Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조인정 | * |
dc.date.accessioned | 2021-11-10T16:31:04Z | - |
dc.date.available | 2021-11-10T16:31:04Z | - |
dc.date.issued | 2021 | * |
dc.identifier.issn | 2233-6079 | * |
dc.identifier.issn | 2233-6087 | * |
dc.identifier.other | OAK-30136 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/259310 | - |
dc.description.abstract | Background: Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We devel-oped 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) co-hort 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 net-work 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 mod-els were similar; however, certain results differed. Conclusion: The DL-based T2DM prediction model, constructed using a cohort representative of the population, performs bet-ter than the conventional model. After pilot tests, this model will be provided to all Korean national health screening recipients in the future. | * |
dc.language | English | * |
dc.publisher | KOREAN DIABETES ASSOC | * |
dc.subject | Diabetes mellitus | * |
dc.subject | type 2 | * |
dc.subject | Mass screening | * |
dc.subject | Prediabetic state | * |
dc.subject | Prediction | * |
dc.title | Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort | * |
dc.type | Article | * |
dc.relation.issue | 4 | * |
dc.relation.volume | 45 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.index | KCI | * |
dc.relation.startpage | 515 | * |
dc.relation.lastpage | 525 | * |
dc.relation.journaltitle | DIABETES & METABOLISM JOURNAL | * |
dc.identifier.doi | 10.4093/dmj.2020.0081 | * |
dc.identifier.wosid | WOS:000692093800006 | * |
dc.author.google | Rhee, Sang Youl | * |
dc.author.google | Sung, Ji Min | * |
dc.author.google | Kim, Sunhee | * |
dc.author.google | Cho, In-Jeong | * |
dc.author.google | Lee, Sang-Eun | * |
dc.author.google | Chang, Hyuk-Jae | * |
dc.contributor.scopusid | 조인정(26537053500) | * |
dc.date.modifydate | 20240603130120 | * |