多源异构医疗大数据融合与分析技术

doi:10.6040/j.issn.1672-3961.0.2025.194

Abstract

Abstract: Healthcare data, as a core element of modern medical research and practice, was characterized by multi-source heterogeneity, fragmentation, and low utilization rate, making it difficult to effectively uncover the implicit correlations and knowledge value. Overcoming the integration challenges of multi-source heterogeneous data became a key obstacle in shifting health management from passive treatment to proactive intervention. Focusing on the core value and integration difficulties of healthcare data, the research progress and technological breakthroughs were systematically reviewed. Through comprehensive analysis of multimodal and multi-source heterogeneous data fusion, interpretable knowledge discovery, and cross-modal correlation mining, an advanced technological framework for multi-source heterogeneous medical big data was innovatively proposed. This framework supported the triple evolution of healthcare data systems toward multi-modal transformation, knowledge graph upgrading, and interpretability innovation, thereby fully unleashing the multiplier effect of healthcare data as a national strategic resource.

Key words: medical big data, multi-source heterogeneity, multimodal fusion, data driven and knowledge guided, interpretable model

CLC Number:

TP391

CUI Lizhen, SUN Xiaofang, LIU Ning, XU Yonghui, HE Wei. Multi-source heterogeneous medical big data fusion and analysis technology[J].Journal of Shandong University(Engineering Science), 2026, 56(1): 1-13.

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