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DC Field | Value | Language |
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dc.citation.endPage | 4713 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 4702 | - |
dc.citation.title | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.volume | 26 | - |
dc.contributor.author | Lim, Min Hyuk | - |
dc.contributor.author | Cho, Young Min | - |
dc.contributor.author | Kim, Sungwan | - |
dc.date.accessioned | 2023-12-21T13:39:20Z | - |
dc.date.available | 2023-12-21T13:39:20Z | - |
dc.date.created | 2023-09-15 | - |
dc.date.issued | 2022-09 | - |
dc.description.abstract | The objective of this study is to propose MD-VAE: a multi-task disentangled variational autoencoders (VAE) for exploring characteristics of latent representations (LR) and exploiting LR for diverse tasks including glucose forecasting, event detection, and temporal clustering. We applied MD-VAE to one virtual continuous glucose monitoring (CGM) data from an FDA-approved Type 1 Diabetes Mellitus simulator (T1DMS) and one publicly available CGM data of real patients for glucose dynamics of Type 1 Diabetes Mellitus. LR captured meaningful information to be exploited for diverse tasks, and was able to differentiate characteristics of sequences with clinical parameters. LR and generative models have received relatively little attention for analyzing CGM data so far. However, as proposed in our study, VAE has the potential to integrate not only current but also future information on glucose dynamics and unexpected events including interactions of devices in the data-driven manner. We expect that our model can provide complementary views on the analysis of CGM data. | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.26, no.9, pp.4702 - 4713 | - |
dc.identifier.doi | 10.1109/JBHI.2022.3175928 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.scopusid | 2-s2.0-85130471807 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/66024 | - |
dc.identifier.wosid | 000852247000035 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Multi-Task Disentangled Autoencoder for Time-Series Data in Glucose Dynamics | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics | - |
dc.relation.journalResearchArea | Computer Science; Mathematical & Computational Biology; Medical Informatics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Glucose | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Insulin | - |
dc.subject.keywordAuthor | Trajectory | - |
dc.subject.keywordAuthor | Diabetes | - |
dc.subject.keywordAuthor | Decoding | - |
dc.subject.keywordAuthor | Reactive power | - |
dc.subject.keywordAuthor | Continuous glucose monitoring | - |
dc.subject.keywordAuthor | disentanglement | - |
dc.subject.keywordAuthor | generative model | - |
dc.subject.keywordAuthor | latent representation | - |
dc.subject.keywordAuthor | Type 1 diabetes mellitus | - |
dc.subject.keywordPlus | DATA-DRIVEN APPROACH | - |
dc.subject.keywordPlus | INSULIN SENSITIVITY | - |
dc.subject.keywordPlus | BLOOD | - |
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