Combination drug recognition and ranking method based on medical literature database

A technology of medical literature and sorting method, applied in computer technology in the field of medical clinic, can solve the problem of result error and so on

Pending Publication Date: 2017-05-24
CHONGQING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although this method proposes how to scientifically sort medical literature, there is a problem. What MedRank actually provides is the ranking of all involved single drugs for a certain disease, but many literatures now propose a ranking for a certain disease. The treatment plan of a disease involves a combination

Method used

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  • Combination drug recognition and ranking method based on medical literature database
  • Combination drug recognition and ranking method based on medical literature database
  • Combination drug recognition and ranking method based on medical literature database

Examples

Experimental program
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Effect test

Embodiment 1

[0056] Such as figure 1 as shown, figure 1 It is a schematic diagram; a method for identifying and sorting combined drugs based on a medical literature database provided in this embodiment, first uses text mining to extract classification features from abstracts that meet the requirements, and secondly uses the support vector machine model in machine learning to perform Classify, and use genetic algorithm to optimize the parameters of the support vector machine model; since then, the literature containing multiple drugs and the combination relationship between drugs can be identified, and finally the medrank algorithm is used to sort these literatures, and the results for a certain disease are obtained. Recommendation results for combination drugs.

[0057] Among them, the extraction of classification features can be implemented simply by using the JAVA language, and the classification by using the support vector machine model can use a simple, easy-to-use, fast and effective...

Embodiment 2

[0059] The method provided in this embodiment is as follows:

[0060] First, grab the article information containing the specified disease in the MEDLINE literature database, and use the drug entity to identify the literature information containing multiple drugs; use the abstract information and title information in the article as a data set, and then use part of these data sets as The training set and the test set were manually marked, and the documents marked as the combination relationship and the non-combination relationship of the drug were marked; then, the feature selection method CHI chi-square statistical method in text mining was used to extract the classification keywords, and TF / IDF was used to classify each A keyword is weighted as a feature, and the selected classification features include classification keywords, whether the drug appears in the same sentence, word features, part-of-speech features, logical features, and dependent syntactic features of this sente...

Embodiment 3

[0097] This implementation example uses data from the medline medical literature dataset from 1966 to 2015. Use the xml dataset provided by medline. The format of the dataset is as follows:

[0098] Each of the bibliographic information starts with start with Finish. The key fields included are described below:

[0099]

[0100] The disease studied in this example is hypertension.

[0101] 2. Specific steps:

[0102] Grab the document information containing the keywords "humans" and "hypertension" in the mesh word;

[0103] Grab the literature containing multiple drug entities in the abstract, and obtain 7911 abstracts as the original corpus;

[0104] Manually annotate some of the summaries. Mark as summaries with combined relationship and abstracts without combined relationship;

[0105] Use the text representation method and text feature selection method in text mining to extract classification keywords. Finally, 20 classification keywords are selected, and thei...

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Abstract

The invention discloses a combination drug recognition and ranking method based on a medical literature database. First, public medical literature abstracts in the medical literature database are captured, and drug entities in the medical literature abstracts are recognized; then, a feature extraction method in text mining is used for extracting features, a classification algorithm in machine learning is used for classifying drugs, and parameters of the classification algorithm are optimized through an optimization algorithm; last, Medrank is used for performing combination drug ranking to obtain a combination drug use recommendation scheme related to a certain disease. According to the combination drug recognition and ranking method, the problem that medical researchers cannot read and discover the law in mass medical literature which is increased at an exponential order every year is solved through the text mining technology and machine learning related knowledge, the ranking result of combination drugs for treating a certain disease and the variation trend over the years in the literature can be quickly known, and therefore the pressure on the medical researchers reading the mass literature is relieved.

Description

technical field [0001] The invention relates to computer technology in the field of medical clinical technology, in particular to a combined drug identification and sorting method based on a medical literature database. Background technique [0002] As we all know, medical literature has become an important source of information for medical researchers and workers, but in today's society where information is exploding, medical information is also exploding in large numbers. According to statistics, medical information resources account for more than 30% of Internet information resources, and the number of medical literature is growing at an alarming rate. There are nearly 30,000 medical journals in the world, and more than 2 million papers are published every year with an annual growth rate of 7%. The increasing update of medical literature has become a major challenge for medical researchers and workers. On average, clinicians have to read a large amount of professional li...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/35G06F16/367
Inventor 李学明张琦
Owner CHONGQING UNIV
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