CN111914095A - Medicine interaction relation extraction method and system - Google Patents
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- 239000003814 drug Substances 0.000 title claims abstract description 131
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Abstract
The invention provides a method and a system for extracting interaction relation of medicines, wherein the method comprises the following steps: inputting the medicine specification into a trained machine learning model, and identifying the medicine entity relationship, wherein the machine learning model is established by extracting characteristic text information in the medicine specification in a semi-supervised learning training mode; and providing the drug entity relationship for reference of pharmacists and users. According to the method and the system for extracting the drug interaction relationship, provided by the embodiment of the invention, a semi-supervised learning mode is adopted, a machine learning model is trained, and the drug entity names appearing in a drug specification can be comprehensively judged, so that the result is more accurate.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a medicine interaction relation extraction method and system.
Background
People pay more and more attention to their health, and the operating load of each large pharmacy is increased virtually, so that pharmacists have higher and higher requirements on professional knowledge. Generally, a pharmacist can prescribe a proper prescription to a patient by virtue of his or her own professional knowledge, but due to limitations of time and energy, the pharmacist cannot completely understand each specific medicine commodity, and thus, the corresponding medicine specification becomes the first reference text for the pharmacist on how to take the medicine. However, because there are many written texts in some drug specifications, it may be difficult for pharmacists to find out useful key information in a short time, and visual fatigue is easily caused when pharmacists highly concentrate on reading drug specifications for a long time, which further increases the manual burden of pharmacists and is easy to cause medical accidents.
Currently, with the increasingly perfect health informatization construction of medical institutions in China, information related to various medicines can be checked in an electronic version form, for example, detailed descriptions about various medicines, various open medicine websites and the like on the national medicine supervision and administration bureau have very detailed text information related to medicine administration. The method provides a basis for the development of the big data work of the medicine, can reasonably utilize the information resources disclosed on the network, and provides a certain foundation for the automation of the medication reference of the pharmacist. However, the current general technology for extracting and processing text information has great limitation, is not suitable for extracting text information in a medicine specification, and has the following defects: 1. the accuracy is not sufficient, the medical field is a very rigorous field, the requirement on the accuracy of the result is high, and a more accurate model is needed for identifying the relationship among complex drug entities, disease entities, food name entities and the like in the drug specification. 2. The identification and extraction of all the numerous and complex entities and their relationships (e.g., drug interactions, indications, contraindications, cautions, etc.) appearing in the drug specification does not provide a good solution. 3. Various entity names (including a large number of entity names nested with each other) appearing in a medicine specification are identified by adopting a pure named entity identification technology, and the accuracy of identification is insufficient due to the lack of cooperation of a professional pharmacist team, so that the actual requirement is not met, and many problems can occur. 4. The text information in the specification is not divided into fine granularity enough, so that the requirement of the actual work of a pharmacist is not met. 5. The correctly identified data lacks a uniform management form, subsequent data utilization cannot be carried out, and data is wasted.
Therefore, there is a need for a new drug interaction extraction method to solve the above problems.
Disclosure of Invention
The present invention provides a drug interaction extraction method and system that overcomes or at least partially solves the above mentioned problems, and according to a first aspect of the invention, the invention provides a drug interaction extraction method comprising:
inputting the medicine specification into the trained machine learning model, and identifying the medicine entity relationship; the machine learning model is established by extracting characteristic text information in the drug specification in a semi-supervised learning training mode;
and providing the drug entity relationship for reference of pharmacists and users.
Wherein the method further comprises:
establishing the machine learning model;
training the machine learning model.
Wherein the establishing the machine learning model comprises:
acquiring drug specification data, and constructing the drug specification data, drug entity names and specific relationship types between the drug specification data and the drug entity names into a category database;
and extracting text information in the medicine specification based on the category database, and completely supplementing the missing syntax structure to establish the machine learning model.
Wherein the training the machine learning model comprises:
and labeling the text information extracted from the medicine specification in a semi-supervised learning mode.
Wherein the method further comprises:
and checking the output result of the trained machine learning model, correcting wrong text information, and extracting all the text information with wrong identification so as to update the machine learning model.
Wherein the method further comprises:
and extracting the identified drug entity relationship to form a triple, and establishing a drug specification knowledge graph.
According to a second aspect of the present invention there is provided a drug interaction relation extraction system comprising:
the identification and extraction module is used for inputting the medicine specification into a trained machine learning model and identifying the medicine entity relationship, wherein the machine learning model is established by extracting characteristic text information in the medicine specification in a semi-supervised learning training mode;
and the comprehensive judgment module is used for providing the drug entity relationship for the reference of pharmacists and users.
According to a third aspect provided by the present invention, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the steps of the drug interaction relation extracting method as provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the steps of the drug interaction relation extraction method as provided in the first aspect.
According to the method and the system for extracting the drug interaction relationship, provided by the embodiment of the invention, a semi-supervised learning mode is adopted, a machine learning model is trained, and the drug entity names appearing in a drug specification can be comprehensively judged, so that the result is more accurate.
