Disease prediction method based on automatic medical specialist knowledge extraction

Abstract

The invention relates to a disease prediction method based on automatic medical specialist knowledge extraction, and belongs to the technical field of intelligent medical treatment. The method comprises the following steps: firstly, constructing a disease relation network according to historical diagnosis record data, calculating the disease feature vectors on the network through the explicit andimplicit correlations between the disease entities by using the neural network model, and calculating the correlation matrix between the diseases through disease feature vectors to serve as medical specialist knowledge; secondly, designing a disease prediction model based on deep learning, and subjecting the original medical index data of the patient to dimensionality reduction through a noise reduction self-encoder stack model, and predicting the potential disease of the patient by taking the data as the input data of the multi-label disease prediction model; and finally, in the parameter learning part of the model, taking a disease similarity matrix which is automatically extracted in the first step as a medical background constraint condition, making an optimal parameter of the algorithm learning model, and taking a disease with relatively high incidence probability as a prediction result. Compared with the prior art, the disease prediction accuracy is improved.

Description

technical field [0001] The invention relates to a disease prediction method, in particular to a disease prediction method based on automatic extraction of medical expert knowledge, and belongs to the field of intelligent medical technology. Background technique [0002] In recent years, with the continuous development of medical equipment and the continuous improvement of the electronic level of medical institutions, the electronicization of medical information has become more and more popular, enabling doctors to make predictions and diagnoses of patients' conditions by referring to real-time data that could not be obtained before. At the same time, the digitization of medical information also quantifies and saves doctors' diagnostic records and patients' physical data, providing data support for the establishment of a more intelligent disease prediction system. At present, many research institutions have carried out research on disease prediction systems. However, the hug...

AI Technical Summary

Problems solved by technology

However, although there are many indicators of the patient's physical examination, there are relatively few indicators that actually have test results, resulting in the sparsity of the patient's physiological data.

If the outliers in the result values ​​are directly used for judgment, the potential correlation between different indicators is ignored. For example, some diseases that require comprehensive judgment of multiple indicators may not be found

The current research work does not take into account the dimensionality reduction representation of the patient's physiological data to generate denser and more valuable data for disease prediction, so the prediction results obtained are not satisfactory

Images