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Text mining method and system based on unstructured electronic medical records

A text mining, unstructured technology, applied in the fields of natural language processing and machine learning, which can solve the problems of medical worker burden, error transmission, and a large number of manual labels.

Inactive Publication Date: 2019-11-08
UNIV OF JINAN +1
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  • Description
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  • Application Information

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Problems solved by technology

At the same time, the method of text mining for electronic medical records is now supervised learning, which not only requires a lot of manual labeling, but also brings a burden to medical workers, and is likely to cause error transmission.

Method used

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  • Text mining method and system based on unstructured electronic medical records
  • Text mining method and system based on unstructured electronic medical records
  • Text mining method and system based on unstructured electronic medical records

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Embodiment Construction

[0047] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0048]It should be noted that the terminology used herein is only for describing specific embodiments, and is not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

[0049] Implementation example one

[0050] This embodiment discloses a text mining method based on unstructured electronic medi...

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Abstract

The invention discloses a text mining method and system based on unstructured electronic medical records. The method comprises the steps that multiple current medical history records derived from a hospital database serve as the original experimental data, and each sample is spread in a time series, words describing the meaning of time are identified at first, then long text is divided into several pieces of short text by taking a time node as boundary, that is to say, current medical histories are split into each hospital record; the features of the current medical history and extraction rules are determined and saved as an xml file; on the basis of medical history information extraction and structured storage, the defined rules are rewritten to form a regular expression to achieve feature extraction of unstructured text; quantitative representation of features is conducted, wherein by means of the data type of eigenvalue obtained after analysis and extraction, the eigenvalue is numerically quantified. The quantified eigenvalue is unified into the feature representation X=(x1, x2, x3, ..., x57) during one hospital stay, and then serves as the text features of an unsupervised clustering algorithm to implement text clustering.

Description

technical field [0001] The present disclosure relates to the technical field of natural language processing and machine learning, in particular to a text mining method and system based on unstructured electronic medical records. Background technique [0002] In Chinese Electronic Medical Record (EMR) data, main complaints, current medical history, past history, imaging reports, surgical records, etc. are mainly described in natural language, and collected and stored in structured and unstructured data formats. It is a concrete embodiment of clinicians' actual diagnosis and treatment details, including a comprehensive, professional and accurate description of patient health information, and is a valuable medical knowledge resource. Therefore, the structured processing and text mining of Chinese electronic medical records are of great significance to medical clinical auxiliary diagnosis and treatment. [0003] Currently, structured data in electronic medical records is stored...

Claims

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Application Information

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IPC IPC(8): G16H50/70G16H10/60G06F16/81G06F16/35
CPCG16H10/60G16H50/70G06F16/35G06F16/81
Inventor 杨波王芮彭立志李宝生朱健
Owner UNIV OF JINAN
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