CN111475686A - Medicine classification method and device, storage medium and intelligent equipment - Google Patents
Medicine classification method and device, storage medium and intelligent equipment Download PDFInfo
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Abstract
The application is suitable for the technical field of information processing, and provides a medicine classification method, a medicine classification device, a storage medium and intelligent equipment. The method comprises the following steps: acquiring an original medication record table, and extracting medication information from the original medication record table; cleaning the medication information according to a preset cleaning rule to obtain a medicine name; matching the medicine name with a medicine universal name in a medicine information standard library, and determining a standard medicine universal name corresponding to the medicine name; acquiring a medicine classification requirement input by a user; and determining a classification label corresponding to the medicine name according to the standard medicine universal name obtained by matching and the medicine classification requirement, wherein the classification label is a label predefined according to the medicine classification requirement and the standard medicine universal name in advance. The method and the device can improve the efficiency of acquiring and utilizing the medicine information and realize effective management of the medicine information.
Description
Technical Field
The application belongs to the technical field of information processing, and particularly relates to a medicine classification method, a medicine classification device, a storage medium and intelligent equipment.
Background
With the rapid development of information technology, the medical industry in China is accelerating the construction of medical informatization. The medical information construction is beneficial to improving the medical treatment efficiency, provides good experience for patients and provides great help for improving the medical service quality. The drug information is an important basis for medical insurance settlement and also an important component of medical informatization construction. The medicine information management system can classify different medicines, can greatly improve the efficiency of utilizing and managing medicine information, and has important significance for developing medical informatization construction.
In the process of classifying medicines, medicines are generally classified according to the characteristics and classification rules of the medicines, however, each hospital is provided with a specific medicine classification standard according to the condition of the hospital, and when medical information is integrated, the medicine information of different hospitals cannot be automatically associated due to the inconsistency of the medicine classification standards, which is not beneficial to improving the efficiency of acquiring the medicine information.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for classifying drugs, a storage medium, and an intelligent device, so as to solve the problem in the prior art that drug information of different hospitals cannot be automatically associated due to inconsistent drug classification standards, which is not favorable for improving efficiency of acquiring and utilizing drug information.
In a first aspect, an embodiment of the present application provides a method for classifying a drug, including:
acquiring an original medication record table, and extracting medication information from the original medication record table;
cleaning the medication information according to a preset cleaning rule to obtain a medicine name;
matching the medicine name with a medicine universal name in a medicine information standard library, and determining a standard medicine universal name corresponding to the medicine name;
acquiring a medicine classification requirement input by a user;
and determining a classification label corresponding to the medicine name according to the standard medicine universal name obtained by matching and the medicine classification requirement, wherein the classification label is a label predefined according to the medicine classification requirement and the standard medicine universal name in advance.
In a possible implementation manner of the first aspect, the step of matching the drug name with a drug common name in a drug information standard library and determining a standard drug common name corresponding to the drug name includes:
calculating the text similarity between the medicine name and the medicine universal name in the medicine information standard library;
and if the text similarity reaches a preset similarity threshold, determining the universal drug name corresponding to the text similarity as the standard universal drug name of the drug name.
Optionally, the step of calculating the text similarity between the drug name and the drug common name in the drug information standard library specifically includes:
acquiring the character string and the character string length of the medicine name and the character string length of the medicine universal name;
calculating the editing distance between the medicine name and the medicine universal name according to the character string and the character string length of the medicine name and the character string length of the medicine universal name, wherein the editing distance refers to the minimum conversion times required for converting the medicine name to be the same as the medicine universal name;
and determining the text similarity between the medicine name and the medicine universal name according to the editing distance.
Optionally, the step of determining the text similarity between the drug name and the drug common name according to the edit distance includes:
calculating a similarity value Sim (a, b) of the drug name and the drug generic name according to the following formula:
wherein lev (a, b) represents an edit distance between the character string a of the drug name and the character string b of the drug common name, len (a) represents a character string length of the character string a, len (b) represents a character string length of the character string b, and max (len (a), len (b)) represents a longer character string length of the character string a and the character string b.
In a possible implementation manner of the first aspect, the step of matching the drug name with a drug common name in a drug information standard library and determining a standard drug common name corresponding to the drug name further includes:
if the text similarity between the medicine names and the medicine names reaches more than one medicine universal name with a preset similarity threshold, determining the medicine universal name corresponding to the text similarity between the medicine names and the medicine names as the undetermined universal name;
acquiring applicable symptom information of the to-be-classified medicine corresponding to the medicine name from the original medication record table;
acquiring applicable symptom information of the standard medicine corresponding to the undetermined universal name from the medicine information standard library;
and determining the undetermined universal name corresponding to the standard medicine with the same applicable symptom information as the medicine to be classified as the standard medicine universal name of the medicine to be classified.
