Abstract
As we move forwards to Healthcare Industry 4.0 era, more and more high accuracy and technological devices are applied into medical field, for the better services in health domain. However, the traditional storage methods of scaled data limit the application of data analysis. Besides, electronic medical data (EMR) and electronic health data (EHR), unstructured data including MRIs, CT scans, X-ray and PET scans are the fastest growing part of medical data. Due to different regulations between structured and unstructured data, and among various unstructured imaging data, though the traditional data warehouses can solve the problems of scalability and data fragmentation, the cost of solution is high and it is not real-time. The multimodal data are separated and this ineffective data storage is insufficient to enable further efficient hybrid query.
Therefore, to solve the fragmentation of multimodal data storage and enable hybrid query for further data analytics services, this paper proposes a high-efficiency heterogenous medical data fusion framework (HMDFF) for multimodal and heterogeneous data in medical field based on data lake. This framework aims to fuse the fragmented medical data and provide users with effective management methods and user-friendly interfaces to perform hybrid query.
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This work was supported by Institute of Precision Medicine, Tsinghua University.
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Ren, P. et al. (2021). HMDFF: A Heterogeneous Medical Data Fusion Framework Supporting Multimodal Query. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_23
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DOI: https://doi.org/10.1007/978-3-030-90885-0_23
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