关键词：公共卫生信息学;个人健康记录;隐马尔科夫模型;卫生检查;数据挖掘;大数据
摘 要：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.