In a possible implementation manner of the first aspect, the step of matching the drug name with a drug common name in a drug information standard library and determining a standard drug common name corresponding to the drug name includes:
matching the medicine name with a medicine universal name in a medicine information standard library;
the successfully matched medicine names are classified into a first class of medicine name set, and the matched medicine universal names are determined as standard medicine universal names corresponding to the medicine names in the first class of medicine name set;
the medicine names which are not successfully matched are classified into a second type medicine name set;
clustering the medicine names in the first kind of medicine name set according to a specified clustering algorithm to obtain a first clustering name subset, wherein the first clustering name subset comprises first clustering names obtained through clustering;
clustering the medicine names in the second type of medicine name set according to a specified clustering algorithm to obtain a second clustering name subset, wherein the second clustering name subset comprises second clustering names obtained through clustering;
and matching the first cluster name with the second cluster name, and determining a standard medicine universal name corresponding to the medicine name in the second medicine name set according to a matching result.
In a second aspect, an embodiment of the present application provides a medicine sorting device, including:
the system comprises a medication information extraction unit, a medication information processing unit and a medication information processing unit, wherein the medication information extraction unit is used for acquiring an original medication record table and extracting medication information from the original medication record table;
the information cleaning unit is used for cleaning the medication information according to a preset cleaning rule to obtain a medicine name;
the universal name matching unit is used for matching the medicine name with the medicine universal name in a medicine information standard library and determining the standard medicine universal name corresponding to the medicine name;
the system comprises a classification requirement acquisition unit, a classification requirement acquisition unit and a classification requirement acquisition unit, wherein the classification requirement acquisition unit is used for acquiring a medicine classification requirement input by a user;
the classification label determining unit is used for determining a classification label corresponding to the medicine name according to the standard medicine universal name and the medicine classification requirement which are obtained through matching, wherein the classification label is a label predefined according to the medicine classification requirement and the standard medicine universal name in advance;
and the medicine classification unit classifies the medicines corresponding to the medicine names according to the classification labels.
In a third aspect, embodiments of the present application provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements a drug sorting method as set forth in the first aspect of the embodiments of the present application.
In a fourth aspect, an embodiment of the present application provides an intelligent device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor, when executing the computer program, implements the drug classification method as set forth in the first aspect of the embodiment of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, which, when run on a terminal device, causes the terminal device to execute the drug sorting method according to the first aspect.
In the embodiment of the application, the original medication record table is obtained, the medication information is extracted from the original medication record table, the medication information is automatically cleaned according to the preset cleaning rule to obtain the medicine name, so that the medicine name used for matching the standard medicine universal name is concise, the matching accuracy is improved, then the medicine name is matched with the medicine universal name in the medicine information standard library, the standard medicine universal name corresponding to the medicine name is determined, the medicine classification requirement input by a user is obtained, then the classification label corresponding to the medicine name is determined according to the standard medicine universal name and the medicine classification requirement obtained by matching, the classification label is the label predefined according to the medicine classification requirement and the standard medicine universal name in advance, and finally the medicine corresponding to the medicine name is classified according to the classification label, this application need not the medicine information of other hospitals of relevance, can improve the efficiency of acquireing and utilizing medicine information greatly to the medicine classification according to the demand simultaneously to the realization carries out effectual management to medicine information.
It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flowchart of an implementation of a drug sorting method provided by an embodiment of the present application;
fig. 2 is a flowchart of a specific implementation of the drug classification method S103 provided in the embodiment of the present application;
fig. 3 is a flowchart illustrating a specific implementation of the drug sorting method S103 according to another embodiment of the present application;
fig. 4 is a flowchart illustrating a specific implementation of the drug sorting method S103 according to still another embodiment of the present application;
FIG. 5 is a flowchart of an implementation of a drug classification method when the drug name cannot be matched with a drug generic name in a drug information standard library according to an embodiment of the present application;
fig. 6 is a block diagram of a medicine sorting device according to an embodiment of the present application;
fig. 7 is a schematic diagram of an intelligent device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The medicine classification method provided by the embodiment of the application can be applied to intelligent devices such as a server, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook and the like, and the specific type of the terminal device is not limited at all in the embodiment of the application.
