| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 102 === Survival rate of cancer patient has recently been greatly raised due to the advances of clinical technology. With the aging population, elder people recovering from cancer ac-count for large portion in the society. According to the research literatures, elderly can-cer survivors (ECSs) are afraid of the cancer recurrence, and suffer from geriatric syn-dromes with high risk. Thus, there is an increasing need for ECSs to be aware of their health status at home environment. However, most of current home monitoring systems use common medical standard to identify the hazardous situation without referring to ESC-centric personalized data analysis. Besides, those vital-sign-based risk analysis systems are able to estimate the occurrence of certain disease since multivariate clinical information such as medical diagnosis and disease history are available, whereas they are cannot estimate the overall health status for ECSs. Therefore, in this thesis we propose an ECS-centric health monitoring and its preliminary estimating system. The underlying analysis technique of the system is to utilize dynamic Bayesian network (DBN) to esti-mate ECS’s preliminary health condition. Specifically, after relevant features are ex-tracted from vital sign of elders, the developed DBN is able to first learn the temporal pattern of those features under various corresponded health situation, and then further estimate their current preliminary health statuses (e.g. poor or good). To verify the plau-sibility of our proposed system, two elderly survivors with breast cancer history are in-vited to apply the system for about 3 weeks, and the analysis results show that the abil-ity of estimation and friendliness of our system are quite promising.
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