Research on Precision Analysis and Application of Medical Big Data Driven by Data Science
Abstract
This study systematically explores the pivotal role of data science in precision analysis and application of medical big data. By
analyzing the methodological frameworks of core technologies such as data mining and machine learning, it outlines implementation pathways for medical data processing. The research identifies typical application scenarios including disease diagnosis assistance, personalized
treatment, and drug development, while providing in-depth analysis of practical challenges like data security, privacy protection, and model
accuracy along with corresponding strategies. Through concrete case studies, the feasibility and effectiveness of data science-driven medical
big data analysis are validated. Finally, the study outlines future development trends, emphasizing the importance of strengthening interdisciplinary collaboration and improving data governance systems to promote sustainable development in this field.
analyzing the methodological frameworks of core technologies such as data mining and machine learning, it outlines implementation pathways for medical data processing. The research identifies typical application scenarios including disease diagnosis assistance, personalized
treatment, and drug development, while providing in-depth analysis of practical challenges like data security, privacy protection, and model
accuracy along with corresponding strategies. Through concrete case studies, the feasibility and effectiveness of data science-driven medical
big data analysis are validated. Finally, the study outlines future development trends, emphasizing the importance of strengthening interdisciplinary collaboration and improving data governance systems to promote sustainable development in this field.
Keywords
Healthcare; Big data; Analysis; Applied research
Full Text:
PDFReferences
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[2] Zhao Jie, He Xianying. Medical Big Data Mining and Visualization [M]. Beijing: Peoples Posts and Telecommunications Press, 2023, 09.
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DOI: http://dx.doi.org/10.70711/aitr.v3i5.8356
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