Search method and device and storage medium

Abstract

Embodiments of the invention provide a search method and apparatus, and a storage medium. The method comprises the steps of obtaining search information; obtaining at least one symptom feature based on the search information; according to the semantic association degree of the symptom features and the symptoms, weights are set for the symptom features respectively; obtaining a target similarity between each symptom feature with the set weight and at least one preset symptom entity; and determining a target symptom entity from the at least one preset symptom entity according to the target similarity and outputting the target symptom entity. Through the scheme, the accuracy of symptom entities can be identified, and similar symptoms and non-similar symptoms can be effectively distinguished.

Description

technical field [0001] The embodiments of the present application relate to the technical field of artificial intelligence, and in particular to a search method, device, and storage medium. Background technique [0002] In the medical field, artificial intelligence is often used to provide users with self-service medical services. For example, in the medical intelligent assistant application based on artificial intelligence, the medical intelligent assistant application will perform follow-up disease judgment, department guidance, registration to find a doctor and other services based on the user's input symptoms. However, for the same symptom, the user's input is usually very different. For example, the same standard symptom "abdominal pain", user a's input may be: "stomach pain", user b's input may be: "slight stomach pain". Since it is necessary to carry out follow-up disease judgment, department guidance, registration to find a doctor and other processes based on the s...

AI Technical Summary

Problems solved by technology

[0003] During the research and practice of the existing technology, the inventors of the embodiments of the present application found that the model in the medical intelligent assistant application is obtained by training a supervised machine learning or deep learning classification model based on manually labeled training data, although the final training The generalization ability of the obtained model is strong, but it needs to rely on a large amount of accurate manual labeling data, and the cost of manual labeling is high

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