Drawings
FIG. 1 is a schematic flow chart of a method for extracting interaction relationships of drugs according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an overall process for extracting interaction relationship between drugs according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a drug interaction extraction system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Fig. 1 is a schematic flow chart of a method for extracting a drug interaction relationship according to an embodiment of the present invention, as shown in fig. 1, including:
101. inputting the medicine specification into the trained machine learning model, and identifying the medicine entity relationship; the machine learning model is established by extracting characteristic text information in the drug specification in a semi-supervised learning training mode;
102. and providing the drug entity relationship for reference of pharmacists and users.
It should be noted that, providing automated medication reference for pharmacists is to extract all text information in important items related to "interaction", "contraindication" and "notice" in each drug specification by program scripts, and what information the pharmacists need to use can be automatically provided. For example, if a pharmacist needs to use the data of drug interaction, the established algorithm model can be used to classify the text information of drug interaction into two categories: "interaction mechanism and results" and "processing opinion". If the pharmacist needs a specific method for processing opinions of a certain medicine, the specific method for processing opinions can be given through the established algorithm model through related text information, and the specific interaction mechanism and result can also be given. For another example, if it is known that the effect of a certain drug on a certain disease is "improved" or "aggravated", a corresponding judgment can be given from the text information in the specification through the established algorithm model.
By the method, the time overhead of extraction of key information in the text information of the drug specification by a pharmacist can be greatly reduced, the work of the pharmacist is only to check the result of the algorithm judgment, and the pharmacist can provide corresponding text information in the original drug specification. Meanwhile, corresponding medication reference can be provided for the user who buys the medicine, so that the user can take the medicine more safely. The problem of doctor-patient relationship at the present stage can be greatly relieved.
In view of the above situation, the embodiment of the present invention adopts a semi-supervised learning manner, and a manner of combining the labeling of the pharmacist experts with machine learning, so as to ensure a high accuracy of the result.
Fig. 2 is a schematic diagram of an overall process of extracting interaction relationship between medicines according to an embodiment of the present invention, and as shown in fig. 2, the overall process provided by the embodiment of the present invention includes S1-S7.
Actually, an algorithm model is trained in a semi-supervised learning mode to identify the drug entity relationship from a newly provided drug specification, an identification result is given, and the identification result is provided for a pharmacist to take a reference for medication in actual work, so that the search of the pharmacist in text information of a large amount of drug specifications can be saved; the identification result can also be provided for the user to refer, so that the medication is more secure and has basis.
It can be understood that, in the method provided by the embodiment of the present invention, for a large number of numerous and complicated drug names or disease names appearing in the text information of the drug specification, the drug name entity provided and labeled by the professional pharmacist team is mainly used, and the drug name entity identified by the named entity identification technology is used for comprehensively judging the drug entity names appearing in the drug specification, so that the result is more accurate.
On the basis of the above embodiment, the method further includes:
establishing the machine learning model;
training the machine learning model.
As shown in FIG. 2 at S1-S5, the embodiment of the present invention specifically requires a model building and training process.
The method comprises the following steps:
step S1: firstly, complete medicine specification data is obtained from public data on a medicine website through a web crawler technology, and each specification is stored locally.
Step S2: the drug entity name provided by the pharmacist, the specific relationship type thereof and the supplement of the named entity identification technology are used to form a category library, which is the basis for extracting the relevant text information in the drug specification in step S3.
Step S3: the text information of two medicine entities which appear in the text information of the medicine specification at the same time is extracted by utilizing the established category rule base, and the missing syntax structure is completely supplemented to the text information lacking the related subject by utilizing the related technology of NLP, so that the text information of the specification is richer and more comprehensive, and the reference of pharmacists and users is facilitated.
Step S4: and through a semi-supervised learning mode, the text information extracted from the medicine specification is labeled to a professional pharmacist team. In the labeling process, the pharmacist provides relevant rules, deeply analyzes useful information characteristics in the text and establishes a machine learning training model. The model used above includes one or more of CRF, BiGRU, BERT.
And step S5, checking the output result of the algorithm model by a pharmacist, correcting the text information with the wrong identification, extracting all the text information with the wrong identification, analyzing the reason of the error, adjusting the parameters of the algorithm to reestablish the model, and finally enabling the comprehensive accuracy of the identification to be the highest.
Further, after the process of establishing and training the model is completed, referring to fig. 2, the general flow of the method for extracting the drug interaction relationship provided by the embodiment of the present invention further includes:
step S6: and inputting the medicine specification into the trained machine learning model, and outputting the medicine entity relationship.
Step S7: the output medicine entity relation is provided for the reference of pharmacists and users on one hand, and data support is provided for establishing a medicine specification knowledge graph on the other hand.
On the basis of the foregoing embodiment, the establishing the machine learning model includes:
acquiring drug specification data, and constructing the drug specification data, drug entity names and specific relationship types between the drug specification data and the drug entity names into a category database;
and extracting text information in the medicine specification based on the category database, and completely supplementing the missing syntax structure to establish the machine learning model.
