Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Medicine recognition method based on machine learning and related equipment

A machine learning and drug technology, applied in the field of entity recognition, can solve problems such as low accuracy and low efficiency, and achieve the effect of improving efficiency and accuracy

Pending Publication Date: 2020-08-11
PING AN TECH (SHENZHEN) CO LTD
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the sorting out of drugs is still done manually, with low efficiency and low accuracy

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
  • Medicine recognition method based on machine learning and related equipment
  • Medicine recognition method based on machine learning and related equipment
  • Medicine recognition method based on machine learning and related equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0084] figure 1 It is a flow chart of the machine learning-based drug identification method provided by Embodiment 1 of the present invention. The medicine identification method based on machine learning is applied to computer equipment for identifying the medicine in the medicine sentence to be recognized.

[0085] Such as figure 1 As shown, the drug identification method based on machine learning includes:

[0086] 101. Acquire a first drug sentence sample set, where each first drug sentence sample in the first drug sentence sample set includes a missing word and a missing word label.

[0087] In a specific embodiment, said obtaining the first drug sentence sample set includes:

[0088] (1) Scan and recognize paper-based medical books through Optical Character Recognition (OCR, Optical Character Recognition).

[0089] For example, book images of paper-based medical books can be acquired by optical scanners or digital cameras; book images can be binarized, and book images...

Embodiment 2

[0144] figure 2 It is a structural diagram of a machine learning-based drug identification device provided in Embodiment 2 of the present invention. The machine learning-based drug identification device 20 is applied to computer equipment. The machine learning-based drug identification device 20 is used to identify the drug in the drug sentence to be identified.

[0145] Such as figure 2 As shown, the drug identification device 20 based on machine learning may include a first acquisition module 201, a first training module 202, a second acquisition module 203, a second training module 204, a third training module 205, and a third acquisition module 206 , the first identification module 207 , and the second identification module 208 .

[0146] The first acquiring module 201 is configured to acquire a first drug sentence sample set, and each first drug sentence sample in the first drug sentence sample set includes a missing word and a missing word label.

[0147] ...

Embodiment 3

[0199] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned embodiment of the drug identification method based on machine learning are implemented, for example figure 1 Steps 101-108 shown:

[0200] 101. Obtain a first drug sentence sample set, where each first drug sentence sample in the first drug sentence sample set contains a missing word and a missing word label;

[0201] 102. Use the first drug sentence sample set to train a coding model;

[0202] 103. Obtain a second drug statement sample set and a third drug statement sample set, each second drug statement sample in the second drug statement sample set contains a chemical substance label, and each of the third drug statement sample sets The third drug statement sample contains a therapeutic substance label;

[0203] 104. Use the encoding model to extract the vector sequence ...

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 provides a medicine recognition method based on machine learning and related equipment. The medicine recognition method comprises the following steps of: extracting a vector sequence ofa second drug statement sample by using an encoding model, and training a chemical substance identification model according to a chemical substance label of a second drug sample by taking a vector sequence of the second drug sample as input; extracting a vector sequence of a third drug statement sample by using the encoding model, and training a therapeutic substance recognition model according toa therapeutic substance label of a third drug sample by taking a vector sequence of the third drug sample as input; extracting a vector sequence of a to-be-recognized drug statement by using the encoding model, acquiring a chemical substance entity set by recognizing the vector sequence of the to-be-recognized drug statement by means of the chemical substance recognition model, and acquiring a therapeutic substance entity set by recognizing the vector sequence of the to-be-recognized drug statement by means of the therapeutic substance recognition model; and determining substance entities existing in both the chemical substance entity set and the treatment substance entity set as drugs. According to the medicine recognition method, the drug identification efficiency and accuracy rate areimproved.

Description

technical field [0001] The present invention relates to the technical field of entity recognition, in particular to a drug recognition method, device, computer equipment and computer-readable storage medium based on machine learning. Background technique [0002] For many medical texts, extracting the drug names in them is of great help to understand the content of the text. In order to help relevant practitioners and researchers obtain drug names in medical texts quickly and efficiently, it is urgent to identify drug named entities and effectively obtain drug named entities from a large number of medical texts. [0003] In practical applications, named entity recognition technology is still a blank in the field of drug named entity recognition. At present, the sorting of drugs is still done manually, which is inefficient and has low accuracy. Contents of the invention [0004] In view of the above, it is necessary to propose a drug identification method, device, compute...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/289G06F40/295G06F40/30G06K9/20G06N20/00
CPCG06F40/289G06F40/295G06F40/30G06N20/00G06V10/22
Inventor 顾大中
Owner PING AN TECH (SHENZHEN) CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products