CN113362964A - Method and system for processing medicine data and storage medium thereof - Google Patents
Method and system for processing medicine data and storage medium thereof Download PDFInfo
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- CN113362964A CN113362964A CN202110616331.8A CN202110616331A CN113362964A CN 113362964 A CN113362964 A CN 113362964A CN 202110616331 A CN202110616331 A CN 202110616331A CN 113362964 A CN113362964 A CN 113362964A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/12—Use of codes for handling textual entities
- G06F40/126—Character encoding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/237—Lexical tools
- G06F40/247—Thesauruses; Synonyms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Abstract
The present disclosure relates to a method for processing drug data, a system for processing drug data, and a computer-readable storage medium, the method for processing drug data includes acquiring original medical data, the original medical data including original drug information; analyzing the original medicine information to obtain basic information; comparing a medicine information table, and judging whether the name information is matched with the information in the medicine information table, wherein the medicine information table at least comprises a standard medicine name, a synonym corresponding to the standard medicine name and a code corresponding to the standard medicine name; and under the condition that the matching result meets a preset condition, generating target medical data based on the matched information in the medicine information table. The processing device includes an acquisition unit; an analysis module; a matching module; and generating a module. All medicine information in the original medical data can be accurately and uniformly subjected to standardized processing through the embodiments of the disclosure.
Description
Technical Field
The disclosure relates to the technical field of medical data intelligent processing, in particular to a method and a system for processing medicine data and a storage medium thereof.
Background
At present, in the process of processing medical data containing medicine information, due to the fact that various names of medicines are written clinically, for the same medicine, only commodity names are recorded in original medical data, and only general names are recorded in original medical data, data processing is very inconvenient, and medical analysis, research and diagnosis are affected.
Disclosure of Invention
The present disclosure is intended to provide a method for processing drug data, a system for processing medical data, and a computer-readable storage medium, which can accurately and uniformly perform standardized processing on all drug information in original medical data.
According to one aspect of the present disclosure, there is provided a method for processing drug data, including:
S1acquiring original medical data, wherein the original medical data comprises original medicine information;
S2analyzing original medicine information to obtain basic information;
S3comparing a medicine information table, and judging whether the basic information is matched with the information in the medicine information table, wherein the medicine information table comprises a standard medicine name, a synonym corresponding to the standard medicine name and a code corresponding to the standard medicine name;
S4and if the matching result meets the preset condition, generating the target medical data based on the information in the matched medicine information table.
Further, said S2The basic information in the step includes name information.
Further, said S3The method comprises the following steps: s31Comparing the name information with synonyms in the medicine information table; s32Analyzing the similarity between the name information and the synonym in the medicine information table.
Further, said S4The method comprises the following steps: s41Selecting synonyms in the medicine information table with highest similarity; s42Acquiring a standard medicine name corresponding to the synonym; s43Using the standard medicineThe name generates target medical data.
Further, the basic information also includes dosage form information and medication route information.
Further, said S4The method also comprises the following steps; s422Acquiring a code corresponding to a standard medicine name corresponding to the synonym; if the corresponding codes are multiple, determining a final code based on the dosage form information and the medication route information; s433Generating the target medical data by the standard medicine name and the code.
Further, the original medicine information is preprocessed, and the preprocessing mode comprises at least one of the following modes: converting characters; character regular matching; and (5) character cleaning.
Further, the construction method of the drug information table includes:
S01: establishing a medicine information table based on the ATC code; s02: and maintaining a medicine information table by adding synonyms with the same meaning as the ATC name representation.
Further, according to one aspect of the present disclosure, there is provided a medical data processing system including:
an acquisition unit configured to acquire raw medical data, the raw medical data containing raw drug information;
the analysis module is configured to analyze at least name information in the original medicine information;
the matching module is configured to compare a medicine information table and judge whether the name information is matched with information in the medicine information table, wherein the medicine information table at least comprises a standard medicine name and a synonym corresponding to the standard medicine name;
a generating module configured to generate target medical data based on the matched information in the drug information table if the matching result satisfies a preset condition.
Further, in some embodiments, among others, a drug information table construction module is further included, which is configured to:
establishing a medicine information table based on the ATC code;
and maintaining a medicine information table by adding synonyms with the same meaning as the ATC name representation.
Further, according to one aspect of the present disclosure, a computer-readable storage medium having stored thereon computer-executable instructions for execution by a processor is provided.
