Rapid stroke identification method combining TOPSIS and artificial intelligence

Abstract

The invention relates to a rapid stroke identification method combining TOPSIS and artificial intelligence, and the method comprises the steps: firstly, judging whether the single-side muscle force decline exists or not through the horizontal lifting degree of the two arms of a user, and carrying out the classification of the muscle force difference through the angle between the arms; analyzing the degree of unclear mouth and teeth of the user through artificial intelligence speech recognition in combination with a word error rate calculation method; detecting position coordinates of face feature points by using an artificial intelligence face feature point recognition model, calculating a face droop degree through a TOPSIS method, and finally quickly recognizing the stroke risk degree through the TOPSIS method by synthesizing muscle force difference classification, an unclear degree of mouth and teeth and a face droop degree. According to the invention, the patient and the family members thereof can effectively and quickly identify the stroke symptom at home, and the stroke patient is automatically shunted to the corresponding stroke center for treatment according to different severity degrees of the stroke, so that the first-aid efficiency and quality of emergency medical aid service are improved.

Description

technical field [0001] The invention relates to a rapid recognition method for stroke on a mobile terminal, in particular to a rapid recognition method for stroke based on TOPSIS and artificial intelligence. Background technique [0002] According to the "China Stroke Prevention Report", stroke is the number one cause of life loss in my country. Relevant data show that among the people who died of diseases in my country, at least 1 out of every 5 people died of stroke. Stroke is not only the number one cause of death among Chinese adults, but also the number one cause of disability, bringing heavy blows and burdens to individuals, families, and society. However, in the current treatment of stroke, there are still two bottlenecks that need to be solved urgently, the difficulty of pre-hospital identification and the long time of pre-hospital transfer. These two issues are related to the pre-hospital emergency time (OTT) of acute ischemic stroke. Studies have shown that every...

AI Technical Summary

Problems solved by technology

However, most of the existing machine learning-based work requires many cases to improve the accuracy of the model, and the model formed based on the existing cases may have the problem of over-fitting, which makes the generated machine learning model not suitable for new Rapid identification of stroke risk

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