General anesthesia invasive systolic pressure prediction method and system based on machine learning fusion

Abstract

The invention relates to a general anesthesia invasive systolic pressure prediction method and system based on machine learning fusion. The method comprises the following steps: S1, collecting the body feature data and vital sign data of a patient, carrying out the preprocessing of all data, and dividing the preprocessed data into a training set, a test set and a verification set; respectively performing slice sampling on the training set, the test set and the verification set to collect n training subsets, test subsets and verification subsets; s2, constructing a prediction model based on machine learning fusion, and predicting the general anesthesia invasive systolic pressure by using the prediction model; the prediction model comprises n primary learners and a secondary learner, and the output values of all the primary learners are used as the input of the secondary learner. The system comprises a database module, a data collection module, a prediction module and an interaction module. A prediction program is stored in the prediction module. According to the method, the defect of low prediction precision of a single algorithm is overcome, the input of the secondary learner is optimized, and the prediction precision is further improved.

Description

technical field [0001] The invention belongs to the technical field of medical surgical operation monitoring, and in particular relates to a method and system for predicting invasive systolic blood pressure under general anesthesia based on machine learning fusion. Background technique [0002] During general anesthesia surgery, invasive systolic blood pressure is an important basis for doctors to judge the patient's vital signs, which will directly affect the smooth progress of the operation. Accurately predicting changes in invasive systolic blood pressure during general anesthesia and making countermeasures in advance can effectively reduce the risk of surgery. risk. At present, life monitoring instruments are generally used to monitor invasive systolic blood pressure, blood oxygen, heart rate and other variables in real time during surgery. Doctors are required to artificially predict invasive systolic blood pressure based on clinical experience, and the results of artif...

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Problems solved by technology

[0004] T.H.Wu et al. (T.H.Wu, G.K.Pang and E.W.Kwong, “Predicting Systolic Blood Pressure Using Machine Learning” in 7th International Conference on Information and Automation for Sustainability, Colombo, Sri Lanka, 2014, pp.1-6.) using machine learning techniques , to predict systolic blood pressure by modeling characteristic variables such as body mass index (BMI), age, exercise, drinking and smoking conditions, using backpropagation neural network and radial basis function network to construct and verify the prediction system, the prediction system The highest prediction accuracy rate is 69.9% when the absolute error is 15mmHg, and the prediction accuracy is low

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