CN112668280A - Medical data processing method and device and storage medium - Google Patents
Medical data processing method and device and storage medium Download PDFInfo
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Abstract
The present disclosure relates to a processing method of medical data, a processing apparatus of medical data, and a computer-readable storage medium, the processing method including acquiring original medical data, the original medical data containing original drug information; analyzing at least name information in the original medicine information; comparing a drug information base, and judging whether the name information is matched with information in the drug information base, wherein the drug information base is configured with a standard drug name and a first synonym corresponding to the standard drug 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 base. 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 present disclosure relates to the field of medical data intelligent processing technology, and in particular, to a medical data processing method, a medical data processing apparatus, and a computer-readable storage medium.
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 medical data processing method, a medical data processing apparatus, 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 medical data, including:
acquiring original medical data, wherein the original medical data comprises original medicine information;
analyzing at least name information in the original medicine information;
comparing a drug information base, and judging whether the name information is matched with information in the drug information base, wherein the drug information base is configured with a standard drug name and a first synonym corresponding to the standard drug 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 base.
In some embodiments, wherein the determining whether the name information matches information in the drug information base against the drug information base comprises:
comparing the name information with a first synonym in the drug information base;
and analyzing the similarity between the name information and the first synonym in the medicine information base.
In some embodiments, the generating target medical data based on the matched information in the drug information base in the case that the matching result satisfies a preset condition includes:
selecting a first synonym in the drug information base with highest similarity;
acquiring a standard medicine name corresponding to the first synonym;
and generating target medical data by the standard medicine name.
In some embodiments, wherein the drug information base is configured with a code corresponding to the standard drug name and a second synonym corresponding to the code;
the analyzing of at least the name information in the original medicine information includes: analyzing the dosage form information and the medication route information in the original medicine information;
in the case where multiple codes corresponding to the same standard drug name are present, the method further comprises:
comparing the dosage form information and/or the medication route information with a second synonym in the drug information base;
and analyzing the similarity between the dosage form information and/or the medication route information and the second synonym in the medicine information base.
In some embodiments, further comprising:
selecting a second synonym in the drug information base with the highest similarity;
and acquiring the code corresponding to the second synonym.
In some embodiments, among others, further comprising:
preprocessing the original medicine information, wherein the preprocessing mode comprises at least one of the following modes:
converting characters;
character regular matching;
and (5) character cleaning.
In some embodiments, the drug information library is constructed in a manner including:
establishing a medicine information base based on the ATC code;
the drug information base is maintained at least by adding a first synonym having the same meaning as the ATC name representation.
According to one aspect of the present disclosure, there is provided a processing apparatus of medical data, comprising:
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 drug information base and judge whether the name information is matched with information in the drug information base, wherein the drug information base is configured with a standard drug name and a first synonym corresponding to the standard drug name;
a generating module configured to generate target medical data based on the matched information in the drug information base if the matching result satisfies a preset condition.
In some embodiments, there is further included a drug information library building module configured for:
establishing a medicine information base based on the ATC code;
the drug information base is maintained at least by adding a first synonym having the same meaning as the ATC name representation.
According to one aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement:
the method for processing medical data according to the above.
The medical data processing method, the medical data processing device and the computer readable storage medium of various embodiments of the present disclosure are achieved by acquiring original medical data, wherein the original medical data contains original drug information; analyzing at least name information in the original medicine information; comparing a drug information base, and judging whether the name information is matched with information in the drug information base, wherein the drug information base at least comprises a standard drug name and a first synonym corresponding to the standard drug 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 base, so that the original medicine information can be subjected to standardized unified processing by combining the medicine information base on the basis of analyzing the original medicine information, and the original medicine information in the original medical data is normalized by 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 shows a flow chart of a method of processing medical data to which an embodiment of the present disclosure relates;
fig. 2 shows an architecture diagram of a medical data processing apparatus according to an embodiment of the present disclosure;
FIG. 3 illustrates a drug information library according to an embodiment of the present disclosure, partially shown by way of example in the form of a first drug information table;
fig. 4 shows a drug information library according to an embodiment of the present disclosure, which is partially shown in the form of a second drug information table for illustration.
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 medical data, including:
s101: acquiring original medical data, wherein the original medical data comprises original medicine information;
s102: analyzing at least name information in the original medicine information;
s103: comparing a drug information base, and judging whether the name information is matched with information in the drug information base, wherein the drug information base is configured with a standard drug name and a first synonym corresponding to the standard drug name;
s104: 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 base.
One of the inventive concepts of the present disclosure is to standardize and unify the original drug information by combining with a drug information library 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 base of the present disclosure to determine whether the name information matches information in the drug information base includes:
comparing the name information with a first synonym in the drug information base;
and analyzing the similarity between the name information and the first synonym in the medicine information base.
