Acute coronary syndrome early warning method and device based on metabonomics data

Abstract

The invention discloses an acute coronary syndrome early warning method and device based on metabonomics data. The method comprises the following steps: acquiring metabolite data of a normal coronaryartery biological individual and metabolite data of an acute coronary syndrome biological individual; screening out differential metabolite data; according to the differential metabolite data, carrying out machine learning on an LightGBM model to obtain an acute coronary syndrome early warning model, wherein the acute coronary syndrome early warning model comprises multiple biomarkers used for diagnosing the acute coronary syndrome, according to the acute coronary syndrome early warning model, obtaining an index value of each biomarker corresponding to the to-be-diagnosed biological individual, judging whether the index value of each biomarker exceeds a preset normal value range or not, and outputting acute coronary syndrome early warning information of the to-be-diagnosed biological individual according to a judgment result. Acute coronary syndrome early warning can be performed on the patient according to the metabolite data of the patient so that non-invasive early warning can be realized.

Description

technical field [0001] The invention relates to the field of machine learning applications, in particular to a method and device for early warning of acute coronary syndrome based on metabolomics data. Background technique [0002] This section is intended to provide a background or context to embodiments of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] Coronary artery disease (CAD), also known as coronary atherosclerotic heart disease, is a metabolic disorder caused by both genes and the environment, and has become one of the leading causes of death in both developed and developing countries. According to its clinical symptoms, arterial obstruction and degree of myocardial damage, CAD can be divided into different categories: stable coronary artery disease (sCAD), unstable angina pectoris (UA) and acute myocardial infarction (acute myocardial infarction). infarction, AMI), amon...

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Problems solved by technology

[0007] An embodiment of the present invention provides a method for early warning of acute coronary syndrome based on metabolomics data to solve the technical problem that the prior art cannot realize early warning of acute coronary syndrome based on metabolite data. The method includes : Obtain the metabolite data of normal coronary artery individual and acute coronary syndrome individual; screen out differential metabolite data based on the metabolite data of normal coronary artery individual and acute coronary syndrome individual; Data, machine learning is performed on the LightGBM model, and the acute coronary syndrome early warning model is obtained, wherein the acute coronary syndrome early warning model includes: a variety of biomarkers for the diagnosis of acute coronary syndrome; according to the acute coronary syndrome Sign early warning model, obtain the index value corresponding to each biomarker of the individual to be diagnosed, and judge whether the index value of each biomarker corresponding to the individual to be diagnosed exceeds the preset normal value range, and output the to-be-diagnosed value according to the judgment result Early warning information of acute coronary syndrome in biological individuals

[0008] The embodiment of the present invention also provides an early warning device for acute coronary syndrome based on metabolomics data, which is used to solve the technical problem that the prior art cannot realize early warning for acute coronary syndrome based on metabolite data. Including: metabolite data acquisition module, used to obtain metabolite data

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