Kawamoto Ryouhei, Nazir Alwis, Kameyama Atsuyuki, Ichinomiya Takashi, Yamamoto Keiko, Tamura Satoshi, Yamamoto Mayumi, Hayamizu Satoru, Kinosada Yasutomi
Graduate School of Engineering, Gifu University, Japan.
Stud Health Technol Inform. 2013;192:491-5.
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.
在本文中，我们应用隐马尔可夫模型（HMM）来分析时间序列个人健康检查数据。HMM被广泛用于具有连续性和可扩展性的数据，如时间序列健康检查数据。因此，使用HMM作为概率模型对健康检查数据进行建模被认为是合适的，并且HMM可以表达一个人的健康状况变化过程。在本文中，准备了一个具有六个状态的HMM，这些状态排列在一个2×3矩阵中。我们收集了包括时间序列健康检查数据在内的训练特征。每个特征由八个检查参数组成，如BMI、SBP和TG。然后使用训练特征构建HMM。在实验中，我们分别使用数千个训练特征向量为不同性别和年龄条件（如50多岁男性）构建了五个HMM。通过对这些HMM的研究，我们发现这些HMM可以对三种健康风险水平进行建模。这些模型还可以表示健康转变或变化，表明有可能估计与生活方式相关疾病的风险。
