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Big Data Mining on Health Informatics Data for Cities
23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021 ; : 1720-1727, 2022.
Article in English | Scopus | ID: covidwho-1909207
ABSTRACT
Advancements in modern technologies has generated and collected very large volumes of data at a rapid rate. Embedded in these big data is implicit, previously unknown and potentially useful information and knowledge. This explains why big data are often considered as a new oil. Discovered knowledge may help cities to enhance performance and well-being, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens. To elaborate, discovered knowledge from digital technologies may support urban and transportation analytics for smart cities. Discovered knowledge from healthcare data and disease reports may support and enhance decision or policy making for the well-being of citizens within a city. For example, analyzing and mining health informatics data - such as COVID-19 epidemiological data - for cities help decision markers get a better understanding of the disease and come up with ways to detect, control and combat the disease. It also help them prepare for the needs of their citizens (e.g., needs for hospital beds in regular wards or ICU, needs of patients of different age groups). Hence, in this paper, we present a solution for big data mining on health informatics data for cities. Specifically, we mine COVID-19 epidemiological data with spatial and demographic hierarchies capturing characteristics of COVID-19 patients. Evaluation on real-life COVID-19 data demonstrates the practicality of our solution. © 2021 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Reviews Language: English Journal: 23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Reviews Language: English Journal: 23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021 Year: 2022 Document Type: Article