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Query reconstruction method for electronic medical record description

An electronic medical record and sub-query technology, applied in text database query, medical data mining, unstructured text data retrieval, etc. Limitation issues, reducing the effect of human negligence

Active Publication Date: 2020-06-16
TONGJI UNIV
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Problems solved by technology

[0005] Judging from the current research results of the TREC CDS task, the main work of document retrieval is concentrated on the processing of the original query statement, the mainstream method is query expansion based on keywords, and the query processing work for long texts of electronic medical records is very rare

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  • Query reconstruction method for electronic medical record description
  • Query reconstruction method for electronic medical record description
  • Query reconstruction method for electronic medical record description

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Embodiment Construction

[0022] The concrete implementation process of the present invention is as figure 1 As shown, including the following 5 steps:

[0023] Step 1. Preprocessing the electronic medical record text and medical literature text in the data set;

[0024] Step 2, train the SVM classifier to predict the query intent of the electronic medical record text;

[0025] Step 3. Obtain all sub-queries of the electronic medical record text and perform preliminary pre-screening on it;

[0026] Step 4, train the query quality prediction model, and select the optimal sub-query from the sub-queries pre-screened and output in step 3;

[0027] Step 5. Combining the query intent obtained in step 2 with the optimal subquery output in step 4 to obtain the final reconstructed query.

[0028] The individual steps are detailed below.

[0029] Step 1: Preprocessing the electronic medical record text and medical literature text in the dataset

[0030] As an example, the dataset used comes from the TREC CD...

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Abstract

The invention discloses a query reconstruction method for electronic medical record description, and relates to a query reconstruction method for electronic medical record long texts in clinical decision support. Query reconstruction in information retrieval refers to a process of automatically processing information input by a user to form a new query expression, and the purpose is to further mine a real intention of user query in a large amount of complex information data and improve a retrieval effect. In the medical field, medical literature retrieval is an important application supportedby clinical decisions, and required information is obtained from massive medical texts by taking electronic medical record texts as query input. The description of the electronic medical record is very complex, and query reconstruction needs to be carried out to obtain an effective retrieval effect. Therefore, for the electronic medical record description long text with redundant information, thesub-query with the highest query quality is selected to replace the original query by utilizing the sub-query segmentation and screening and query quality prediction technology, and the query intention is predicted to reconstruct the query, so that the retrieval efficiency is improved.

Description

technical field [0001] The invention relates to the field of text retrieval, in particular to the processing of query in text retrieval. Background technique [0002] Information retrieval is the process of finding the information users need from unstructured large-scale data, and it is an effective method to obtain key information from massive data. As early as the last century, medical experts considered using data and models to assist clinical decision-making, and thus proposed a clinical decision support system. The clinical decision support system is a medical information technology application system, including the use of information retrieval technology to predict diagnosis, that is, to use the patient description in the medical record as a query to find relevant medical literature to assist decision-making. Through this method, the clinical decision support system can effectively mine the deep data in medical treatment, improve the efficiency of medical service, and...

Claims

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Application Information

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IPC IPC(8): G16H10/60G16H50/70G06K9/62G06F16/33
CPCG16H10/60G16H50/70G06F16/33G06F18/2411G06F18/214
Inventor 方钰姚窅陆明名黄欣翟鹏珺
Owner TONGJI UNIV
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