Graded triage method and system based on machine learning and computer equipment

Abstract

The invention relates to a grading triage method and system based on machine learning, and the method comprises the steps: receiving questionnaires filled by target patients to be subjected to grading triage, and summarizing the answers of the questionnaires; extracting disease characteristics of the target patient from answers of the questionnaire, wherein the disease characteristics comprise symptoms, disease onset duration, body temperature, heart rate, systolic pressure, diastolic pressure, gender and age; preprocessing the disease features of the target patient according to a preset feature preprocessing strategy to obtain target disease features represented in a digital form, the target disease features including gender, age, symptom, onset time, body temperature, pulse pressure difference, heart rate level, systolic pressure level, danger level, and critical level; inputting the target disease feature into a preset grading triage model, and obtaining the grading information of the target patient. According to the method and system, various body factors of the patient and the stealth problem of diseases are integrated, and disease grades of the patient are accurately divided based on machine learning.

Description

Technical field [0001] The present invention relates to the field of hierarchical distinction, and more particularly to a macro-learning-based hierarchical method, system, and computer equipment. Background technique [0002] At present, patients go to the hospital for medical treatment, and the hospital's nurses will be guided by the patient's symptoms, initially determine the patient's response to the patient's patient's disease level to place the hospital resources. However, manual grading diagnosis, low efficiency, lack of human resources, no solving this problem, there is an intelligent emergency classification system in the prior art, and the emergency patient is graded, and the server is stored patients with each emergency patient. Registration information; the first client obtains the corresponding patient registration information according to the patient's condition data from the server, and associates the condition data to the corresponding patient registration informat...

AI Technical Summary

Problems solved by technology

However, manual grading and triage is inefficient and lacks human resources. This problem has not been solved. In the prior art, there is an intelligent emergency grading and triage system, which performs grading diagnosis on emergency patients, and the server saves the patient records of each emergency patient. Registration information: the first client obtains the corresponding patient registration information from the server according to the condition data of emergency patients, associates the condition data with the corresponding patient registration information to form associated data, and assigns grades to the corresponding associated data according to the condition data send back to the server

However, the above-mentioned system only uses the common customer service terminal and server docking, and only adopts the form of threshold to classify and triage. Although it can solve some human resource problems, the grading method is too mechanized and does not comprehensively consider the various aspects of patients. Physical factors and hidden problems of the disease, such as the patient's age, gender, onset time, etc., so the above-mentioned grading and triage levels are unscientific and have limitations

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