyanaphongs, Rajesh Ranganath
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:1194-1217, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-zhang26a, title = {New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography}, author = {Zhang, Hao and Jethani, Neil and Puli, Aahlad and Garber, Leonid and Jankelson, Lior and Aphinyanaphongs, Yindalon and Ranganath, Rajesh}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {1194--1217}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/zhang26a/zhang26a.pdf}, url = {https://proceedings.mlr.press/v297/zhang26a.html}, 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.} }
Endnote
%0 Conference Paper %T New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography %A Hao Zhang %A Neil Jethani %A Aahlad Puli %A Leonid Garber %A Lior Jankelson %A Yindalon Aphinyanaphongs %A Rajesh Ranganath %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-zhang26a %I PMLR %P 1194--1217 %U https://proceedings.mlr.press/v297/zhang26a.html %V 297 %X 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.
APA
Zhang, H., Jethani, N., Puli, A., Garber, L., Jankelson, L., Aphinyanaphongs, Y. & Ranganath, R.. (2026). New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:1194-1217 Available from https://proceedings.mlr.press/v297/zhang26a.html.

Related Material