Control method and control device for realizing disease prediction based on eigenvector

Abstract

The invention provides a control method and a control device for realizing disease prediction based on an eigenvector. The control method comprises the steps of: a, converting one or more original vectorized representations into a dense vectorized representation based on an Embedding model; b, regarding the dense vectorized representation as an input based on a diagnostic model, and determining the similarity of each disease of the dense vectorized representation in the diagnostic model; c, and determining a disease type with the highest matching degree with the dense vectorized representationbased on the similarity of each disease of the dense vectorized representation in the diagnostic model. The control method and the control device integrate one or more pieces of case information of auser, takes a super-long vector representing a panoramic disease of the patient, reserve a relevance hypothesis, introduce a deep neural network model to learn an interactive feature of a single feature and depth in the super-long vector, and determine a probability of each disease matched with the user case information. The control method and the control device have the advantages of simple operation, convenient use and high commercial value.

Description

technical field [0001] The invention belongs to the fields of medical clinical auxiliary diagnosis, diagnosis guidance and case quality control, and in particular relates to a control method and a control device for realizing disease prediction based on feature vectors. Background technique [0002] In recent years, although there have been few reports of errors in clinical diagnosis and treatment, any form of medical diagnosis cannot completely avoid misdiagnosis even with the assistance of the most advanced equipment. Therefore, one of the goals of clinical diagnosis research is to explore the occurrence of misdiagnosis. The rules and preventive measures of the medical system can reduce the probability of misdiagnosis and increase the rate of diagnosis, thereby promoting the development of the medical field. At present, due to the limitation of medical resources in China, in the medical consultation environment, the attending doctor has no way to accurately and comprehensi...

AI Technical Summary

Problems solved by technology

[0002] In recent years, although there have been few reports of errors in clinical diagnosis and treatment, any form of medical diagnosis cannot completely avoid misdiagnosis even with the assistance of the most advanced equipment. Therefore, one of the goals of clinical diagnosis research is to explore the occurrence of misdiagnosis. Laws and preventive measures to reduce the probability of misdiagnosis and increase the rate of diagnosis, thereby promoting the development of the medical field

At present, due to the limitation of medical resources in China, in the medical consultation environment, the attending doctor has no way to accurately and comprehensively understand the symptoms and signs of each patient during the consultation. Therefore, a set of evidence-based medical auxiliary diagnosis system is developed to improve It is of great value to improve the diagnosis and treatment level of doctors, improve the medical awareness of patients, and optimize the pre-hospital services of both doctors and patients.

At present, some medical diagnosis, guidance, case quality control and other systems on the market are based on the analysis of electronic medical records, and extract the patient's chief complaint, history of present illness, examination, family history and other data information vectorization, and based on the above vectors, the disease forecasting, and the forecasting methods are mainly divided into two types, namely writing artificial forecasting rules and using conventional machine learning models, such as naive ES and logistic regression, and there are more or less shortcomings in the above technologies, for example, from Vectorized information such as patient complaints, current medical history, examinations, and symptoms extracted from family history has up to tens of thousands of dimensions, and due to the limitation of vector length, existing methods have adopted different selection methods, which cannot make good use of this information mak

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