Specifically, in the present embodiment, the matching degree may be calculated based on the number of words included in the name information and the first synonym in the drug information base, 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 similarity of the present disclosure may be determined based on common characteristics in a common order that the name information and the first synonym in the drug information base both contain. The common feature of the present embodiment may be that the character string of the name information (first character string) and the character string of the drug information library (second character string) contain a plurality of identical character segments in a common arrangement order. For example, the first character string is arranged from left to right and includes a plurality of specific characters in this order, and the second character string is arranged from left to right and also includes the same plurality of specific characters in this order. Of course, some preferred schemes may be adopted in a progressive manner, such as that the first character string and the second character string contain the largest and same number of character sequences in the arrangement order, and that the first character string and the second character string contain the largest and same number of character sequences in the arrangement order and are arranged in series, which is intended to ensure the accuracy of the processing result by determining the longest common subsequence. For example, a keyword comprises "abbcdheff", a standard word or first synonym comprises "bbcedhff", the longest common subsequence is "bbcdhff", and characters need not necessarily be contiguous. Assuming that the length of the longest common subsequence is Lg, the length of the keyword is L1, and the length of the standard word or the first synonym is L2, the matching can be determined by the operational relationship and the proportional relationship between the three, for example, the similarity calculation can be defined as: Lg/(L1+ L2-Lg). Specifically, in the above embodiment, the length of the longest common subsequence "bbcdhff" is Lg ═ 7, the length of the keyword "abbcdheff" is L1 ═ 9, and the length of the standard word or the first synonym "bbcedhff" is L2 ═ 8, then based on the longest common subsequence, the similarity in this embodiment can be determined to be 7/(9+8-7) ═ 0.7.
In some embodiments, the generating target medical data based on the matched information in the drug information base in the case that the matching result satisfies a preset condition includes:
selecting a first synonym in the drug information base with highest similarity;
acquiring a standard medicine name corresponding to the first synonym;
and generating target medical data by the standard medicine name.
Specifically, referring to fig. 3, the drug information library of this embodiment may implement configuration of a standard drug name and a first synonym corresponding to the standard drug name by setting a first drug information table. Of course, in combination with the context, the drug information library of this embodiment may implement the corresponding functions of the first drug information table and the second drug information table of the embodiments of the present disclosure in a manner of collectively setting the information tables, or may implement the corresponding functions by respectively setting the independent first drug information table and the independent second drug information table.
In a specific embodiment, the name information in the original drug information in this embodiment may be matched with each first synonym in the drug information base, for example, the first synonym may be matched with the first synonyms such as "diquinamine, diquinamine buccal tablet (prallezine), prallezine, cetylpyridinium chloride buccal tablet", and the like, and then this embodiment may preferably select the first synonym with the highest similarity, for example, "diquinamine", may be obtained by calculating the number of words in intersection between the two and the number of words in union of the two.
In some embodiments, the drug information library of the present disclosure further comprises a code corresponding to a standard drug name;
the generating of the target medical data based on the matched information in the drug information base 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 first synonym;
and generating target medical data by the standard medicine name and the code.
Specifically, for example, as shown in fig. 3, the raw medical data input of the present embodiment includes information of clinical drug names, dosage forms, and medication routes, for example, drug information of "aspirin injection, and injection", and the first synonym about aspirin in the drug information base can be matched by the matching method in the present embodiment, so that at least the ATC name referred to as "aspirin" is specified, and the information for generating the final target medical data based on the ATC name is realized.
In some embodiments, the drug information repository of the present disclosure is configured with a code corresponding to the standard drug name and a second synonym corresponding to the code;
the analyzing of at least the name information in the original medicine information includes: analyzing the dosage form information and the medication route information in the original medicine information;
in the case where multiple codes corresponding to the same standard drug name are present, the method further comprises:
comparing the dosage form information and/or the medication route information with a second synonym in the drug information base;
and analyzing the similarity between the dosage form information and/or the medication route information and the second synonym in the medicine information base.
The idea of this embodiment is to determine the ATC code required for generating the target medical data finally in this embodiment, when there is a case where different ATC codes correspond to the same ATC name, for example, referring to fig. 3, where "a 01AB 0024" and "S01 AX 0027" both correspond to the ATC name "cetylpyridinium chloride", based on analyzing the information of the dosage form and the information of the administration route from the original drug information, by differentiating the dosage form and the administration route.