The embodiment of the application provides a medicine classification method, so that medicine classification information has universality and wider application range, and the efficiency of utilizing and managing the medicine information is greatly improved.
Fig. 1 shows an implementation process of a drug classification method provided by an embodiment of the present application, where the method process includes steps S101 to S105. The specific realization principle of each step is as follows:
s101: an original medication record table is obtained, and medication information is extracted from the original medication record table.
In the embodiment of the application, an original medication record table of a target hospital is obtained, and medication information is extracted from the original medication record table. The intelligent device collects the original medication record form by connecting and accessing the server of the target hospital. The target hospital refers to a hospital to be subjected to medical information integration, and the original medication record table refers to a record table used for recording medication information in the hospital. Further, in order to improve the efficiency of data cleaning and classification, the original medication record table in the specified time period can be obtained, and repeated cleaning and classification of the cleaned and classified medicine information is avoided.
The original medication record table is composed of a plurality of pieces of medication information. And dividing the original medication record table to obtain medication information of one strip of the original medication record table. Specifically, a table structure of the original medication record table is obtained, and the table structure is divided to obtain each piece of medication information in the original medication record table, where the medication information includes, but is not limited to, medication time, dosage, and medicine information.
Further, medicine information is extracted from the medicine information, and the frequency of the same medicine information appearing in the original medicine record table is counted. The purpose of the statistical frequency is to determine the priority of the drug purge. If the resources are limited, the medicine names with high frequency of occurrence are cleaned firstly in the cleaning process.
S102: and cleaning the medication information according to a preset cleaning rule to obtain the name of the medicine.
Specifically, the medication information includes, but is not limited to, medication time, dosage, drug information, administration route, manufacturer, etc., the drug information includes drug names, and the cleaning of the medication information according to a preset cleaning rule refers to removing the other medication information except the drug names in the medication information.
Optionally, this step is implemented by drug name regularization, which refers to reducing the feature dimension of drug information. For example, usage amounts often contain numbers, and manufacturers generally exclude medication information other than the name of the drug by an informational feature. Alternatively, the process is implemented by establishing a standard word library, for example, integrating the frequently occurring drug administration routes into the standard library, deleting words in the drug administration information including the standard library of drug administration routes, and similarly, setting information of the drug administration routes, manufacturers, and the like as target words, and removing the target words in the drug administration information. In this example, information of non-drug names such as administration route (intravenous injection, oral administration, subcutaneous, etc.), usage amount (x.xxmg, X tablets per day, etc.), manufacturer (hamiltu factory, sunofil, etc.) in the medication information was removed to obtain the drug names of the drugs recorded in the original medication record table.
In the embodiment of the application, the names of the medicines obtained by washing are the names of main components in the medicines. For example, the original record is "oral metformin hydrochloride sustained release tablet (sunofil)", and after words such as drug ingredients and the like which are irrelevant to names are removed, only "metformin" is left. This makes it more likely that subsequent drug name matching and word-based similarity clustering will be successful in matching or cluster with the same component.
S103: and matching the medicine name with the medicine universal name in the medicine information standard library, and determining the standard medicine universal name corresponding to the medicine name.
In this embodiment, the standardization of the drug information depends on an existing drug information standard library, where the drug information standard library includes a standard drug common name and all information related to the drug corresponding to the standard drug common name, and the drug information standard library includes the standard drug common name, and performs fuzzy matching between the drug name in the acquired original medication record table and the drug common name in the drug information standard library to determine the standard drug common name corresponding to the drug name.
As an embodiment of the present application, fig. 2 shows a specific implementation flow of step S103 of the drug classification method provided in the embodiment of the present application, which is detailed as follows:
a1: and calculating the text similarity between the medicine name and the medicine universal name in the medicine information standard library. Specifically, the step a1 specifically includes:
a11: and acquiring the character string and the character string length of the medicine name and the character string length of the medicine universal name. The medicine name and the medicine universal name are both represented by character strings, and the character string length of the medicine name and the character string length of the medicine universal name are respectively obtained.
A12: and calculating the editing distance between the medicine name and the medicine universal name according to the character string and the character string length of the medicine name and the character string length of the medicine universal name, wherein the editing distance refers to the minimum conversion times required for converting the medicine name to be the same as the medicine universal name. The transformation includes insertion, deletion and replacement.
