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

    研究成果: Conference contribution

    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.

    元の言語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
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ137-141
    ページ数5
    ISBN(電子版)9781538677438
    DOI
    出版物ステータス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
    China
    Hangzhou, Zhejiang
    期間18/10/1918/10/21

    Fingerprint

    Hidden Markov models
    Health
    Equipment and Supplies
    health
    Delivery of Health Care
    illness

    ASJC Scopus subject areas

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

    これを引用

    Tago, K., & Jin, Q. (2018). Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM. : Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018 (pp. 137-141). [8589272] (Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITME.2018.00040

    Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM. / Tago, Kiichi; Jin, Qun.

    Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 137-141 8589272 (Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018).

    研究成果: Conference contribution

    Tago, K & Jin, Q 2018, Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM. : Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018., 8589272, Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018, Institute of Electrical and Electronics Engineers Inc., pp. 137-141, 9th International Conference on Information Technology in Medicine and Education, ITME 2018, Hangzhou, Zhejiang, China, 18/10/19. https://doi.org/10.1109/ITME.2018.00040
    Tago K, Jin Q. Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM. : Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 137-141. 8589272. (Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018). https://doi.org/10.1109/ITME.2018.00040
    Tago, Kiichi ; Jin, Qun. / Detection of Anomaly Health Data by Specifying Latent Factors with SEM and Estimating Hidden States with HMM. Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 137-141 (Proceedings - 9th International Conference on Information Technology in Medicine and Education, ITME 2018).
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