CN109472029B

CN109472029B - Medicine name processing method and device

Info

Publication number: CN109472029B
Application number: CN201811331378.4A
Authority: CN
Inventors: 肖菲菲
Original assignee: Tianjin Xinkaixin Life Technology Co ltd; Tianjin Happy Life Technology Co ltd
Current assignee: Tianjin Xinkaixin Life Technology Co ltd; Tianjin Happy Life Technology Co ltd
Priority date: 2018-11-09
Filing date: 2018-11-09
Publication date: 2023-04-07
Anticipated expiration: 2038-11-09
Also published as: CN109472029A

Abstract

The disclosure provides a medicine name processing method and device. The medicine name processing method comprises the following steps: acquiring a plurality of word segments of the drug name; and inquiring corresponding data of the multiple participles in a preset data table, and determining standard medicine names corresponding to the multiple participles according to the corresponding data to record the corresponding relation between the standard medicine names and the medicine names. The drug name processing method provided by the disclosure can quickly determine the standard name and classification of drug names in a hospital or medical institution database.

Description

Medicine name processing method and device

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for processing names of medicines, which can quickly determine names and classifications of a large number of medicines.

Background

Since there are a large number of manufacturers of commercially available drugs, there are various descriptions of names of drugs recorded in databases of hospitals or medical institutions, and there are many kinds of drugs named by various naming rules such as product names, common names, standard drug names (International non-proprietary names or standard drug names), combinations of various names and other information such as specifications and manufacturers, and the like, such as "white plus black", "aspirin enteric coated tablets", "metformin (gelatan)", "baking soda [0.5g × 100 tablets ] [ torr ]", and the like. To establish a drug database that is easy to query and accurately determine the efficacy of drugs, drug names named according to different rules need to be normalized.

In the related art, the normalization of the drug names usually requires a large amount of manual labeling, and the manual labeling results are transmitted to a computer and then summarized by the computer to form a normalization vocabulary of the drug names. However, due to different naming rules, some medicine names are very similar, such as a sodium bicarbonate tablet (yellow sea) and a sodium bicarbonate tablet (Tianjin), etc., and the standard medicine names of the medicine, namely the sodium bicarbonate tablet, are the same, and the difference is only the production of different manufacturers, so that if a large amount of manual labeling is carried out, the labeling cost is inevitably increased. In addition, with the increase of the manual labeling amount, the normalization vocabulary also rapidly increases, and the manual labeling process inevitably causes wrong labeling results, so that the computer depends on the normalization vocabulary labeling, which enlarges wrong data to reduce the normalization accuracy, and also causes huge workload for regularly checking the correctness of the normalization table.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The present disclosure is directed to a method and an apparatus for processing a name of a drug, which are used to overcome, at least to some extent, the problems of low efficiency, high error rate, and the like in normalizing a name of a drug due to the limitations and disadvantages of the related art.

According to a first aspect of the embodiments of the present disclosure, there is provided a method for processing a drug name, including: acquiring a plurality of word segments of the drug name; inquiring corresponding data of the multiple participles in a preset data table; determining standard medicine names corresponding to the multiple participles according to the corresponding data; and recording the corresponding relation between the standard medicine name and the medicine name.

In an exemplary embodiment of the present disclosure, further comprising:

determining the drug classification in the preset data table according to the standard drug name;

and recording the corresponding relation between the drug name and the drug classification.

In an exemplary embodiment of the present disclosure, the querying, in a preset data table, the corresponding data of the plurality of participles includes:

acquiring word segmentation information of the preset data table;

inquiring a matching result of the participles in the preset data table according to the participle information of the preset data table;

and when a matching result is inquired, determining the vocabulary category corresponding to the matching result according to the preset data table.

In an exemplary embodiment of the present disclosure, the vocabulary categories include trade name, common name, standard drug name, dosage form, acid radical, ester/salt radical, specification.

In an exemplary embodiment of the present disclosure, the determining the standard drug names corresponding to the plurality of participles according to the correspondence data includes:

determining the vocabulary type with the highest preset priority according to the vocabulary types corresponding to the plurality of corresponding data;

and searching the standard medicine name corresponding to the corresponding data in the preset data table according to the corresponding data corresponding to the vocabulary type with the highest priority.

In an exemplary embodiment of the present disclosure, the searching, according to the corresponding data corresponding to the vocabulary category with the highest priority, a standard drug name corresponding to the corresponding data in the preset data table includes:

and if the corresponding data corresponding to the vocabulary category with the highest priority is multiple, determining unique corresponding data in the multiple corresponding data according to the sequential combination words of the multiple participles.