A13: and determining the text similarity between the medicine name and the medicine universal name according to the editing distance. Specifically, the similarity value Sim (a, b) of the drug name and the drug common name is calculated according to the following formula (1):
wherein lev (a, b) represents an edit distance between the character string a of the drug name and the character string b of the drug common name, len (a) represents a character string length of the character string a, len (b) represents a character string length of the character string b, and max (len (a), len (b)) represents a longer character string length of the character string a and the character string b.
Optionally, an edit distance lev (a, b) between the character string a of the drug name and the character string b of the drug common name is determined according to the following formula (2):
wherein, leva,b(i, j) refers to the distance between the first i characters in string a and the first j characters in string b. For ease of understanding, this may be considered as a length. Since the first character index of the character string starts from 1, the last edit distance is the distance lev when i ═ a |, and j ═ b |, respectivelya,b(|a|,|b|)；
When min (i, j) is 0, corresponding to the first i characters in the character string a and the first j characters in the character string b, and at this time, i, j has a value of 0, which indicates that one of the character strings a and b is an empty string, only the next single character editing operation is needed to switch from a to b, so the editing distance between them is max (i, j), i.e., the maximum of i, j.
Lev when min (i, j) ≠ 0a,b(| a |, | b |) is the minimum of three cases:
1.leva,b(i-1, j) +1 represents deletion ai；
2.leva,b(i, j-1) +1 represents an insertion bj；
3.leva,b(i-1,j-1)+1(ai≠bj)Represents replacement bj，1(ai≠bj)Is an indicator function, and represents when ai＝bjWhen the time is 0; when a isi≠bjAt the time, its value is 1.
A2: and if the text similarity reaches a preset similarity threshold, determining the universal drug name corresponding to the text similarity as the standard universal drug name of the drug name.
In the embodiment of the application, the similarity value is determined by the difference value between the ratio of the edit distance between the medicine name and the medicine universal name for similarity comparison and the length of the longer character string in the medicine name and the medicine universal name and 1, so that the accuracy of similarity calculation can be improved. Illustratively, the edit distance between the metformin hydrochloride and the metformin hydrochloride is 3, and the edit distance is divided by the longer character string of the character strings to be compared, namely the metformin hydrochloride, so that the similarity score is calculated to be larger and the matching rate is higher.
Further, as an example of the present application, due to the specificity of drugs, there are drugs with similar names but different efficacies in the drugs. Drugs with different efficacies may have similar but in no way identical generic names for the drugs. As shown in fig. 3, the step S103 further includes:
b1: and if the text similarity between the medicine names and the medicine names reaches more than one medicine universal name with a preset similarity threshold, determining the medicine universal name corresponding to the text similarity between the medicine names and the medicine names as the undetermined universal name.
B2: and acquiring the applicable symptom information of the to-be-classified medicine corresponding to the medicine name from the original medication record table. Specifically, the original medication record table includes not only medication information, but also applicable symptom information of the medicine.
B3: and acquiring the applicable symptom information of the standard medicine corresponding to the undetermined universal name from the medicine information standard library.
B4: and determining the undetermined universal name corresponding to the standard medicine with the same applicable symptom information as the medicine to be classified as the standard medicine universal name of the medicine to be classified.
Note that the same applicable symptom information in the present embodiment does not mean the absolute same. Specifically, the applicable symptom information of the drug to be classified is compared with the applicable symptom information of the standard drug in similarity, and the applicable symptom information is determined to be the same when the similarity reaches a specified similarity value. Optionally, the applicable symptom information of the standard drug includes applicable symptom information of the drug to be classified, that is, the applicable symptom information of the standard drug is determined to be the same as the applicable symptom information of the drug to be classified.
In the embodiment of the application, in order to improve the matching accuracy, after the text similarity between the drug name and the drug common name in the drug information standard library is calculated, if the text similarity between the drug name and the drug name in the drug information standard library reaches more than one drug common name with the preset similarity threshold, the standard drug common name corresponding to the drug name is determined by further comparing the applicable symptom information, so that the determination of the standard drug common name is more accurate and effective.
Optionally, in this embodiment, for a drug name that cannot be successfully matched with a drug common name in the drug information standard library, the standard drug common name may be determined in a manner of manual cleaning, and a standard drug common name corresponding to a drug alias is obtained through manual retrieval. For example, in the original standard library, there is no saying of "fast urination", and after one manual search, the common name of the drug corresponding to "fast urination" is determined to be "furosemide".
