Disease cognition system based on main symptom and accompanying symptom words

Abstract

The invention provides a disease cognition system based on main symptom and accompanying symptom words. The system comprises a data acquisition module used for acquiring disease feature information and corresponding symptom feature information and establishing a disease knowledge database, a feature extraction module which is used for obtaining to-be-recognized clinical symptom feature informationand obtaining feature words, a matching module which is used for matching the disease feature words to be recognized with the disease feature information and searching symptom feature information corresponding to the disease feature information from a disease knowledge database according to the matching similarity, and a cognition module which is used for establishing a Jaccard coefficient similarity algorithm, calculating the similarity between the symptom feature words to be identified and the symptom feature information and generating a corresponding cognition report. According to the method, a combined cognitive model is constructed through the keyword matching method and the Jaccard coefficient similarity theorem, the to-be-recognized disease is accurately recognized through the combined cognitive model, recognition accuracy of the system is improved, and user experience is improved.

Description

technical field

[0001] The invention relates to the field of computer technology, in particular to a disease recognition system based on main symptoms and accompanying symptom words. Background technique

[0002] Each disease has its specific etiology and pathology, especially has a certain development and evolution law, and shows different characteristic information of clinical symptoms. The clinical symptom feature information is the abnormal state of the patient due to the disease. Everyone is different in age, gender, and constitution. Even if they suffer from the same disease, their symptoms are also different. Symptoms in the early, middle, and late stages of the disease also show periodic changes.

[0003] In recent years, relevant experts have begun to study the methods of deriving and predicting diseases through the characteristic information of clinical symptoms. For example, the method of deriving disease probability based on the feature weight of clinical symp...

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