The invention has the beneficial effects that:
the medical data processing method, the medical data processing system and the computer readable storage medium of various embodiments of the present disclosure are achieved by acquiring original medical data, the original medical data containing original drug information; analyzing original medicine information to obtain basic information; comparing a medicine information table, and judging whether the basic information is matched with information in the medicine information table, wherein the medicine information table comprises a standard medicine name, a synonym corresponding to the standard medicine name and a code corresponding to the standard medicine name; and under the condition that the matching result meets the preset condition, generating target medical data based on the matched information in the medicine information table, so that the original medicine information can be subjected to standardized unified processing by combining the medicine information table on the basis of analyzing the original medicine information, and the original medicine information in the original medical data is normalized by using the standard medicine name. The method and the device can accurately, effectively and intuitively present the medicine name standardization result to the user, the processing performance reaches 99%, and accurate target medical data can be completely provided, so that the accuracy and the efficiency of medical research and medical diagnosis and treatment are improved.
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, as claimed.
Drawings
In the drawings, which are not necessarily drawn to scale, like reference numerals may designate like components in different views. Like reference numerals with letter suffixes or like reference numerals with different letter suffixes may represent different instances of like components. The drawings illustrate various embodiments generally, by way of example and not by way of limitation, and together with the description and claims, serve to explain the disclosed embodiments.
FIG. 1 is a flowchart of an embodiment of a method for processing drug data.
FIG. 2 is an architecture diagram of one embodiment of a system for a method of processing drug data.
Fig. 3 is a diagram of a medicine information table in a specific implementation of a medicine data processing method.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described below clearly and completely with reference to the accompanying drawings of the embodiments of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are within the scope of protection of the disclosure.
Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items.
To maintain the following description of the embodiments of the present disclosure clear and concise, a detailed description of known functions and known components have been omitted from the present disclosure.
The present disclosure relates to medical data processing for the standardized unified processing of drug information. In the process of processing medical data containing medicine information, due to the fact that various names of medicines are written clinically, for the same medicine, only commodity names are recorded in original medical data, and only general names are recorded in original medical data, so that data processing is very inconvenient, and medical analysis, research and diagnosis are affected. For example, calcium carbonate has many different names as a pharmaceutical product, including: landa, kateli, syndali, guyuan, calcium carbonate oral suspension, calcium carbonate chewable tablets, calcium carbonate dry suspension, calcium carbonate effervescent tablets, calcium carbonate effervescent granules, calcium carbonate tablets, calcium carbonate capsules, calcium carbonate granules, nanoka, calcium tablets, both of which are named differently and of which the pharmaceutical form (e.g. dosage form) differs. In the current standard and unification mode for drug names, there is an ATC code (an anatomical Therapeutic and chemical classification system, abbreviated as ATC (analog Therapeutic chemical)) system, which is an official classification system for drugs by the world health organization, wherein the ATC code includes an ATC drug name and an ATC code, and the ATC code has 7 digits, wherein the 1 st, 4 th and 5 th digits are letters, and the 2 nd, 3 th, 6 th and 7 th digits are numbers.
As one aspect, as shown in fig. 1, an embodiment of the present disclosure provides a method for processing drug data, including:
acquiring original medical data, wherein the original medical data comprises original medicine information;
analyzing original medicine information to obtain basic information;
comparing a medicine information table, and judging whether the basic information is matched with information in the medicine information table, wherein the medicine information table comprises a standard medicine name, a synonym corresponding to the standard medicine name and a code corresponding to the standard medicine name;
and if the matching result meets the preset condition, generating target medical data based on the information in the matched medicine information table.
One of the inventive concepts of the present disclosure is to standardize and unify the original drug information by combining with the drug information table on the basis of analyzing the original drug information, standardize the original drug information in the original medical data by the standard drug name, and accurately, effectively and intuitively present the drug name standardization result to the user.
The original medical data in the embodiments of the present disclosure, which belongs to the data source, need not be particularly limited, and may be historical data or current real-time data. From the aspect of data format, it can be medical history text data, video data, audio data, etc., as long as it can identify the original medicine information that can be contained therein by identification means, such as text recognition (e.g., NLP recognition, OCR recognition, etc.), voice recognition, video image recognition, etc., or the medicine information content identified by character splitting, word and sentence splitting, etc., or the like. In some embodiments, the original medical data of the present disclosure may also be included in medical records, diagnostic books, and prescriptions, such that it is sufficient to extract the corresponding drug information to be the processing object of the present disclosure. In various data information processing scenarios, the original medical data in the embodiment of the present disclosure may be a medical text of the original medical data input by a user through an interactive interface and an input device, and may be used for interpreting related medicine information manually, by a machine, and the like in a labeling or parsing manner.