Optionally, as an embodiment of the present application, as shown in fig. 4, the step S103 includes:
c1: and matching the medicine name with the medicine universal name in the medicine information standard library.
C2: and classifying the successfully matched medicine names into a first class of medicine name set, and determining the matched general medicine names as standard general medicine names corresponding to the medicine names in the first class of medicine name set. The first type of drug name set is used for storing drug names matched with the common names of the determined standard drugs.
C3: and classifying the medicine names which are not successfully matched into a second type medicine name set. The second type of drug name set is used for storing drug names which are not matched with the common names of the determined standard drugs.
C4: and clustering the medicine names in the first class of medicine name set according to a specified clustering algorithm to obtain a first cluster name subset, wherein the first cluster name subset comprises the first cluster names obtained by clustering.
C5: and clustering the medicine names in the second type of medicine name set according to a specified clustering algorithm to obtain a second clustering name subset, wherein the second clustering name subset comprises second clustering names obtained by clustering.
C6: and matching the first cluster name with the second cluster name, and determining a standard medicine universal name corresponding to the medicine name in the second medicine name set according to a matching result.
In this embodiment of the present application, the drug name matching the standard common name in the drug information standard library is determined as a first class drug name, the drug name failing to match the standard common name in the drug information standard library is determined as a second class drug name, the two classes of drug names are clustered respectively, then the second cluster name in the clustering result of the second class drug name is matched with the first cluster name in the clustering result of the first class drug name, and the standard drug common name corresponding to the drug name failing to match the standard common name in the drug information standard library is determined. The matching efficiency can be improved by clustering and then performing name matching.
For example, the first class originally has 1000 words, 100 clusters are obtained by clustering, and the first class has 100 unique drugs (chemical components in each cluster are the same, but specific common names may be different, for example, metformin and metformin hydrochloride sustained release tablets are in one cluster); similarly, the second class originally has 1300 words, and clustering results in 150 clusters, at which time the 150 words can be compared with 100 words in the first class. Because the clusters obtained are chemical names and the words are short, a part of words belonging to the second class are more likely to match the simplified words in the first class due to simplification (for example, the edited distance between the 'synthetic insulin for injection (Xenofei)' and the 'insulin' is large, the similarity is small, and the words are not matched, but the former is simplified into the 'insulin' after the medicine names are simplified, and the words are more likely to match the standard names). Before clustering, comparing every two of 1000 words in the first class with 1300 words in the second class, and calculating the similar distance for 1000 times 1300/2 times; after clustering, only the similarity between clusters needs to be calculated, namely similarity calculation for 100 × 150/2 times, so that the calculation amount is reduced, and the matching efficiency can be improved.
For some drugs whose names (e.g., trade names) are not represented by common names that result in a failed match, it is generally necessary to manually search for the common name of the drug. If through clustering, we only need to search for the representative word of each cluster manually, and do not need to search for all the words belonging to the cluster, saving the cost of manual cleaning (for example, after the alias of furosemide injection is "furosemide", and the words of "furosemide 10 mg" and "furosemide" in the original record are simplified and clustered into "furosemide", we only need to determine that "furosemide" is "furosemide" once, and do not need to search for "furosemide" every time it appears manually).
Further, in the embodiment of the application, the intelligent device supplements the standard universal names obtained after clustering and the corresponding medicine information thereof to the medicine standard library, so that the medicine standard library is more and more abundant along with the accumulation of cleaning experience, the manual cleaning work is less and less, and finally, full automation can be realized.
Optionally, as another embodiment of the present application, as shown in fig. 5, when the drug name cannot be matched with a drug universal name in a drug information standard library, the drug classification method further includes:
d1: and simplifying the medicine names according to a preset simplification rule to obtain first simplified medicine names to be matched. For example, the words for pharmaceutical dosage forms and pharmaceutical vehicles contained in the drug names are deleted so that the drug names contain as few words as possible representing chemical components.
D2: and acquiring the drug names of the drugs in the drug information standard library, and simplifying according to the preset simplification rule to obtain a second simplified drug name. As above, the words for pharmaceutical dosage form and pharmaceutical vehicle contained in the drug name are deleted so that the drug name contains as few words representing chemical components as possible.
D3: and clustering the second simplified medicine names to form clusters with a set number. Specifically, an initial set is established according to all second simplified medicine names obtained after simplification in the medicine information standard library, a set number of second simplified medicine names are randomly selected from the initial set to serve as initial clustering centers, and a set number of clusters are routed according to the similarity between the second simplified medicine names in the initial set and the initial clustering centers.