In an exemplary embodiment of the present disclosure, the obtaining the plurality of segments of the drug name includes:

setting the word number of the medicine name as a word segmentation range;

inquiring a corresponding value in a preset vocabulary table according to the medicine name, and if the corresponding value is inquired, taking the medicine name as a word segmentation result;

if the corresponding value is not inquired, subtracting one from the word segmentation range, and generating an inquiry keyword according to the new word segmentation range;

and inquiring a corresponding value in the preset vocabulary table according to the inquiry keyword, if the corresponding value is inquired, segmenting other characters of the medicine name, and if not, returning to the previous step.

According to a second aspect of embodiments of the present disclosure, there is provided a medicine name processing apparatus including:

the word segmentation module is arranged as a plurality of word segmentation data matching modules for acquiring the drug names and is arranged for inquiring corresponding data of the words in a preset data table;

the data matching module is used for inquiring corresponding data of the multiple participles in a preset data table;

a standard name determining module configured to determine standard drug names corresponding to the plurality of participles according to the corresponding data;

and the standard name recording module is set to record the corresponding relation between the standard medicine name and the medicine name.

According to a third aspect of the present disclosure, there is provided a medicine name processing apparatus including: a memory; and a processor coupled to the memory, the processor configured to perform the method of any of the above based on instructions stored in the memory.

According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the drug name processing method as recited in any one of the above.

According to the method and the device for automatically classifying the medicine names, the medicine names are automatically classified, corresponding data are searched in the preset data table according to the classification, and finally the standard medicine names are determined according to the types of the corresponding data, so that the accurate and efficient automatic normalization of a large number of medicine names can be realized, and a data basis is provided for establishing an accurate medicine catalog and indexing. Further, the ATC classification of the medicine can be determined quickly and accurately by inquiring the ATC classification matched with the medicine name in the preset data table according to the standard medicine name.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

Fig. 1 is a flowchart of a medicine name processing method in an exemplary embodiment of the present disclosure.

Fig. 2 is a sub-flowchart of a drug name processing method in an exemplary embodiment of the present disclosure.

Fig. 3 is a sub-flowchart of a drug name processing method in an exemplary embodiment of the present disclosure.

Fig. 4 is a sub-flowchart of a drug name processing method in an exemplary embodiment of the present disclosure.

Fig. 5 is a flowchart of a medicine name processing method in an exemplary embodiment of the present disclosure.

Fig. 6 is a block diagram of a drug name processing device in an exemplary embodiment of the present disclosure.

FIG. 7 is a block diagram of an electronic device in an exemplary embodiment of the disclosure.

FIG. 8 is a schematic diagram of a computer-readable storage medium in an exemplary embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Further, the drawings are merely schematic illustrations of the present disclosure, in which the same reference numerals denote the same or similar parts, and thus, a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The following detailed description of exemplary embodiments of the disclosure refers to the accompanying drawings.

Fig. 1 schematically illustrates a flowchart of a medicine name processing method in an exemplary embodiment of the present disclosure. Referring to fig. 1, a drug name processing method 100 may include:

s1, acquiring a plurality of word segments of medicine names;

s2, inquiring corresponding data of the multiple participles in a preset data table;

s3, determining standard medicine names corresponding to the multiple participles according to the corresponding data;

and S6, recording the corresponding relation between the standard medicine name and the medicine name.

According to the method and the device for automatically classifying the medicine names, the medicine names are automatically classified, corresponding data are searched in the preset data table according to the classification, and finally the standard medicine names are determined according to the types of the corresponding data, so that the accurate and efficient automatic normalization of a large number of medicine names can be realized, and a data basis is provided for establishing an accurate medicine catalog and indexing.

Next, each step of the medicine name processing method 100 will be described in detail.

In step S1, a plurality of segmented words of a drug name are acquired.

Fig. 2 is a sub-flowchart of step S1.

Referring to fig. 2, in one exemplary embodiment of the present disclosure, step S1 includes:

step S11, setting the word number of the medicine name as a word segmentation range;

step S12, inquiring a corresponding value in a preset vocabulary according to the medicine name, and if the corresponding value is inquired, taking the medicine name as a word segmentation result;

step S13, if the corresponding value is not inquired, subtracting one from the word segmentation range, and generating an inquiry keyword according to the new word segmentation range;

and S16, inquiring a corresponding value in the preset vocabulary according to the inquiry keyword, if the corresponding value is inquired, segmenting other characters of the medicine name, and if not, returning to the previous step.

First, a preset vocabulary including a large number of words for drug names may be created. The preset vocabulary may include a plurality of vocabulary categories of drug name terms. In the disclosed embodiments, the vocabulary categories include, but are not limited to, trade name, common name, standard drug name, dosage form, acid radical, ester/salt radical, specification. The vocabulary corresponding to the plurality of vocabulary categories facilitates accurate determination of the vocabulary included in the drug name.

