Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM

Kiichi Tago, Qun Jin

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Nowadays, it has become convenient to record data related to an individual using a wearable device. However, it is difficult to utilize the data according to the individual, especially to anomaly detection. Anomaly detection is very important for healthcare, e.g., early detecting of illness. In our previous study, we proposed an approach to specifying latent factors using Structural Equation Modeling (SEM). In this paper, we propose an improved approach for anomaly detection taking account of personal status based on latent factors. To estimate the states, we adopt Hidden Markov Model (HMM). Moreover, we use Hotelling's theory to detect abnormal data statistically. By using our approach, even if states can not be explicitly obtained from a device, hidden states can be estimated to perform anomaly detection in more details.

本文言語English
ホスト出版物のタイトルProceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ137-141
ページ数5
ISBN(電子版)9781538677438
DOI
出版ステータスPublished - 2018 12 26
イベント9th International Conference on Information Technology in Medicine and Education, ITME 2018 - Hangzhou, Zhejiang, China
継続期間: 2018 10 192018 10 21

出版物シリーズ

名前Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018

Conference

Conference9th International Conference on Information Technology in Medicine and Education, ITME 2018
国/地域China
CityHangzhou, Zhejiang
Period18/10/1918/10/21

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 医学(その他)
  • 情報システム
  • 健康情報学
  • 教育

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