D4: and generating a matching list according to the central medicine name of each cluster. The matching list comprises the names of the central medicines of all clusters.
D5: matching the simplified medicine name to be matched with the central medicine name in the matching list, and determining the matched central medicine name as the common standard medicine name of the medicine name.
Specifically, firstly, words of drug dosage forms and drug media contained in drug names are deleted, so that the drug names only contain words representing chemical components as much as possible, secondly, simplified second simplified drug names in a drug information standard library are clustered, namely, similarity calculation between every two simplified drug names is carried out, the words with high similarity are classified into one class, one drug name in a certain class represents the class, the selection method is the drug name with the similarity higher than a certain threshold value most than that of other drug names in the class, therefore, the similar drug names are clustered, finally, a matching list is generated according to the center drug name of each cluster, a first simplified drug name to be matched is matched according to the matching list, the matching method is still as described above, information irrelevant to the drug chemical components in the drug names is reduced through simplification and clustering, and matching rate is improved, meanwhile, the workload of manual cleaning can be reduced, so that the matching effectiveness of the medicine names can be improved, and the efficiency of determining the standard general medicine names is improved.
S104: and acquiring the medicine classification requirement input by the user.
In the embodiment of the present application, the medicine classification requirement refers to a classification purpose of a user. The drug classification requirements include drug use. During medical big data analysis, the classification scale and standard of the medicines are different according to different research problems, for example, when the research recommends diabetes treatment medicines, the concerned scale may be oral medicines or insulin, and then the hypoglycemic medicines are respectively marked as oral medicines or insulin; in the study of the treatment of heart failure patients with various diseases, the possible consideration is whether to recommend hypoglycemic agents to the patients, and at this time, both oral hypoglycemic agents and insulin should be labeled as hypoglycemic agents. Therefore, in the embodiment of the application, the classification label is preset according to the classification requirement, and the classification label is further determined by acquiring the medicine classification requirement input by the user.
S105: and determining a classification label corresponding to the medicine name according to the standard medicine universal name obtained by matching and the medicine classification requirement, wherein the classification label is a label predefined according to the medicine classification requirement and the standard medicine universal name in advance.
The classification label is a label predefined in advance according to the medicine classification requirement and the universal name of standard medicines. It should be noted that, because the categories of the same drug under different drug classification requirements may be different, when there are a plurality of classification requirements input by the user, the same drug may correspond to a plurality of classification labels at the same time.
Further, after the step S105, the method further includes classifying the drugs corresponding to the drug names according to the classification labels, so that the drug classification information has more universality and wider application range, and the efficiency of utilizing and managing the drug information is greatly improved.
S106: and classifying the medicines corresponding to the medicine names according to the classification labels.
In the embodiment of the application, the medicines corresponding to the medicine names are classified according to the classification labels.
In the embodiment of the application, the original medication record table is obtained, the medication information is extracted from the original medication record table, the medication information is automatically cleaned according to the preset cleaning rule to obtain the medicine name, so that the medicine name used for matching the standard medicine universal name is concise, the matching accuracy is improved, then the medicine name is matched with the medicine universal name in the medicine information standard library to determine the standard medicine universal name corresponding to the medicine name, the medicine classification requirement input by a user is obtained, then the classification label corresponding to the medicine name is determined according to the standard medicine universal name and the medicine classification requirement obtained by matching, the classification label is the label predefined according to the medicine classification requirement and the standard medicine universal name in advance, and finally the medicine corresponding to the medicine name is classified according to the classification label, this application need not the medicine information of other hospitals of relevance, can improve the efficiency of acquireing and utilizing medicine information greatly to the medicine classification according to the demand simultaneously to the realization carries out effectual management to medicine information.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 6 shows a block diagram of a medicine sorting apparatus according to an embodiment of the present application, which corresponds to the medicine sorting method according to the above embodiment, and only the relevant parts of the embodiment of the present application are shown for convenience of description.
Referring to fig. 6, the medicine sorting apparatus includes: medicine information extraction unit 61, information washing unit 62, universal name matching unit 63, categorised demand acquisition unit 64, categorised label determination unit 65, medicine classification unit 66, wherein:
a medication information extraction unit 61, configured to obtain an original medication record table and extract medication information from the original medication record table;
the information cleaning unit 62 is configured to clean the medication information according to a preset cleaning rule to obtain a medicine name;
a universal name matching unit 63, configured to match the drug name with a drug universal name in a drug information standard library, and determine a standard drug universal name corresponding to the drug name;
a classification requirement obtaining unit 64, configured to obtain a medicine classification requirement input by a user;
a classification label determining unit 65, configured to determine, according to the matched standard drug universal name and the drug classification requirement, a classification label corresponding to the drug name, where the classification label is a label predefined according to the drug classification requirement and the standard drug universal name in advance;
and a medicine classification unit 66 for classifying the medicine corresponding to the medicine name according to the classification label.
