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Lim, Chiehyeon
Service Engineering & Knowledge Discovery Lab
Research Interests
  • Smart service systems, Service-oriented data analytics, Service operations, Service design, Decision science, Personal process management


Risk score-embedded deep learning for biological age estimation: Development and validation

DC Field Value Language Kim, Suhyeon ko Kim, Hangyeol ko Lee, Eun-Sol ko Lim, Chiehyeon ko Lee, Junghye ko 2021-12-31T00:09:24Z - 2021-12-29 ko 2022-03 ko
dc.identifier.citation INFORMATION SCIENCES, v.586, pp.628 - 643 ko
dc.identifier.issn 0020-0255 ko
dc.identifier.uri -
dc.description.abstract The health index measures a person’s overall health status which provides useful information for people to manage their health, so developing a precise and relevant health index is urgent. Currently, many researchers have studied the biological age (BA) estimation, one of the beneficial health indices, by applying machine learning and deep learning techniques to health data. However, most of them have focused on the chronological age prediction or basic latent feature extraction methods. In this paper, we present a new algorithm to estimate BA, called Risk Score-Embedded Autoencoder-based BA (RSAE-BA). RSAE-BA can provide an accurate health index by using deep representation learning with an individual’s health risk. We first proposed a notion of risk score (RS) calculation to monitor a person’s health risk. Then we extracted latent features by using an autoencoder embedding the RS, and used them to generate BA. To evaluate RSAE-BA, we presented a new BA validation method using the RS, which is applicable to both unlabeled and labeled data. We compared the results of RSAE-BA with existing methods, and demonstrated the accuracy of RSAE-BA and its applicability to predict disease incidence. We believe that RSAE-BA will be a useful alternative method to measure a person’s health. ko
dc.language 영어 ko
dc.publisher ELSEVIER SCIENCE INC ko
dc.title Risk score-embedded deep learning for biological age estimation: Development and validation ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-85121235795 ko
dc.identifier.wosid 000768237300011 ko
dc.type.rims ART ko
dc.identifier.doi 10.1016/j.ins.2021.12.015 ko
dc.identifier.url ko
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