Meng Guo
In the industry environment of rapid iteration of medical informatization and accelerated implementation of smart healthcare and precision medicine, cross-institutional and cross-system data silos have long hindered the flow of medical data elements. Semantic interoperability, as the core supporting technology for breaking down heterogeneous data barriers, has become a research hotspot in the global field of medical and health informatization. Most of the existing domestic and foreign literature focuses on standard sorting and theoretical review, lacking systematic mathematical modeling, comparative experimental verification, and engineering implementation code. The problem of disconnection between theoretical research and clinical engineering implementation is prominent. This article is based on the hierarchical theoretical system of semantic interoperability, systematically elaborating on the basic principles of medical semantic interoperability, domestic and foreign standard theoretical frameworks, and mainstream key technical mechanisms. On this basis, a complete experimental system was built around three core innovative points: the hierarchical weighted mixed semantic similarity model, the lightweight federated privacy semantic interaction architecture, and the cross-standard automated mapping scheme for Chinese and Western medical terminology. Eleven mathematical calculation formulas were introduced to quantify semantic matching logic, and six sets of comparative data tables and five result visualization charts were designed. Python engineering implementation code was used to complete the empirical research from the entire process of dataset partitioning, experimental environment construction, control experiment design, actual measurement result statistics, and multidimensional data analysis. The research aims to improve the theoretical shortcomings of semantic interoperability in the context of the integration of traditional Chinese and Western medicine in China. The performance advantages of the self-developed algorithm compared to traditional matching schemes are verified at the experimental level. The actual test results confirm that the innovative model proposed in this paper can effectively solve the three major industry problems of heterogeneous multi-standard terms, cross-hospital data semantic misalignment, and privacy security and data-sharing conflicts in China. It not only enriches the basic theoretical system of medical semantic interoperability but also provides theoretical references, mathematical models, and reusable engineering solutions for the implementation of data interoperability projects in medical institutions at all levels in China.