Optionally, the common name matching unit 63 includes:
the text similarity calculation module is used for calculating the text similarity between the medicine name and the medicine universal name in the medicine information standard library;
and the first universal name determining module is used for determining the universal name of the medicine corresponding to the text similarity as the standard universal name of the medicine if the text similarity reaches a preset similarity threshold.
Optionally, the text similarity calculation module specifically includes:
the character string information acquisition submodule is used for acquiring the character string and the character string length of the medicine name and the character string length of the medicine universal name;
the editing distance determining submodule is used for calculating the editing distance between the medicine name and the medicine universal name according to the character string and the character string length of the medicine name and the character string length of the medicine universal name, and the editing distance refers to the minimum conversion times required for converting the medicine name to be the same as the medicine universal name;
and the text similarity determining submodule is used for determining the text similarity between the medicine name and the medicine universal name according to the editing distance.
Optionally, the text similarity determining sub-module specifically includes:
a calculating submodule for calculating a similarity value Sim (a, b) between the drug name and the drug common name according to the following formula:
wherein lev (a, b) represents an edit distance between the character string a of the drug name and the character string b of the drug common name, len (a) represents a character string length of the character string a, len (b) represents a character string length of the character string b, and max (len (a), len (b)) represents a longer character string length of the character string a and the character string b.
Optionally, the common name matching unit 63 further includes:
the undetermined universal name determining module is used for determining the universal name of the medicine corresponding to the text similarity reaching the preset similarity threshold value as the undetermined universal name if the text similarity reaching the preset similarity threshold value with the medicine name is more than one;
the first applicable information determining module is used for acquiring the applicable symptom information of the to-be-classified medicine corresponding to the medicine name from the original medication record table;
the second applicable information determining module is used for acquiring applicable symptom information of the standard medicine corresponding to the undetermined universal name from the medicine information standard library;
and the second universal name determining module is used for determining the universal name to be determined corresponding to the standard medicine with the same applicable symptom information as the medicine to be classified as the standard medicine universal name of the medicine to be classified.
Optionally, the common name matching unit 63 includes:
the information matching module is used for matching the medicine name with the medicine universal name in the medicine information standard library;
the first medicine name set determining module is used for classifying the successfully matched medicine names into a first class medicine name set and determining the matched medicine universal names as standard medicine universal names corresponding to the medicine names in the first class medicine name set;
the second medicine name set determining module is used for classifying the medicine names which are not successfully matched into a second type medicine name set;
the first clustering module is used for clustering the medicine names in the first medicine name set according to a specified clustering algorithm to obtain a first clustering name subset, and the first clustering name subset comprises first clustering names obtained through clustering;
the second clustering module is used for clustering the medicine names in the second medicine name set according to a specified clustering algorithm to obtain a second clustering name subset, and the second clustering name subset comprises second clustering names obtained through clustering;
and the first cluster matching module is used for matching the first cluster name with the second cluster name and determining a standard medicine universal name corresponding to the medicine name in the second medicine name set according to a matching result.
Optionally, when the drug name cannot be matched with a drug universal name in a drug information standard library, the drug sorting apparatus further includes:
and the first simplifying unit is used for simplifying the medicine names according to a preset simplifying rule to obtain the first simplified medicine names to be matched.
And the second simplifying unit is used for acquiring the medicine names of the medicines in the medicine information standard library, and simplifying according to the preset simplifying rule to obtain second simplified medicine names.
And the third clustering unit is used for clustering the second simplified medicine names to form clusters with set number.
And the matching list generating unit is used for generating a matching list according to the central medicine name of each cluster.
And the name matching unit is used for matching the simplified medicine name to be matched with the central medicine name in the matching list and determining the matched central medicine name as the standard medicine universal name of the medicine name.
