Health management method based on large data intelligent algorithm and system

Abstract

The invention discloses a health management method based on a large data intelligent algorithm and system. The health management method comprises steps: based on evidence-based medicine, epidemiologyand crowd intervention data, a multi-layer cumulative logistic regression model is established, multi-dimensional individual health measurement indexes and a nutrition and health intervention scheme library are generated, an individual nutrition and health intervention scheme decision tree model is established by using the individual health measurement index information, and an individual nutrition and health intervention scheme is acquired. During the nutrition and health intervention scheme execution process, health assessment is carried out regularly and best intervention effects are achieved. The management system established based on the method is composed of three modules: a personal health information acquisition module, a calculation module and a display and supervision module. Through the health management method and the system, scientific, personalized and traceable health management solutions can be provided for residents.

Description

technical field [0001] The invention relates to a health management method and a management system, in particular to a health management method and system based on a big data intelligent algorithm, belonging to the technical field of health care. Background technique [0002] In recent years, with the advancement of life sciences, people's understanding of the process of life activities has become more and more in-depth, and correspondingly, the level of cognition and intervention technology for the occurrence and development of diseases has also been improved. With the development of evidence-based medicine and life sciences, people have achieved a lot of consensus on disease standards and disease types, making the disease a clear definition and classification standard, which is helpful for medical practitioners to choose appropriate means to intervene in diseases. It can delay the development of the disease or even restore the non-disease state. As we all know, the occurr...

AI Technical Summary

Problems solved by technology

At present, in the field of medical health, so far, there is no clear and feasible judgment method and consensus standard for the boundary between health and sub-health, which makes people unclear about whether they are in a sub-health state, nor can they understand the degree of risk of future development of the disease. As a result, disease prevention is often an empty ideal

[0003] In fact, at present, people's understanding of their own disease or non-disease state (including healthy state and sub-health state) comes from regular medical institution physical examination reports, and the main problems include: 1. The reference standard comes from the category of medical disease discrimination, which cannot evaluate the examinee The comprehensive health or sub-health state and degree level can only judge whether you are in a disease state, and cannot know when your own future risk of disease; 2. After the physical examination results come out, there is often a lack of indicators for abnormalities but not reaching the disease level Simple and effective coping methods, as well as correct guidance, require the comprehensive application of multidisciplinary technological advances

Images