On the basis of the above embodiment, the training the machine learning model includes:
and labeling the text information extracted from the medicine specification in a semi-supervised learning mode.
On the basis of the above embodiment, the method further includes:
and checking the output result of the trained machine learning model, correcting wrong text information, and extracting all the text information with wrong identification so as to update the machine learning model.
As shown in step S5 in fig. 2, in the embodiment of the present invention, the output result of the algorithm model may be checked by the administrator, the text information with the identification error is corrected, all the text information with the identification error is extracted, the cause of the error is analyzed, the parameter of the algorithm is adjusted to rebuild the model, and finally, the comprehensive accuracy of the identification is maximized.
On the basis of the above embodiment, the method further includes:
and extracting the identified drug entity relationship to form a triple, and establishing a drug specification knowledge graph.
As shown in step S7 in fig. 2, the embodiment of the present invention may further extract pairs of drug entities and their relationships into triples, which provide data support for establishing a drug specification knowledge graph.
It can be understood that the result identified by the method provided by the embodiment of the invention can naturally form a triple form of entity pair + relation, and the construction of the knowledge graph of the drug specification is seamlessly connected, so that good data support is provided, and the data defect of the knowledge graph of the drug specification at home and abroad is overcome.
Fig. 3 is a schematic structural diagram of a drug interaction relation extraction system according to an embodiment of the present invention, as shown in fig. 3, including: an identification extraction module 301 and a comprehensive judgment module 302, wherein:
the identification and extraction module 301 is configured to input the drug instruction manual into a trained machine learning model, and identify a drug entity relationship, where the machine learning model is established by extracting feature text information in the drug instruction manual in a semi-supervised learning training manner;
the comprehensive judgment module 302 is used for providing the drug entity relationship for the pharmacist and the user to refer.
For details, how to extract the interaction relationship of the medicine by using the identification and extraction module 301 and the comprehensive judgment module 302 may refer to the embodiment shown in fig. 1, and the embodiment of the present invention is not described herein again.
Fig. 4 illustrates a schematic structural diagram of an electronic device, and as shown in fig. 4, the server may include: a processor (processor)401, a communication Interface (communication Interface)402, a memory (memory)403 and a bus 404, wherein the processor 401, the communication Interface 402 and the memory 403 complete communication with each other through the bus 404. The communication interface 404 may be used for information transmission between the server and the smart tv. Processor 401 may call logic instructions in memory 403 to perform the following method: inputting the medicine specification into a trained machine learning model, and identifying the medicine entity relationship, wherein the machine learning model is established by extracting characteristic text information in the medicine specification in a semi-supervised learning training mode; and providing the drug entity relationship for reference of pharmacists and users.
The present embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, enable the computer to perform the methods provided by the above-described method embodiments, for example, including: inputting the medicine specification into a trained machine learning model, and identifying the medicine entity relationship, wherein the machine learning model is established by extracting characteristic text information in the medicine specification in a semi-supervised learning training mode; and providing the drug entity relationship for reference of pharmacists and users.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the methods provided by the above method embodiments, for example, including: inputting the medicine specification into a trained machine learning model, and identifying the medicine entity relationship, wherein the machine learning model is established by extracting characteristic text information in the medicine specification in a semi-supervised learning training mode; and providing the drug entity relationship for reference of pharmacists and users.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, the method of the present application is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method for extracting drug interaction relationships, comprising:
inputting the medicine specification into the trained machine learning model, and identifying the medicine entity relationship; the machine learning model is established by extracting characteristic text information in the drug specification in a semi-supervised learning training mode;
and providing the drug entity relationship for reference of pharmacists and users.
2. The drug interaction relationship extraction method of claim 1, further comprising:
establishing the machine learning model;
training the machine learning model.
3. The drug interaction relationship extraction method of claim 2, wherein the establishing the machine learning model comprises:
acquiring drug specification data, and constructing the drug specification data, drug entity names and specific relationship types between the drug specification data and the drug entity names into a category database;
and extracting text information in the medicine specification based on the category database, and completely supplementing the missing syntax structure to establish the machine learning model.
4. The drug interaction relationship extraction method of claim 2, wherein the training the machine learning model comprises:
and labeling the text information extracted from the medicine specification in a semi-supervised learning mode.
5. The drug interaction relationship extraction method of claim 4, further comprising:
and checking the output result of the trained machine learning model, correcting wrong text information, and extracting all the text information with wrong identification so as to update the machine learning model.
6. The drug interaction relationship extraction method of claim 1, further comprising:
and extracting the identified drug entity relationship to form a triple, and establishing a drug specification knowledge graph.
7. A drug interaction relation extraction system, comprising:
the identification and extraction module is used for inputting the medicine specification into a trained machine learning model and identifying the medicine entity relationship, wherein the machine learning model is established by extracting characteristic text information in the medicine specification in a semi-supervised learning training mode;
and the comprehensive judgment module is used for providing the drug entity relationship for the reference of pharmacists and users.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the drug interaction relation extraction method of any of claims 1 to 6.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the drug interaction relationship extraction method of any of claims 1 to 6.
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