Post-physical examination chronic disease prognosis system based on multi-label learning

Abstract

The invention discloses a post-physical examination chronic disease prognosis system based on multi-label learning. The system comprises a data acquisition module, a data preprocessing module, a basicprediction model construction module and a local prediction module. The data acquisition module is used for acquiring physical examination data of a physical examination user; the basic prediction model construction module is used for constructing a multi-label learning model for a physical examination scene; the local prediction module comprises a local model training unit and a prediction unit,the local model training unit solidifies the trained local prediction model into the local prediction module, the prediction unit outputs prediction prognosis indexes for a plurality of chronic disease occurrence conditions, and finally the future expected occurrence time of the chronic disease is obtained. According to the system, a multi-label learning method is used, the internal relation under the chronic disease concurrency condition can be extracted, the characteristic of high concurrency of chronic diseases is better met, and accurate prediction of the chronic disease occurrence condition in the future can be better completed.

Description

technical field [0001] The invention belongs to the technical field of medical treatment and machine learning, and in particular relates to a chronic disease prognosis system after physical examination based on multi-label learning. Background technique [0002] Various chronic diseases, including diabetes, heart disease, coronary heart disease, and chronic kidney disease, have become the most important types of diseases that have caused a significant decline in the quality of life of the people and a substantial increase in the medical economic burden worldwide. Chronic diseases have the characteristics of high concealment, low awareness rate in the early stage, great harm in the later stage, and extremely low cure rate. According to the statistics of the World Health Organization (WHO), the number of deaths caused by cardiovascular disease and diabetes in the world and in 2012 was 17 million, accounting for 50.2% of non-communicable disease deaths. In the report on the pr...

AI Technical Summary

Problems solved by technology

However, the current physical examination system mainly determines whether a patient has a specific disease at the current moment based on the current examination indicators. There is a lack of systematic technical means to analyze the chronic diseases in the next few years through the current examination data and the existing chronic disease status. Prognosis

[0005] Existing physical examination clinical decision support systems that use traditional single-label machine learning methods to predict various diseases cannot extract the concurrent correlations between different chronic diseases, resulting in a decline in the accuracy of predictions, and there may be prominent discrepancies among the prediction results of multiple diseases. Medical Logical Contradiction

At present, there are very few physical examination clinical decision support systems using multi-label machine learning, and related research can only assist in the diagnosis of the disease at the current time node, but cannot predict the occurrence of chronic diseases in the future.

Images