Innovative Design of Medical Big Data Platform Integrating Machine Learning and Knowledge Graph

DOI

10.2991/978-94-6463-242-2_76How to use a DOI?

Keywords

Medical big data; Platform Design; Machine Learning; Knowledge Graph

Abstract

With the rapid development of medical information technology, the amount of medical data continues to increase, and the data structure becomes increasingly complex. How to efficiently process and utilize this data to improve the quality and efficiency of medical services has become an important issue. This article proposes an innovative design for a medical big data platform that integrates machine learning and knowledge graph, using large-scale language models and deep learning models to conduct deep analysis and mining of medical text, images, and other data; Adopting a knowledge graph based medical data integration method to build a sustainable medical big data ecosystem. By transforming medical data from different sources and categories into a unified knowledge representation, the integration, storage, management, analysis, and mining of medical data can be achieved. The research results will provide more accurate, faster, and effective data decision-making support for applications such as hospital management, clinical treatment, and scientific research and teaching.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jun Wang
AU  - Ai-Rong Yu
PY  - 2023
DA  - 2023/09/22
TI  - Innovative Design of Medical Big Data Platform Integrating Machine Learning and Knowledge Graph
BT  - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
PB  - Atlantis Press
SP  - 620
EP  - 627
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-242-2_76
DO  - 10.2991/978-94-6463-242-2_76
ID  - Wang2023
ER  -