Main diagnosis and main operation matching detection method and system based on federal association rule mining

Abstract

The invention discloses a main diagnosis and main operation matching detection method and system based on federal mining, and the method comprises the steps: carrying out the mining of the cross combination of operation items and diagnosis items at each medical institution end based on the operation frequency and the diagnosis frequency, and uploading the related data of a mining result to a central node end; carrying out support degree, confidence degree and improvement degree analysis on the mining result related data at a central node end, and screening a matching rule pair of main diagnosis and main operation from the mining result related data according to an analysis result to construct a rule database; during application, clinical matching detection of the main diagnosis and the main operation is carried out by utilizing the matching rules in the rule database. According to the method and the system, mining and discovery of association rules are carried out by efficiently, safely and compliantly using massive medical record home page data of multiple medical institutions, so that the clinical matching between the main diagnosis and the main operation in the medical record home page is automatically detected.

Description

technical field [0001] The invention belongs to the field of medical record quality control, in particular to a method and device for matching detection of main diagnosis and main operation based on federated mining. Background technique [0002] Association rule mining is a common unsupervised machine learning method, which uses metrics such as support, confidence, and lift to mine associations in data, which are unknown and hidden in advance. Apriori and FP-growth are common association rule mining algorithms, but in the face of big data or massive data, they have less room to play. In order to effectively use massive data, researchers have also developed a variety of distributed association algorithms, such as Parallel FP-Growth (PFP), Fast Distributed Association Rules Mining (Fast Distributed Association Rules Mining, FDM) and Distributed Mining of Association rules (distributed association rule mining, DMA), etc. [0003] However, these distributed association algori...

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Problems solved by technology

[0003] However, these distributed association algorithms do not take into account data security issues

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