Abstract
In the medical field, the rapid growth of medical equipment produced a large amount of medical data which has a wide range of sources and complex structures. Besides, medical data contains essential information that contributes to data exploration. However, the existing platforms based on Data Warehouse or Data Lake cannot effectively integrate more comprehensive multi-source heterogeneous medical data and efficiently manage large-scale multi-modal medical data. This paper presents a Multi-source Heterogeneous Data of Medical Lakehouse (MHDML), the platform that integrates multiple pieces of open-source software reasonably to integrate more comprehensive multi-source heterogeneous medical data. Multi-modal data fusion is an important method of the platform to improve multi-modal data management in the medical field. Finally, we customize Restful APIs for medical data exploration tasks. Based on the real data of sepsis and knee osteoarthritis, the platform realizes more comprehensive multi-source heterogeneous medical data acquisition and effective multi-modal medical data management, providing simple operations and visual data exploration functions for medical staff.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., Ma, J.: A framework for cardiac arrhythmia detection from IoT-based ECGs. World Wide Web 23(5), 2835–2850 (2020). https://doi.org/10.1007/s11280-019-00776-9
Kalkman, S., Mostert, M., Udo-Beauvisage, N., Van Delden, J., Van Thiel, G.: Responsible data sharing in a big data-driven translational research platform: lessons learned. BMC Med. Inform. Decis. Mak. 19(1), 1–7 (2019)
Armbrust, M., Ghodsi, A., Xin, R., Zaharia, M.: Lakehouse: a new generation of open platforms that unify data warehousing and advanced analytics. In: Proceedings of CIDR (2021)
Farooqui, N.A., Mehra, R.: Design of a data warehouse for medical information system using data mining techniques. In: 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 199–203. IEEE (2018)
Neamah, A.F.: Flexible data warehouse: towards building an integrated electronic health record architecture. In: 2020 International Conference on Smart Electronics and Communication (ICOSEC), pp. 1038–1042. IEEE (2020)
Spengler, H., Gatz, I., Kohlmayer, F., Kuhn, K.A., Prasser, F.: Improving data quality in medical research: a monitoring architecture for clinical and translational data warehouses. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 415–420. IEEE (2020)
Khan, M.Z., Kidwai, M.S., Ahamad, F., Khan, M.U.: Hadoop based EMH framework: a big data approach. In: 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1068–1070. IEEE (2021)
Maini, E., Venkateswarlu, B., Gupta, A.: Data lake-an optimum solution for storage and analytics of big data in cardiovascular disease prediction system (2018)
Mesterhazy, J., Olson, G., Datta, S.: High performance on-demand de-identification of a petabyte-scale medical imaging data lake. arXiv preprint arXiv:2008.01827 (2020)
Melchor-Uceda, I.A., Olivares-Rojas, J.C., Gutiérrez-Gnecchi, J.A., García-Ramírez, M.C., Reyes-Archundia, E., Téllez-Anguiano, A.C.: Data ingestion system for interoperability and integration of hospital data online and in real time. In: 2021 Mexican International Conference on Computer Science (ENC), pp. 1–5. IEEE (2021)
Ren, P., et al.: MHDP: an efficient data lake platform for medical multi-source heterogeneous data. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds.) WISA 2021. LNCS, vol. 12999, pp. 727–738. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87571-8_63
Oreščanin, D., Hlupić, T.: Data lakehouse - a novel step in analytics architecture. In: 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), pp. 1242–1246. IEEE (2021)
Begoli, E., Goethert, I., Knight, K.: A lakehouse architecture for the management and analysis of heterogeneous data for biomedical research and mega-biobanks. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 4643–4651. IEEE (2021)
Zhang, Y., et al.: HKGB: an inclusive, extensible, intelligent, semi-auto-constructed knowledge graph framework for healthcare with clinicians’ expertise incorporated. Inf. Process. Manage. 57(6), 102324 (2020)
Acknowledgements
This work was supported by National Key R &D Program of China (2020AAA0109603) and Foundation of University Young Key Teacher of Henan Province (2019GGJS040, 2020GGJS027).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xiao, Q. et al. (2022). MHDML: Construction of a Medical Lakehouse for Multi-source Heterogeneous Data. In: Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L. (eds) Health Information Science. HIS 2022. Lecture Notes in Computer Science, vol 13705. Springer, Cham. https://doi.org/10.1007/978-3-031-20627-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-20627-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20626-9
Online ISBN: 978-3-031-20627-6
eBook Packages: Computer ScienceComputer Science (R0)