In various embodiments, in the implementation process of the present disclosure, the name information in the original medical drug information of the present embodiment, and the dosage form information and the medication route information extracted in necessary scenes may be extracted through a neural network model. In the implementation process, the specific neural network model is not particularly limited, and can be implemented by adopting a neural network model which meets the requirements and is matched with the architecture. According to the more preferable scheme, the extraction accuracy of various information can be further optimized through the adaptive neural network model on the basis of the pre-training model. For extracting medical entity content, entity extraction can be performed based on a text recognition mode, for example, a text recognition mode such as NLP (natural language processing), and clauses and classifications are performed on entities by combining medical concepts. More preferably, the entity can be analyzed by combining the international ATC code and the ATC name, and the extraction can be carried out on the basis of combining the analysis result of the proper medical rule.
In some embodiments, the comparing the drug information table of the present disclosure to determine whether the name information matches information in the drug information table includes:
comparing the name information with synonyms in the medicine information table;
and analyzing the similarity between the name information and the synonym in the medicine information table.
Specifically, in the present embodiment, the determination method of the matching degree may be calculated based on the number of words included in the name information and the synonyms in the drug information table, for example, the matching degree may be calculated by the number of words in intersection between the two and the number of words in union of the two, or may be referred to as the similarity between the two.
In some embodiments, the generating target medical data based on the matched information in the medicine information table in the case that the matching result satisfies a preset condition includes:
selecting synonyms in the medicine information table with the highest similarity;
acquiring a standard medicine name corresponding to the synonym;
and generating target medical data by the standard medicine name.
Specifically, as shown in fig. 3, the name information in the original drug information in this embodiment is matched with each synonym in the drug information table, for example, the synonym can be matched with "diquinamine, diquinamine buccal tablet (prallezine), prallezine, cetylpyridinium chloride buccal tablet", and the like, then this embodiment may preferably select the synonym with the highest similarity, for example, "diquinamine", is obtained by calculating the number of words in intersection and the number of words in union between the two.
In some embodiments, the drug information table of the present disclosure includes a code corresponding to a standard drug name;
the generating of the target medical data based on the matched information in the drug information table under the condition that the matching result meets the preset condition further comprises:
acquiring a code corresponding to the standard medicine name corresponding to the synonym;
and generating target medical data by the standard medicine name and the code.
Specifically, for example, referring to fig. 3, the raw medical data input of the present embodiment includes information of clinical drug name, dosage form, and route of administration, for example, drug information of "aspirin injection, and injection", and by means of the matching manner in the present embodiment, synonyms about aspirin in the drug information table can be matched, so as to guide the ATC code about ATC named "aspirin". Based on the ATC name and the ATC code, as information for generating the final target medical data.
In some embodiments, the analyzing the original drug information to obtain the basic information according to the present disclosure further includes:
analyzing the original medicine information to obtain two basic information, namely dosage form information and medication route information;
if the matching result meets the preset condition, generating target medical data based on the information in the matched medicine information table, and further comprising:
acquiring a code corresponding to the standard medicine name corresponding to the synonym; if the corresponding codes are multiple, determining a final code based on the dosage form information and the medication route information;
generating target medical data by the standard medicine name and the code;
specifically, when there is a case where different ATC codes correspond to the same ATC name, for example, refer to fig. 3 where "a 01AB 0024" and "S01 AX 0027" both correspond to the ATC name "cetylpyridinium chloride". The embodiment can determine the ATC code required for generating the target medical data finally by combining the dosage form and the medication route for distinguishing on the basis of analyzing the dosage form information and the medication route information from the original medicine information.
In some embodiments, the processing method of the present disclosure further includes:
preprocessing the original medicine information, wherein the preprocessing mode comprises at least one of the following modes:
1. converting characters;
2. character regular matching;
3. and (5) character cleaning.
In this embodiment, the original drug information, or the extracted name information, dosage form information, route information, and the like may be preprocessed by referring the original drug information to the standard information table. In this embodiment, the problem of irrelevant information and information conversion can be solved by preprocessing the original medicine information on the basis of AI identification and processing. Among them, the character conversion can be realized as the conversion of characters, letter case, roman numerals and arabic numerals, the conversion of chinese numeral sequence numbers and arabic numerals, etc.; the characters are regularly matched, and useless information can be cleaned in a regular matching mode; character cleaning, deletion of high-frequency useless characters and the like can be realized by cleaning irrelevant words and special symbols. The method is particularly suitable for what preprocessing mode, and the adaptive preprocessing mode can be correspondingly adopted according to the actual content, the composition mode, the information format and other conditions of the actually extracted medicine specific information.