Specifically, with reference to fig. 4, as described above, the drug information library of this embodiment may implement configuration of the code and the second synonym corresponding to the code by setting the second drug information table. The second medicine information table may be integrally provided with the first medicine information table, or may be separately provided. This disclosure is illustrated by way of example in fig. 3 and 4 as separate arrangements. Fig. 4 illustrates an application scenario of the present embodiment by taking an example that "metronidazole" is a standard drug name and corresponds to a plurality of codes. This embodiment may be handled by a second drug information table similar to the first drug information table. That is, for different codes of the same standard name, the embodiment also establishes and maintains a second synonym for each code, for example, establishes a second drug information table about "chlorhexidine":
chlorhexidine D08AC02 "solution; powder "(dosage form and administration route correspond to a code)
Chlorhexidine D09AA12 ointment (dosage form and administration route corresponding to a code)
Chlorhexidine G01AX0019 Chlorhexidine acetate suppository (dosage form and administration route correspond to one code)
Chlorhexidine C05AX0001 "hemorrhoid; thrombolytic chlorhexidine "(dosage form and route of administration correspond to one code).
It can be understood by those skilled in the art that in this application scenario, the ATC code is not determined based on the determination of the ATC drug name, and the second synonym for code in this embodiment is mainly intended to distinguish codes.
Furthermore, the raw medical data input of the present embodiment is in a scenario including information of clinical drug name, dosage form, and administration route, such as "metronidazole; tablet, oral administration and the like, the processing method of the embodiment can analyze metronidazole; tablet, oral "information regarding dosage form, route of administration, which is compared to the second synonym in the second drug information table exemplified in fig. 4. The second synonym includes the words "tooth, mouth, etc. that are semantically similar to" tablet, oral ", and these words are each uniquely associated with the ATC code" a01AB17 ".
In some embodiments, the processing method of the present disclosure further includes:
selecting a second synonym in the drug information base with the highest similarity;
and acquiring the code corresponding to the second synonym.
In a specific embodiment, the method for determining the similarity between the dosage form information and/or the medication route information and the second synonym in the drug information base can be used in the method for determining the similarity between the first synonym, for example, the method can be determined based on the common features of the dosage form, the medication route information and the second synonym in the second drug information table, which are arranged in a common sequence, for example, the similarity is quantified by means of the longest common subsequence, and the accuracy of the processing result is ensured by means of the determination of the longest common subsequence.
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:
converting characters;
character regular matching;
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.
In some specific embodiments, the embodiment is described by taking a regular expression as an example for preprocessing. For example, drug names may frequently occur: XXX tablets, XXX injections, XXX granules, XXX capsules, etc., the words including these "tablets, injections, granules, capsules" are words which are high frequency and appear in a plurality of drugs, and the specificity is not strong, which may cause interference if these words are used for searching matching.
For example, the following three names refer to the standard words of the same drug, "Compound Ranitidine":
compound ranitidine tablet
Compound ranitidine capsule
Compound ranitidine granules
The method is realized by adopting a regular expression: sub (r 'tablet | injection | capsule | powder injection | needle | suspension | oral liquid | capsule | oral solution | granule | ointment | disinfectant | spray | solution | gargle | liquid | fluid', ", x)
Adopting a regular expression:
sub (r' \ d + (\\ d)
re.sub(r'[\(\)\(\)\：\:\/\.\*\×\[\]\；\；\&\#\-]',”,x)
Example of results of preprocessing by the regular expression described above:
' naixin ' (40 mg/count ', after treatment becomes ' naixin '
The "glucose injection (500ml soft bag)" was treated to become "glucose injection".
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 drug information library of the present disclosure is constructed in a manner including:
establishing a medicine information base based on the ATC code;
the drug information base is maintained at least by adding a first synonym having the same meaning as the ATC name representation. Of course, in conjunction with the foregoing and following, the drug information base may also be maintained by adding a second synonym. For example, for a first drug information table and a second drug information table, corresponding maintenance is performed by adding a first synonym and a second synonym.
Specifically, in combination with the foregoing, the drug information base 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 apparatus 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 drug information base and judge whether the name information is matched with information in the drug information base, wherein the drug information base is configured with a standard drug name and a first synonym corresponding to the standard drug name;
a generating module configured to generate target medical data based on the matched information in the drug information base 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 a first synonym in the drug information base;
and analyzing the similarity between the name information and the first synonym in the medicine information base.
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 base, wherein the target medical data comprises the following steps:
selecting a first synonym in the drug information base with highest similarity;
acquiring a standard medicine name corresponding to the first synonym;
and generating target medical data by the standard medicine name.