For a medicine name, matching words can be searched from at least a preset vocabulary table according to the word number, if the matching words are found, other words in the medicine name are continuously matched, and a plurality of independent words included in the medicine name are finally determined. If the name of the medicine is not found, the name of the medicine can be sent to a manual auditing platform, manual marking is submitted, and a preset vocabulary list is enriched according to a manual marking result. When the word segmentation is performed on the medicine name, the word segmentation order should be in the order recorded in the medicine name.

For example, for the drug name of "gankang compound paracetamol amantadine hydrochloride tablet", the full text of the drug name can be firstly searched in a preset vocabulary table, and since a matching vocabulary is not searched, the matching vocabulary can be searched according to the result of reducing the number of words by one, namely "gankang compound paracetamol amantadine hydrochloride" or "kankang compound paracetamol amantadine hydrochloride tablet". And analogizing in turn until word segmentation results of 'Gankang', 'Compound', 'Paracetamol' and 'tablet' are obtained.

In step S2, the corresponding data of the multiple participles is queried in a preset data table.

Fig. 3 is a sub-flowchart of step S2.

Referring to fig. 3, in an exemplary embodiment of the present disclosure, step S2 includes:

step S21, acquiring word segmentation information of the preset data table;

step S22, inquiring a matching result of the participles in the preset data table according to the participle information of the preset data table;

and S23, when the matching result is inquired, determining the vocabulary type corresponding to the matching result according to the preset data table.

In the preset data table, each standard drug name may have a plurality of corresponding commodity names and/or a plurality of corresponding common names. Therefore, to determine the standard drug name corresponding to the drug name, the commodity name and the common name included in the plurality of segments of the drug name may be first determined.

Because some standard medicine names contain two or more medicine components, and the word segmentation result of the medicine components is the minimum word segmentation result and only one medicine component is possible, in order to ensure accurate matching, the preset data table can record the word segmentation information of each word, so that the unique matching result can be determined when the matching result of the word segmentation of the medicine name is inquired according to the word segmentation of the preset data table.

For example, if the word segmentation information of the drug name includes "AB", where a and B are both drug components, the final word segmentation result includes "a", "B". In the preset data table, three matching results of "a", "B", and "AB" can be queried. Because a plurality of matching results exist at this time, the participles can be further combined, and the matching result corresponding to the 'AB' is searched in the matching results, so that the unique matching word is determined. If the word segmentation information of the AB in the preset data table is not recorded, the accurate matching result AB cannot be determined according to the segmented words A and B of the medicine name.

After the matching result is determined, the vocabulary category corresponding to the matching result can be directly obtained, so that the vocabulary category of each participle of the medicine name is determined.

And S3, determining the standard medicine names corresponding to the multiple participles according to the corresponding data.

Fig. 6 is a sub-flowchart of step S3.

Referring to fig. 6, in an exemplary embodiment of the present disclosure, step S3 includes:

step S31, determining the vocabulary type with the highest preset priority according to the vocabulary types corresponding to the corresponding data;

and S32, searching the standard medicine name corresponding to the corresponding data in the preset data table according to the corresponding data corresponding to the vocabulary type with the highest priority.

Since the information included in the commodity name is large, the standard medicine names may be determined in the order of priority of the commodity name > the generic name > the standard medicine name in the embodiment of the present disclosure.

For example, for the drug name "pidotimod (laiy) -weitian", the word segments include "weitian" and "pidotimod", the word type of "weitian" can be determined as the commodity name by looking up in the preset data table, the word type of "pidotimod" is the standard drug name, and the word type of "langyi" is the manufacturer information. Since the priority of the commodity name is higher than that of the standard drug name in the preset priority, the unique standard drug name "pidotimod" corresponding to the commodity name "wei tian" is used as the standard drug name corresponding to "pidotimod (langyi) -wei tian".

In step S6, the correspondence between the standard drug name and the drug name is recorded.

Next, the correspondence between the standard drug name and the drug name can be recorded for use in generating a drug catalog or index.

FIG. 5 is a flowchart of a method for processing a drug name in another embodiment of the disclosure.

Referring to fig. 5, in one embodiment, the drug name processing method may further include:

s5, determining the medicine classification in the preset data table according to the standard medicine name;

and S6, recording the corresponding relation between the medicine name and the medicine classification.

After the standard drug name corresponding to the drug name is determined, the drug classification of the drug name can be determined by querying the drug classification corresponding to the standard drug name in the preset data table.

For example, for the standard drug name "pidotimod", the ATC drug classification in which it is located may be found in the preset data table as L03AX05, i.e., L03AX (other immunopotentiators) -L03A (immunopotentiators) -L03 (immunopotentiators) -L (antineoplastic and immune function modulating drugs).

By recording the ATC medicine classification corresponding to the medicine name, the data application efficiency can be improved, and for example, when the brand and the dosage form of the medicine for treating hypertension need to be inquired, the retrieval result can be quickly obtained.

