Method and system for predicting curative effect of taVNS therapy

Abstract

The invention discloses a curative effect prediction method and system for taVNS therapy. The method comprises the following steps: preprocessing collected multi-dimensional data related to a patient; extracting data features from the preprocessed data through convolution operation; inputting the extracted data features into a prediction model for prediction; and processing a prediction result output by the prediction model through a multi-layer perceptron to obtain a simplified prediction result. According to the method, a curative effect prediction model is established according to the multi-dimensional nerve electrophysiological characterization, the disease specificity clinical characterization and quantifiable traditional Chinese medicine characteristic indexes of a patient, and a prediction model for crowds effective for taVNS therapy is finally fitted, so that a doctor can be helped to judge whether the patient is effective or not after taVNS therapy and predict the disease improvement degree of taVNS during clinical decision making. Therefore, non-medical personnel are helped to predict the taVNS curative effect to a greater extent, and home intelligent self-care medical treatment is better realized.

Description

technical field [0001] The invention relates to the fields of big data and medical technology, in particular to a method and system for predicting curative effect of taVNS therapy. Background technique [0002] Auricular point therapy is an important part of acupuncture and moxibustion in traditional Chinese medicine. It is closely related to the meridians and viscera of the whole body. Transcutaneous auricular vagus nerve stimulation) restores the body's homeostasis balance by regulating the central-autonomic nervous system function, and has significant curative effects in the treatment of epilepsy, insomnia, depression, functional gastrointestinal diseases, Parkinson's, and cerebrovascular diseases. Under the background of the times, the intelligent, convenient and efficient medical model provides a safe, non-invasive, comfortable, wearable, easy-to-operate, and new therapy that can realize home treatment, which has been gradually promoted and used in clinical practice. ...

AI Technical Summary

Problems solved by technology

Although the existing curative effect prediction scheme based on artificial intelligence technology has solved the limitations of traditional schemes, there are still problems that cannot be ignored

The first is to predict the curative effect of difficult diseases such as malignant tumors, and lack of curative effect prediction for common diseases such as epilepsy, insomnia, depression, functional gastrointestinal diseases, Parkinson's disease, and cerebrovascular diseases

The second is to focus on data analysis, so it is easy to ignore the key medical information of the disease, and the information collection is not very targeted, or it is only one-dimensional information, which cannot fully reflect the treatment status, making the prediction accuracy difficult to meet expectations

The third is that traditional machine learning has limited ability to fit high-dimensional nonlinear data, and requires relatively high computing power and weak generalization ability of equipment, resulting in a corresponding increase in cost, and it is difficult to obtain reliable predictions, so it is difficult to be widely used

Fourth, it focuses on the analysis of historical disease records, so the real-time performance is not high

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