New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2022
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909931741904896 |
|---|---|
| author | Zhang, Hao Jethani, Neil Puli, Aahlad Garber, Leonid Jankelson, Lior Aphinyanaphongs, Yindalon Ranganath, Rajesh |
| author_facet | Zhang, Hao Jethani, Neil Puli, Aahlad Garber, Leonid Jankelson, Lior Aphinyanaphongs, Yindalon Ranganath, Rajesh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_02900 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography Zhang, Hao Jethani, Neil Puli, Aahlad Garber, Leonid Jankelson, Lior Aphinyanaphongs, Yindalon Ranganath, Rajesh Machine Learning Artificial Intelligence Computers and Society 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. |
| title | New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2205.02900 |