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

Kiichi Tago, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages137-141
Number of pages5
ISBN (Electronic)9781538677438
DOIs
Publication statusPublished - 2018 Dec 26
Event9th International Conference on Information Technology in Medicine and Education, ITME 2018 - Hangzhou, Zhejiang, China
Duration: 2018 Oct 192018 Oct 21

Publication series

NameProceedings - 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
CountryChina
CityHangzhou, Zhejiang
Period18/10/1918/10/21

Keywords

  • Anomaly detection
  • Health data
  • Hidden Markov Model (HMM)
  • Structural Equation Modeling (SEM)

ASJC Scopus subject areas

  • Computer Science Applications
  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Education

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