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.
C. Zhou—This research was partially supported by the Project of Shandong Province Higher Educational Science and Technology Program (No. J12LN05); the grants from the National Natural Science Foundation of China (No. 61202111, 61273152, 61303017); the Project Development Plan of Science and Technology of Yantai City (No. 2013ZH092); the Doctoral Foundation of Ludong University (No. LY2012023); the US National Library of Medicine (No. R01LM009239); the Natural Science Foundation of Shandong Province China (No. ZR2011GQ001); and Scientific Research Foundation for Returned Scholars of Ministry of Education of China (43th).
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Zhou, C., Li, A., Zhang, Z., Zhang, Z., Qu, H. (2020). A Cloud-Based Platform for ECG Monitoring and Early Warning Using Big Data and Artificial Intelligence Technologies. In: Nah, Y., Kim, C., Kim, SY., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020 International Workshops. DASFAA 2020. Lecture Notes in Computer Science(), vol 12115. Springer, Cham. https://doi.org/10.1007/978-3-030-59413-8_5
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