Electronic medical record phenotype extraction and phenotype name normalization method and system

An electronic medical record and phenotype technology, applied in the field of medical text data processing, can solve problems such as high economic costs, achieve the effects of improving efficiency, eliminating manual consumption, and improving recall accuracy

Active Publication Date: 2019-03-15
TSINGHUA UNIV
View PDF7 Cites 23 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Considering the huge amount of data, if relying on manual s

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Electronic medical record phenotype extraction and phenotype name normalization method and system
  • Electronic medical record phenotype extraction and phenotype name normalization method and system
  • Electronic medical record phenotype extraction and phenotype name normalization method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention:

[0041] Such as Figure 8 As shown, the electronic medical record phenotype extraction and phenotype name standardization system of the present invention includes: a phenotype extraction module, an encoding module, a calculation module, and a mapping module, wherein the encoding module includes: an encoder training sub-module and a phenotype encoding sub-module module. The encoding module, calculation module, and mapping module jointly complete the normalization of phenotype names.

[0042] Based on deep learning, the present invention conducts phenotype extraction and phenotype name standardization method for electronic medical record phenotypes, including: phenotype extraction, taking natural sentences of medical record text as original data, using Bi-LSTM mod...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an electronic medical record phenotype extraction and phenotype name normalization method. The method comprises the following steps: phenotypic extraction: taking natural statements of a medical record text as original data, and adopting Bi-; Named entity recognition is carried out on the LSTM model and the CRF model, and a phenotypic entity class is extracted; And phenotype standardization is carried out, an LSTM encoder is adopted to encode each phenotype, cosine similarity between non-standard phenotype codes and standard phenotype codes in the medical records is calculated, and the non-standard phenotype codes are mapped to the phenotype with the highest cosine similarity. The invention further discloses an electronic medical record phenotype extraction and phenotype name normalization system. According to the method, the named entity identification accuracy, the recall accuracy and the phenotypic mapping accuracy in the electronic medical record are improved; The labor consumption in the medical record structuring process is avoided, and the medical record structuring efficiency is improved; The method can more efficiently and accurately serve medical data mining, clinical decision support, clinical risk assessment and the like.

Description

technical field [0001] The present invention relates to the technical field of medical text data processing, in particular to a method and system for extracting phenotypes from electronic medical records and standardizing phenotype names based on deep learning. Background technique [0002] In conventional medical electronic records, the main carrier of information is natural language, such as image reports, medication records, disease course reports, and medical record inspection reports. It can be said that these natural language texts contain the main clinical information in the process of patient diagnosis and treatment. On the one hand, in recent years, hospitals at all levels in my country have gradually adopted electronic medical record management systems to replace traditional handwritten medical records, thus accumulating more and more medical electronic records. On the other hand, with the deep integration of information technology represented by big data and arti...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F16/25G16H10/60
CPCG16H10/60
Inventor 江瑞黄浩
Owner TSINGHUA UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap