Abstract
The prevalence of heart failure is increasing and is among the most costly diseases to society. Early detection of heart disease would provide the means to test lifestyle and pharmacologic interventions that may slow disease progression. However, the massive medical data have the following characteristics: real-time, high frequency, multi-source, heterogeneous, complex, random and personality. All of these factors make it very difficult to detect heart disease timely and make heart-warning signals accurately. So big data and artificial intelligence technologies are introduced to the field of health care, in order to discover all kinds of diseases and syndromes, and excavate valuable information to provide systematic decision-making for the diagnosis and treatment of heart. A cloud-based platform for ECG monitoring and early warning - HeartCarer is created, including a personalized data description model, the evaluation strategy of physiological indexes, and warning methods of trend-similarity about data flow. The proposed platform is particularly appropriate to address the early detection and warning of heart, which can provide users with efficient, intelligent and personalized services.
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