An ES-based electronic medical record retrieval method

Abstract

The invention discloses an ES-based electronic medical record retrieval method, which relates to the technical field of medical data retrieval. This method introduces semantic analysis model into electronic medical record analysis, including the extraction of subject words and the calculation of semantic similarity, taking advantage of their advantages in text semantic mining, This paper providesthe algorithm support for the latent semantic mining of text information in electronic medical record retrieval by establishing the general medical semantic database (negative word, synonym, ambiguousword), It realizes the high accuracy and recall rate of information retrieval, better adapts to the medical terminology compared with the common natural language is often more complex and constantlychanging, and medical abbreviations, synonyms and polysemous words more characteristics. It meets the scientific research needs of multi-dimensional combined retrieval and the needs of full-text retrieval of related literatures based on latent semantic search. Realize intelligent full-text retrieval with semantic extension and semantic connotation extension in real sense.

Description

technical field [0001] The invention relates to the technical field of medical data retrieval, in particular to an ES-based electronic medical record retrieval method. Background technique [0002] With the popularization and development of information technology and the continuous development of hospital information system, electronic medical records, as the information basis of clinical medical treatment, have gradually become the core of modern hospital comprehensive information system. Electronic medical records include not only all the content of paper medical records, but also multimedia information such as audio-visual, charts, and graphics. Electronic medical records record the patient's condition and treatment process in detail and completely. With the increasingly widespread application of electronic medical records, a large amount of electronic medical record data has been accumulated. How to effectively use these stored data to serve medical institutions, scienti...

AI Technical Summary

Problems solved by technology

[0006] (2) Does not support latent semantic search, resulting in missing documents

Although some electronic medical record management systems take into account the doctor's scientific research or the retrieval needs of intractable diseases, and can query through the patient's demographic attribute information, or query through structured data such as disease name and medication content, they are all maintained at the most basic level. There is no support for ambiguity. Due to the particularity of the medical field, the language habits of different regions, different specialties, and different doctors cannot be completely consistent. The phenomenon of polysemy and polysemy is widespread. The quality of exact match search results has a great relationship with the quality of keyword selection, the query efficiency is low, and the accuracy rate and recall rate (recall rate) are not high

[0007] (3) Focus on structured information retrieval, ignoring full-text retrieval

When querying the existing electronic medical record retrieval system, keywords can only query standardized two-dimensional table data, and cannot query highly subjective text unstructured content such as doctor's orders, medical history, and symptom descriptions. When conducting scientific research, Often the subjective content of this part of the description provides more valuable information than the structured content. If these data can be transformed into valuable information and knowledge, the mining and discovery of disease laws will be greatly improved. help

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