Data analysis methods and equipment

Abstract

The present disclosure provides a medical big data analysis method and equipment. The method includes: receiving the signs and symptoms represented by vector h; using the signs and symptoms as a feature to locate the subspace where the patient is located in the medical big data, wherein a matrix D is used to represent the case set in the big data, D=[D 1 ,D 2 ,...,D M ],D i Represents the i-th subspace, 1≤i≤M, the positioning includes solving the formula; and analyzing the probability P of the patient in a specific subspace by analyzing the semantic consistency of the subspace 1 . In addition, the probability P of a patient at a specific node can be analyzed based on the evidence transfer score on the medical knowledge graph 2 . The probability P of a patient in a specific subspace or node can also be determined according to the following formula: , where is the harmonic parameter, 0<<1. Through these solutions, not only can the accuracy of the analysis be improved, but also the condition of the patient can be analyzed according to the symptoms at the first time, so that the patient can be checked in a targeted manner, thereby reducing costs and improving efficiency.

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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