In some embodiments, the drug information repository of the present disclosure is configured with a code corresponding to the standard drug name and a second synonym corresponding to the code;
the analyzing of at least the name information in the original medicine information includes: analyzing the dosage form information and the medication route information in the original medicine information;
in the case that a plurality of codes correspond to the same standard drug name, the matching module is configured to:
comparing the dosage form information and/or the medication route information with a second synonym in the drug information base;
and analyzing the similarity between the dosage form information and/or the medication route information and the second synonym in the medicine information base.
In some embodiments, the matching module of the present disclosure is further configured to select a second synonym in the drug information base with the highest similarity; and acquiring the code corresponding to the second synonym.
In some embodiments, the processing device of the present disclosure further comprises a drug information library building module configured to:
establishing a medicine information base based on the ATC code;
the drug information base is maintained at least by adding a first synonym having the same meaning as the ATC name representation. Of course, in conjunction with the foregoing and following, the drug information base may also be maintained by adding a second synonym. For example, for a first drug information table and a second drug information table, corresponding maintenance is performed by adding a first synonym and a second synonym.
In combination with the foregoing, the drug information base of the present disclosure may be established and maintained manually, or may be based on an ATC code system, and the drug information base establishing module in this embodiment may continuously and iteratively establish and maintain the drug information base of each embodiment of the present disclosure through a pre-trained deep learning model.
In particular, one of the inventive concepts of the present disclosure is directed to a method for generating a medical image by obtaining raw medical data, the raw medical data containing raw drug information; analyzing at least name information in the original medicine information; comparing a drug information base, and judging whether the name information is matched with information in the drug information base, wherein the drug information base at least comprises a standard drug name and a first synonym corresponding to the standard drug 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 base, so that the original medicine information can be subjected to standardized unified processing by combining the medicine information base on the basis of analyzing the original medicine information, and the original medicine information in the original medical data is normalized by 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.
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 a processing method according to the medical data described above, including at least:
acquiring original medical data, wherein the original medical data comprises original medicine information;
analyzing at least name information in the original medicine information;
comparing a drug information base, and judging whether the name information is matched with information in the drug information base, wherein the drug information base is configured with a standard drug name and a first synonym corresponding to the standard drug 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 base.
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 implemented as a plurality of program modules that collectively implement the method for displaying medical images 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 display unit may be implemented as software code or modules of instructions 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 (10)
1. A method of processing medical data, comprising:
acquiring original medical data, wherein the original medical data comprises original medicine information;
analyzing at least name information in the original medicine information;
comparing a drug information base, and judging whether the name information is matched with information in the drug information base, wherein the drug information base is configured with a standard drug name and a first synonym corresponding to the standard drug 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 base.
2. The method of claim 1, wherein the determining whether the name information matches information in the drug information base against a drug information base comprises:
comparing the name information with a first synonym in the drug information base;
and analyzing the similarity between the name information and the first synonym in the medicine information base.
3. The method of claim 2, wherein the generating target medical data based on the matched information in the drug information base in the case that the matching result satisfies a preset condition comprises:
selecting a first synonym in the drug information base with highest similarity;
acquiring a standard medicine name corresponding to the first synonym;
and generating target medical data by the standard medicine name.
4. The method of claim 3, wherein the drug information base is configured with a code and a second synonym corresponding to the code, the code corresponding to the standard drug name;
the analyzing of at least the name information in the original medicine information includes: analyzing the dosage form information and the medication route information in the original medicine information;
in the case where multiple codes corresponding to the same standard drug name are present, the method further comprises:
comparing the dosage form information and/or the medication route information with a second synonym in the drug information base;
and analyzing the similarity between the dosage form information and/or the medication route information and the second synonym in the medicine information base.
5. The method of claim 4, further comprising:
selecting a second synonym in the drug information base with the highest similarity;
and acquiring the code corresponding to the second synonym.
6. The method of claim 1, further comprising:
preprocessing the original medicine information, wherein the preprocessing mode comprises at least one of the following modes:
converting characters;
character regular matching;
and (5) character cleaning.
7. The method of any of claims 1 to 6, wherein the drug information base is constructed in a manner comprising:
establishing a medicine information base based on the ATC code;
the drug information base is maintained at least by adding a first synonym having the same meaning as the ATC name representation.
8. Apparatus for processing medical data, comprising:
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 drug information base and judge whether the name information is matched with information in the drug information base, wherein the drug information base is configured with a standard drug name and a first synonym corresponding to the standard drug name;
a generating module configured to generate target medical data based on the matched information in the drug information base if the matching result satisfies a preset condition.
9. The apparatus of claim 8, further comprising a drug information library building module configured to:
establishing a medicine information base based on the ATC code;
the drug information base is maintained at least by adding a first synonym having the same meaning as the ATC name representation.
10. A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement:
the method of processing medical data according to any one of claims 1 to 7.
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