BROWSE

Related Researcher

Author's Photo

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

ITEM VIEW & DOWNLOAD

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

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
Risk score-embedded deep learning for biological age estimation: Development and validation
Author
Kim, SuhyeonKim, HangyeolLee, Eun-SolLim, ChiehyeonLee, Junghye
Issue Date
2022-03
Publisher
Elsevier BV
Citation
INFORMATION SCIENCES, v.586, pp.628 - 643
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/55644
DOI
10.1016/j.ins.2021.12.015
ISSN
0020-0255
Appears in Collections:
SME_Journal Papers
Files in This Item:
There are no files associated with this item.

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU