Situation-oriented health risk identification method and system

Abstract

The invention discloses a situation-oriented health risk identification method, which belongs to the technical field of health monitoring and comprises the following steps: establishing a situation-oriented knowledge graph conceptual model for health risk identification; acquiring sensor parameters so as to establish a knowledge graph instance model for situation-oriented health risk identification; and identifying health risks based on a semantic similarity algorithm, and driving the health care robot to provide care services. The technical scheme comprehensively utilizes the technologies ofa sensor network, cloud computing, edge computing, a knowledge graph and the like, exerts the advantages of situation awareness, edge cloud quick response and the like, overcomes the semantic heterogeneity of sensors, sensor parameters, health conditions and care services, improves the accuracy of health risk identification, can actively intervene by using a health care robot in case of abnormal health conditions, finds and provides a first aid in emergency conditions, and improves the daily care quality of chronic diseases such as cardiovascular and cerebrovascular diseases.

Description

technical field [0001] The invention belongs to the technical field of health monitoring, and in particular relates to a situation-oriented health risk identification method and system. Background technique [0002] According to the "China Cardiovascular Disease Report 2018", the mortality rate of cardiovascular and cerebrovascular diseases in China is on the rise; the number of cardiovascular disease patients is 290 million, stroke is 13 million, coronary heart disease is 11 million, and pulmonary heart disease is 5 million. 4.5 million people with heart failure, 2.5 million people with rheumatic heart disease, 2 million people with congenital heart disease, and 245 million people with high blood pressure. A large number of medical practices have shown that, even if it is a sudden heart disease with cardiovascular and cerebrovascular diseases, if the subtle signs can be monitored in advance and intervention measures are taken in emergencies, 70%-80% of the patients can avoi...

AI Technical Summary

Problems solved by technology

[0010] Based on the above description, context-oriented health risk identification methods and systems are still relatively weak areas, mainly manifested in: lack of integration with the context or insufficient integration, relying on physiological parameter monitoring is difficult to accurately identify health risks; contextual data include physiological parameter monitoring Collection, health risk identification, and health care service response are an organic whole. In practical applications, different collection devices and different parameter types need to overcome the problem of semantic heterogeneity in a common knowledge graph model; existing health care services that integrate monitoring and services Robots only target patients with a single type of disease, and it is difficult for hospitals, nursing homes, and nursing centers to take care of multiple patients with different needs at the same time; health care robots need to provide appropriate and timely care services, and complex reasoning is not conducive to the first time. give first aid

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