- Technology/Device
- Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort
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Sang Youl Rhee, Ji Min Sung, Sunhee Kim, In-Jeong Cho, Sang-Eun Lee, Hyuk-Jae Chang
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Diabetes Metab J. 2021;45(4):515-525. Published online February 25, 2021
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DOI: https://doi.org/10.4093/dmj.2020.0081
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 Graphical Abstract
 Abstract
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 - Background
Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We developed a deep learning (DL) based model using a cohort representative of the Korean population.
Methods
This study was conducted on the basis of the National Health Insurance Service-Health Screening (NHIS-HEALS) cohort of Korea. Overall, 335,302 subjects without T2DM at baseline were included. We developed the model based on 80% of the subjects, and verified the power in the remainder. Predictive models for T2DM were constructed using the recurrent neural network long short-term memory (RNN-LSTM) network and the Cox longitudinal summary model. The performance of both models over a 10-year period was compared using a time dependent area under the curve.
Results
During a mean follow-up of 10.4±1.7 years, the mean frequency of periodic health check-ups was 2.9±1.0 per subject. During the observation period, T2DM was newly observed in 8.7% of the subjects. The annual performance of the model created using the RNN-LSTM network was superior to that of the Cox model, and the risk factors for T2DM, derived using the two models were similar; however, certain results differed.
Conclusion
The DL-based T2DM prediction model, constructed using a cohort representative of the population, performs better than the conventional model. After pilot tests, this model will be provided to all Korean national health screening recipients in the future.
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Citations
Citations to this article as recorded by  - Interpretable time-series neural turing machine for prognostic prediction of patients with type 2 diabetes in physician-pharmacist collaborative clinics
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