Efficient and scalable patients clustering based on medical big data in cloud platform
Abstract With the outbreak and popularity of COVID-19 pandemic worldwide, the volume of patients is increasing rapidly all over the world, which brings a big risk and challenge for the maintenance of public healthcare. In this situation, quick integration and analysis of the medical records of patie...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-09-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-022-00324-3 |
| _version_ | 1811265122583183360 |
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| author | Yongsheng Zhou Majid Ghani Varzaneh |
| author_facet | Yongsheng Zhou Majid Ghani Varzaneh |
| author_sort | Yongsheng Zhou |
| collection | DOAJ |
| description | Abstract With the outbreak and popularity of COVID-19 pandemic worldwide, the volume of patients is increasing rapidly all over the world, which brings a big risk and challenge for the maintenance of public healthcare. In this situation, quick integration and analysis of the medical records of patients in a cloud platform are of positive and valuable significance for accurate recognition and scientific diagnosis of the healthy conditions of potential patients. However, due to the big volume of medical data of patients distributed in different platforms (e.g., multiple hospitals), how to integrate these data for patient clustering and analysis in a time-efficient and scalable manner in cloud platform is still a challenging task, while guaranteeing the capability of privacy-preservation. Motivated by this fact, a time-efficient, scalable and privacy-guaranteed patient clustering method in cloud platform is proposed in this work. At last, we demonstrate the competitive advantages of our method via a set of simulated experiments. Experiment results with competitive methods in current research literatures have proved the feasibility of our proposal. |
| first_indexed | 2024-04-12T20:17:05Z |
| format | Article |
| id | doaj.art-4ff108ebc7464c4bb38f0c96a847b6a5 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-12T20:17:05Z |
| publishDate | 2022-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-4ff108ebc7464c4bb38f0c96a847b6a52022-12-22T03:18:05ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2022-09-0111111010.1186/s13677-022-00324-3Efficient and scalable patients clustering based on medical big data in cloud platformYongsheng Zhou0Majid Ghani Varzaneh1Dongseo University Graduate School of DesignDepartment of Electrical and Electronics Engineering, Shiraz University of TechnologyAbstract With the outbreak and popularity of COVID-19 pandemic worldwide, the volume of patients is increasing rapidly all over the world, which brings a big risk and challenge for the maintenance of public healthcare. In this situation, quick integration and analysis of the medical records of patients in a cloud platform are of positive and valuable significance for accurate recognition and scientific diagnosis of the healthy conditions of potential patients. However, due to the big volume of medical data of patients distributed in different platforms (e.g., multiple hospitals), how to integrate these data for patient clustering and analysis in a time-efficient and scalable manner in cloud platform is still a challenging task, while guaranteeing the capability of privacy-preservation. Motivated by this fact, a time-efficient, scalable and privacy-guaranteed patient clustering method in cloud platform is proposed in this work. At last, we demonstrate the competitive advantages of our method via a set of simulated experiments. Experiment results with competitive methods in current research literatures have proved the feasibility of our proposal.https://doi.org/10.1186/s13677-022-00324-3Cloud computingMedical big dataPatients clusteringData integrationPrivacy |
| spellingShingle | Yongsheng Zhou Majid Ghani Varzaneh Efficient and scalable patients clustering based on medical big data in cloud platform Journal of Cloud Computing: Advances, Systems and Applications Cloud computing Medical big data Patients clustering Data integration Privacy |
| title | Efficient and scalable patients clustering based on medical big data in cloud platform |
| title_full | Efficient and scalable patients clustering based on medical big data in cloud platform |
| title_fullStr | Efficient and scalable patients clustering based on medical big data in cloud platform |
| title_full_unstemmed | Efficient and scalable patients clustering based on medical big data in cloud platform |
| title_short | Efficient and scalable patients clustering based on medical big data in cloud platform |
| title_sort | efficient and scalable patients clustering based on medical big data in cloud platform |
| topic | Cloud computing Medical big data Patients clustering Data integration Privacy |
| url | https://doi.org/10.1186/s13677-022-00324-3 |
| work_keys_str_mv | AT yongshengzhou efficientandscalablepatientsclusteringbasedonmedicalbigdataincloudplatform AT majidghanivarzaneh efficientandscalablepatientsclusteringbasedonmedicalbigdataincloudplatform |