In summary, in the embodiment of the present disclosure, the medicine names are segmented, the corresponding words of each segmented word and the vocabulary types of the corresponding words are searched in the preset data table, and finally the unique standard medicine name corresponding to the medicine name is determined according to the priority of the vocabulary types, so that the efficiency of medicine name normalization can be greatly improved, and the labor labeling cost and the labeling error rate can be reduced. In addition, the medicine classification corresponding to the medicine name is further determined according to the preset data table, so that a data basis can be provided for establishing a perfect medicine inquiry system.

Corresponding to the method embodiment, the present disclosure also provides a drug name processing apparatus, which may be used to execute the method embodiment.

Fig. 6 schematically shows a block diagram of a drug name processing device in an exemplary embodiment of the present disclosure.

Referring to fig. 6, the medicine name processing apparatus 600 may include:

a word segmentation module 61 configured to obtain a plurality of words of the drug name;

a data matching module 62 configured to query corresponding data of the plurality of segments in a preset data table;

a standard name determining module 63 configured to determine standard medicine names corresponding to the plurality of participles according to the corresponding data;

and a standard name recording module 64 configured to record the corresponding relationship between the standard medicine name and the medicine name.

In some embodiments, the drug name processing apparatus 600 may further include:

a drug classification determining module 65 configured to determine a vocabulary category with the highest preset priority according to the vocabulary categories corresponding to the plurality of corresponding data;

and the medicine classification recording module 66 is configured to search the standard medicine name corresponding to the corresponding data in the preset data table according to the corresponding data corresponding to the vocabulary category with the highest priority.

Since the functions of the apparatus 600 have been described in detail in the corresponding method embodiments, the disclosure is not repeated herein.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Accordingly, various aspects of the present invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 700 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 7, electronic device 700 is in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, and a bus 730 that couples various system components including the memory unit 720 and the processing unit 710.

Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs the steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification. For example, the processing unit 710 may perform step S1 as shown in fig. 1: acquiring a plurality of word segments of the drug name; step S2: inquiring corresponding data of the multiple participles in a preset data table; and step S3: determining standard medicine names corresponding to the multiple participles according to the corresponding data; step S7: and recording the corresponding relation between the standard medicine name and the medicine name.

The memory unit 720 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 7201 and/or a cache memory unit 7202, and may further include a read only memory unit (ROM) 7203.

The memory unit 720 may also include programs/utilities 7207 having a set (at least one) of program modules 7205, such program modules 7205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 730 may be any representation of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 700 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 700, and/or with any device (e.g., router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 750. Also, the electronic device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 760. As shown, the network adapter 760 communicates with the other modules of the electronic device 700 via the bus 730. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.

Referring to fig. 8, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method for processing a drug name, comprising:

acquiring a plurality of participles of the drug name;

inquiring corresponding data of the multiple participles in a preset data table;

determining standard medicine names corresponding to the multiple participles according to the corresponding data;

recording the corresponding relation between the standard medicine name and the medicine name;

wherein the determining the standard medicine names corresponding to the multiple participles according to the corresponding data comprises:

2. The drug name processing method of claim 1, further comprising:

3. The drug name processing method of claim 1, wherein the querying the preset data table for the corresponding data of the plurality of participles comprises:

acquiring word segmentation information of the preset data table;

4. The drug name processing method of claim 3, wherein the vocabulary categories include trade names, common names, standard drug names, dosage forms, acid groups, ester/salt groups, and specifications.

5. The method for processing names of drugs according to claim 1, wherein the searching for the standard drug name corresponding to the corresponding data in the preset data table according to the corresponding data corresponding to the highest priority vocabulary category comprises:

6. The drug name processing method of claim 1, wherein the obtaining of the plurality of participles of the drug name comprises:

setting the word number of the medicine name as a word segmentation range;

and inquiring a corresponding value in the preset vocabulary according to the inquiry keyword, if the corresponding value is inquired, segmenting other characters of the medicine name, and if not, returning to the previous step.

7. A drug name processing apparatus, comprising:

the word segmentation module is used for acquiring a plurality of words of the medicine names;

the standard name determining module is used for determining standard medicine names corresponding to the multiple participles according to the corresponding data;

the standard name recording module is set to record the corresponding relation between the standard medicine name and the medicine name;

wherein the standard name determination module is configured to: determining the vocabulary type with the highest preset priority according to the vocabulary types corresponding to the plurality of corresponding data; and searching the standard medicine name corresponding to the corresponding data in the preset data table according to the corresponding data corresponding to the vocabulary type with the highest priority.

8. An electronic device, comprising:

a memory; and

a processor coupled to the memory, the processor configured to perform the drug name processing method of any of claims 1-7 based on instructions stored in the memory.

9. A computer-readable storage medium on which a program is stored, the program, when executed by a processor, implementing the drug name processing method according to any one of claims 1 to 7.