In some embodiments, the method for constructing the drug information table of the present disclosure includes:
establishing a medicine information table based on the ATC code;
and maintaining a medicine information table by adding synonyms with the same meaning as the ATC name representation.
Specifically, in combination with the foregoing, the medicine information table of the present disclosure may be established and maintained manually, or may be continuously and iteratively established and maintained through a pre-trained deep learning model based on an ATC code system.
As one of the aspects of the present disclosure, as shown in fig. 2, the present disclosure also provides a processing system of medical data, including:
an acquisition unit configured to acquire raw medical data, the raw medical data containing raw drug information;
the analysis module is configured to analyze at least name information in the original medicine information;
the matching module is configured to compare a medicine information table and judge whether the name information is matched with information in the medicine information table, wherein the medicine information table comprises a standard medicine name, a synonym corresponding to the standard medicine name and a code corresponding to the standard medicine name;
a generating module configured to generate target medical data based on the matched information in the drug information table if the matching result satisfies a preset condition.
In some embodiments, the obtaining unit of the present disclosure may be an input device, a screen capture device, a text recognition device, etc., and is intended to obtain medical data containing original medicine information.
In some embodiments, the matching module of the present disclosure is further configured for: comparing the name information with synonyms in the medicine information table; and analyzing the name information, the similarity of synonyms in the medicine information table, the dosage form information and the medication route information.
In some embodiments, the matching module of the present disclosure is further configured for: the configuration can be specifically as follows:
under the condition that the matching result meets the preset condition, generating target medical data based on the matched information in the medicine information table, wherein the target medical data comprises the following steps:
selecting synonyms in the medicine information table with the highest similarity;
acquiring a standard medicine name corresponding to the synonym;
and generating target medical data by the standard medicine name.
The generating of the target medical data based on the matched information in the drug information table under the condition that the matching result meets the preset condition further comprises:
acquiring a code corresponding to the standard medicine name corresponding to the synonym; if the corresponding codes are multiple, determining a final code based on the dosage form information and the medication route information;
and generating target medical data by the standard medicine name and the code.
In some embodiments, the processing system of the present disclosure further comprises a drug information table building module configured to:
establishing a medicine information table based on the ATC code;
and maintaining a medicine information table by adding synonyms with the same meaning as the ATC name representation.
In combination with the foregoing, the medicine information table of the present disclosure may be established and maintained manually, or may be based on an ATC code system, and the medicine information table establishing module in this embodiment may continuously and iteratively establish and maintain the medicine information table of each embodiment of the present disclosure through a pre-trained deep learning model.
As one of the aspects of the present disclosure, the present disclosure also provides a computer-readable storage medium having stored thereon computer-executable instructions, which, when executed by a processor, mainly implement the above-mentioned method for processing drug data.
In some embodiments, a processor executing computer-executable instructions may be a processing device including more than one general-purpose processing device, such as a microprocessor, Central Processing Unit (CPU), Graphics Processing Unit (GPU), or the like. More specifically, the processor may be a Complex Instruction Set Computing (CISC) microprocessor, Reduced Instruction Set Computing (RISC) microprocessor, Very Long Instruction Word (VLIW) microprocessor, processor running other instruction sets, or processors running a combination of instruction sets. The processor may also be one or more special-purpose processing devices such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a system on a chip (SoC), or the like.
In some embodiments, the computer-readable storage medium may be a memory, such as a read-only memory (ROM), a random-access memory (RAM), a phase-change random-access memory (PRAM), a static random-access memory (SRAM), a dynamic random-access memory (DRAM), an electrically erasable programmable read-only memory (EEPROM), other types of random-access memory (RAM), a flash disk or other form of flash memory, a cache, a register, a static memory, a compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD) or other optical storage, a tape cartridge or other magnetic storage device, or any other potentially non-transitory medium that may be used to store information or instructions that may be accessed by a computer device, and so forth.
In some embodiments, the computer-executable instructions may be embodied as a plurality of program modules that collectively implement the drug data processing method according to any one of the present disclosure.
The present disclosure describes various operations or functions that may be implemented as or defined as software code or instructions. The acquisition unit may be implemented as software code or instruction modules stored on a memory, which when executed by a processor may implement the respective steps and methods.
