Hidden markov model for analyzing time-series health checkup data

Ryouhei Kawamoto*, Alwis Nazir, Atsuyuki Kameyama, Takashi Ichinomiya, Keiko Yamamoto, Satoshi Tamura, Mayumi Yamamoto, Satoru Hayamizu, Yasutomi Kinosada

*この研究の対応する著者

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

In this paper, we apply a Hidden Markov Model (HMM) to analyze time-series personal health checkup data. HMM is widely used for data having continuation and extensibility such as time-series health checkup data. Therefore, using HMM as probabilistic model to model the health checkup data is considered to be suitable, and HMM can express the process of health condition changes of a person. In this paper, a HMM with six states placed in a 2×3 matrix was prepared. We collected training features including the time-series health checkup data. Each feature consists of eight inspection parameters such as BMI, SBP, and TG. The HMM was then built using the training features. In the experiments, we built five HMMs for different gender and age conditions (e.g. male 50's) using thousands of training feature vectors, respectively. Investigating the HMMs we found that the HMMs can model three health risk levels. The models can also represent health transitions or changes, indicating the possibility of estimating the risk of lifestyle-related diseases.

本文言語English
ホスト出版物のタイトルMEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics
出版社IOS Press
ページ491-495
ページ数5
1-2
ISBN(印刷版)9781614992882
DOI
出版ステータスPublished - 2013
外部発表はい
イベント14th World Congress on Medical and Health Informatics, MEDINFO 2013 - Copenhagen, Denmark
継続期間: 2013 8 202013 8 23

出版物シリーズ

名前Studies in Health Technology and Informatics
番号1-2
192
ISSN(印刷版)0926-9630
ISSN(電子版)1879-8365

Conference

Conference14th World Congress on Medical and Health Informatics, MEDINFO 2013
国/地域Denmark
CityCopenhagen
Period13/8/2013/8/23

ASJC Scopus subject areas

  • 生体医工学
  • 健康情報学
  • 健康情報管理

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