A multi-center knowledge graph joint decision support method and system

Abstract

The invention discloses a multi-center knowledge graph joint decision support method and system, which utilizes medical knowledge graph technology and block chain technology, and realizes local semantic reasoning and analysis of clinical data by combining local knowledge graphs and on-chain synchronous graphs. The results are summarized on the chain, so that when the original medical data does not leave the hospital, the fragmented cross-institutional medical data of patients is integrated by the knowledge graph technology, and based on deductive reasoning and evidence-based medicine, an interpretable clinical evidence containing complete patient clinical evidence is provided. clinical decision support. The invention performs anonymization and comparison of patient identity information, ensures complete encryption of data in out-of-hospital links and matching links, and performs encoding mapping and asymmetric encryption on triple node structure information to ensure the safety of data in the transmission process and that only the Permissioned participating centers can decrypt data, effectively ensuring data security and privacy protection in the multi-center joint reasoning process.

Description

technical field [0001] The present invention relates to the technical field of medical knowledge graphs, in particular to a multi-center knowledge graph joint decision support method and system. Background technique [0002] In clinical practice, many patients will see a doctor in multiple hospitals or community hospitals; research shows that 20%-40% of patients will see a doctor in an average of 2 different hospitals within a year. Patients' cross-institution visits will result in fragmentation and isolation of medical data, resulting in incomplete and insufficient patient records in a single hospital; incomplete patient medical data can easily lead to inaccurate clinical decisions by clinicians, resulting in inaccurate clinical decisions. Behaviors such as untimely, inappropriate treatment and repeated medical treatment seriously threaten the quality of medical services and increase the medical burden of the masses. Existing studies have found that incomplete medical data...

AI Technical Summary

Problems solved by technology

[0004] Existing technologies that use multi-center fragmented medical data for decision support have the following problems: (1) Existing technologies only use multi-center medical data in the model training stage, and the trained model can only be used in clinical practice. The patient data of a single hospital is analyzed, and the decision support results still lack the medical data of other hospitals as an auxiliary, and there are still problems in the comprehensiveness and reliability of the generated clinical decisions

(2) The decision support model built based on distributed machine learning, its results are mainly reflected in the form of confidence weight, it cannot give deductive decision support results based on evidence-based medicine, and it is difficult to systematically and comprehensively display decision support related information Patient disease risk factors and clinical evidence, likely to cause low acceptance by doctors

(3) Deductive semantic reasoning technology based on evidence-based medicine. Its distributed algorithm is mainly used for distributed data search and to improve the speed of triplet reasoning; for clinical scenarios of fragmented patient data analysis and application, data security is lacking And privacy protection support, semantic reasoning cannot be performed without summarizing multi-center raw data, and there are still problems in medical data security

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