Medical big data analysis method and apparatus

Abstract

The disclosure provides a medical big data analysis method and apparatus. The method comprises: a sign symptom expressed by a vector h is received; sub space in which a patient is located in medical big data is located by using the sign symptom as a feature, wherein a matrix D is used for expressing a case set in the big data, D is equal to [D1, D2, ...,DM], the Di expresses ith sub space and thei is larger than or equal to 1 and is less than or equal to M, and the location includes calculation of a formula: h=DX; and semantic consistency of the sub space is analyzed to analyze the probability P1 of location of the patient in the specified sub space. In addition, on the basis of an evidence transfer score on a medical mapping knowledge domain, the probability P2 of location of the patientat a specified node is analyzed. The probability P of location of the patient in specified sub space or at a specified node is determined according to a formula: P=alpha+P1+(1-alpha)*P2, wherein thealpha expresses a harmonic parameter and is larger than 0 and is less than 1. Therefore, the accuracy of the analysis can be improved; and the condition of the patient can be analyzed based on the symptom at first time and thus the patient can be checked in an oriented manner, so that the cost is lowered and the efficiency is improved.

Description

technical field [0001] The present disclosure relates to a data analysis method and device, and more specifically, to a data analysis method and device based on medical big data and medical knowledge graph. Background technique [0002] According to a new study by the Advisory Board Company, the cost of health care facilities for the general public will increase by 5% per year over the next decade. Therefore, in order to survive and develop, medical institutions should find ways to reduce costs by 20%. An effective way to achieve this goal is to use big data analysis technology to detect major diseases as early as possible. [0003] As we all know, if the warning signs of major diseases can be found in the early stage, the treatment process will be much simpler and cheaper than later detection, and the patient's recovery effect will be better. According to the EMC report, 22% of medical institutions use data analysis technology to improve the early detection of serious dis...

AI Technical Summary

Problems solved by technology

However, there are two shortcomings in this method: 1) It is necessary to establish a model for each disease type, and when the patient sees a doctor, each model needs to be used for prediction, which is very inefficient; 2) Because the prediction of different disease types is usually Different detection indicators are required, so patients are required to undergo some unnecessary tests, which increases medical costs

At the same time, such practices usually use detection indicators as features, and cannot make full use of other useful information such as patients' self-reported symptoms

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