Such content may be source code or differential code ("delta" or "patch" code) that may be executed directly ("object" or "executable" form). A software implementation of the embodiments described herein may be provided through an article of manufacture having code or instructions stored thereon, or through a method of operating a communication interface to transmit data through the communication interface. A machine or computer-readable storage medium may cause a machine to perform the functions or operations described, and includes any mechanism for storing information in a form accessible by a machine (e.g., a computing display device, an electronic system, etc.), such as recordable/non-recordable media (e.g., Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media, optical storage media, flash memory display devices, etc.). The communication interface includes any mechanism for interfacing with any of a hardwired, wireless, optical, etc. medium to communicate with other display devices, such as a memory bus interface, a processor bus interface, an internet connection, a disk controller, etc. The communication interface may be configured by providing configuration parameters and/or transmitting signals to prepare the communication interface to provide data signals describing the software content. The communication interface may be accessed by sending one or more commands or signals to the communication interface.
The computer-executable instructions of embodiments of the present disclosure may be organized into one or more computer-executable components or modules. Aspects of the disclosure may be implemented with any number and combination of such components or modules. For example, aspects of the disclosure are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments may include different computer-executable instructions or components having more or less functionality than illustrated and described herein.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more versions thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the foregoing detailed description, various features may be grouped together to streamline the disclosure. This should not be interpreted as an intention that a disclosed feature not claimed is essential to any claim. Rather, the subject matter of the present disclosure may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the disclosure should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above embodiments are merely exemplary embodiments of the present disclosure, which is not intended to limit the present disclosure, and the scope of the present disclosure is defined by the claims. Various modifications and equivalents of the disclosure may occur to those skilled in the art within the spirit and scope of the disclosure, and such modifications and equivalents are considered to be within the scope of the disclosure.
Claims (11)
1. A method for processing drug data is characterized by comprising the following steps:
S1acquiring original medical data, wherein the original medical data comprises original medicine information;
S2analyzing original medicine information to obtain basic information;
S3comparing a medicine information table to judge whether the basic information is matched with the information in the medicine information table, wherein the medicine information table comprises a standard medicine name, a synonym corresponding to the standard medicine name and a standardA code corresponding to the medicine name;
S4and if the matching result meets the preset condition, generating the target medical data based on the information in the matched medicine information table.
2. The method for processing drug data according to claim 1, wherein: said S2The basic information in the step includes name information.
3. The method for processing drug data according to claim 2, wherein: said S3The method comprises the following steps:
S31comparing the name information with synonyms in the medicine information table;
S32analyzing the similarity between the name information and the synonym in the medicine information table.
4. The method for processing drug data according to claim 2, wherein: said S4The method comprises the following steps:
S41selecting synonyms in the medicine information table with highest similarity;
S42acquiring a standard medicine name corresponding to the synonym;
S43target medical data is generated by the standard drug name.
5. The method for processing drug data according to claim 2, wherein: the basic information also includes dosage form information and route of administration information.
6. The method for processing drug data according to claim 5, wherein: said S4The method also comprises the following steps;
S422acquiring a code corresponding to a standard medicine name corresponding to the synonym; if the corresponding codes are multiple, determining a final code based on the dosage form information and the medication route information;
S433generating the target medical data by the standard medicine name and the code.
7. The method for processing drug data according to claim 1, wherein:
S2the method for analyzing the original medicine information in the steps comprises the following steps: character conversion or character regular matching or character cleaning or a combination of character conversion and character regular matching or a combination of character regular matching and character cleaning or a combination of the three ways.
8. The method for processing drug data according to any one of claims 1 to 7, wherein: the construction mode of the medicine information table comprises the following steps:
S01: establishing a medicine information table based on the ATC code;
S02: and maintaining a medicine information table by adding synonyms with the same meaning as the ATC name representation.
9. The system for a method of processing pharmaceutical data according to claim 1, wherein: the method comprises the following steps:
an acquisition unit configured to acquire raw medical data, the raw medical data containing raw drug information;
and the analysis module is configured to analyze the original medicine information to obtain basic information.
The matching module is configured to compare a medicine information table and judge whether the name information is matched with information in the medicine information table, wherein the medicine information table comprises a standard medicine name, a synonym corresponding to the standard medicine name and a code corresponding to the standard medicine name;
a generating module configured to generate target medical data based on the matched information in the drug information table if the matching result satisfies a preset condition.
10. The system according to claim 10, wherein the system further comprises: further comprising:
a drug information table building module configured for: establishing a medicine information table based on the ATC code; and maintaining a medicine information table by adding synonyms with the same meaning as the ATC name representation.
11. A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement:
the method for processing drug data according to any one of claims 1 to 8.
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