Physiological data preclinical processing method and system

Abstract

The invention discloses a physiological data preclinical processing method and system. The physiological data preclinical processing method comprises preprocessing physiological data based on the time series; carrying out association rule analysis according to the calculated abrupt change score and by means of a multidimensional abrupt change detection model and an integrated learning algorithm fused with multiple classifiers, and obtaining a disease associated network according to the result of association rule analysis; selecting a disease associated network characteristic from the disease associated network by means of an improved clustering algorithm, obtaining a disease diagnosis result according to the disease associated network characteristic and historical data, wherein the improved clustering algorithm is based on a nonnegative matrix decomposition theory and a self-learning mechanism, and extracting a corresponding connected subgraph from large graph data of the disease associated network as the disease associated network characteristic through adjusting the subgraph density. The physiological data preclinical processing method and system are advantaged in that the method and system are wide in applicability, and high in efficiency and precision, and is flexible and convenient, and can be widely applied to the field of data processing.

Description

Technical field [0001] The invention relates to the field of data processing, in particular to a method and system for preclinical processing of physiological data. Background technique [0002] In the field of conventional processing of human physiological data, the method of conservation of energy metabolism is usually used to detect and process the relevant physiological parameters of the human body and establish related mathematical models, thereby indirectly measuring a certain index of the human body. The current metabolic heat integration method data model is relatively mature, but the model can only process a certain parameter or a certain type of parameter, and cannot co-ordinate the processing of multiple data. This limitation is destined to be unsuitable for multi-purpose monitoring , Especially not suitable for the monitoring of massive data, because of its high data processing cost. [0003] Many complex diseases (such as the causes of type 2 diabetes) have not been i...

AI Technical Summary

Problems solved by technology

However, the method is based on purely medical records and is not applicable to newly diagnosed patients and medical mutational situations

[0008] In addition, most current data mining algorithms only process physiological parameters and use predefined thresholds to generate association rules, which is not efficient

[0009] However, in the process of physiological signal processing, existing machine learning algorithms cannot completely and effectively solve the problem of gene name disambiguation (the reason is that each method has its own limitations, and one method is effective for one data set but not necessarily for another. A data set is valid), leading to the fact that the existing algorithm cannot effectively control the local behavior of the algorithm during the learning process, and the accuracy is not high

Images