In the embodiment of the application, the original medication record table is obtained, the medication information is extracted from the original medication record table, the medication information is automatically cleaned according to the preset cleaning rule to obtain the medicine name, so that the medicine name used for matching the standard medicine universal name is concise, the matching accuracy is improved, then the medicine name is matched with the medicine universal name in the medicine information standard library to determine the standard medicine universal name corresponding to the medicine name, the medicine classification requirement input by a user is obtained, then the classification label corresponding to the medicine name is determined according to the standard medicine universal name and the medicine classification requirement obtained by matching, the classification label is the label predefined according to the medicine classification requirement and the standard medicine universal name in advance, and finally the medicine corresponding to the medicine name is classified according to the classification label, this application need not the medicine information of other hospitals of relevance, can improve the efficiency of acquireing and utilizing medicine information greatly to the medicine classification according to the demand simultaneously to the realization carries out effectual management to medicine information.
Embodiments of the present application further provide a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions, when executed by a processor, implement the steps of any one of the drug sorting methods shown in fig. 1 to 5.
Embodiments of the present application further provide a computer program product, which, when running on a smart device, causes the smart device to execute steps implementing any one of the drug sorting methods shown in fig. 1 to 5.
An embodiment of the present application further provides an intelligent device, which includes a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, where the processor executes the computer readable instructions to implement the steps of any one of the drug sorting methods shown in fig. 1 to 5.
Fig. 7 is a schematic diagram of an intelligent device provided in an embodiment of the present application. As shown in fig. 7, the smart device 7 of this embodiment includes: a processor 70, a memory 71, and computer readable instructions 72 stored in the memory 71 and executable on the processor 70. The processor 70, when executing the computer readable instructions 72, performs the steps in the various drug sorting method embodiments described above, such as steps 101-106 shown in fig. 1. Alternatively, the processor 70, when executing the computer readable instructions 72, implements the functionality of the modules/units in the device embodiments described above, such as the functionality of the units 61 to 66 shown in fig. 6.
Illustratively, the computer readable instructions 72 may be partitioned into one or more modules/units that are stored in the memory 71 and executed by the processor 70 to accomplish the present application. The one or more modules/units may be a series of computer-readable instruction segments capable of performing specific functions, which are used to describe the execution process of the computer-readable instructions 72 in the smart device 7.
The intelligent device 7 may be a computing device such as a smart phone, a notebook, a server, a palm computer, and a cloud intelligent device. The intelligent device 7 may include, but is not limited to, a processor 70, a memory 71. It will be appreciated by those skilled in the art that fig. 7 is merely an example of the smart device 7, and does not constitute a limitation of the smart device 7, and may include more or less components than those shown, or combine certain components, or different components, for example, the smart device 7 may also include input-output devices, network access devices, buses, etc.
The Processor 70 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the smart device 7, such as a hard disk or a memory of the smart device 7. The memory 71 may also be an external storage device of the Smart device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the Smart device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the smart device 7. The memory 71 is used to store the computer readable instructions and other programs and data required by the smart device. The memory 71 may also be used to temporarily store data that has been output or is to be output.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to an apparatus/terminal device, recording medium, computer Memory, Read-Only Memory (ROM), Random-access Memory (RAM), electrical carrier wave signals, telecommunications signals, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A method of classifying a pharmaceutical product, comprising:
acquiring an original medication record table, and extracting medication information from the original medication record table;
cleaning the medication information according to a preset cleaning rule to obtain a medicine name;
matching the medicine name with a medicine universal name in a medicine information standard library, and determining a standard medicine universal name corresponding to the medicine name;
acquiring a medicine classification requirement input by a user;
determining a classification label corresponding to the medicine name according to the standard medicine universal name obtained by matching and the medicine classification requirement, wherein the classification label is a label predefined in advance according to the medicine classification requirement and the standard medicine universal name;
and classifying the medicines corresponding to the medicine names according to the classification labels.
2. The method for classifying a drug according to claim 1, wherein the step of matching the drug name with a drug universal name in a drug information standard library and determining a standard drug universal name corresponding to the drug name comprises:
calculating the text similarity between the medicine name and the medicine universal name in the medicine information standard library;
and if the text similarity reaches a preset similarity threshold, determining the universal drug name corresponding to the text similarity as the standard universal drug name of the drug name.
3. The method for classifying drugs according to claim 2, wherein the step of calculating the text similarity between the drug name and the common drug name in the drug information standard library specifically comprises:
acquiring the character string and the character string length of the medicine name and the character string length of the medicine universal name;
calculating the editing distance between the medicine name and the medicine universal name according to the character string and the character string length of the medicine name and the character string length of the medicine universal name, wherein the editing distance refers to the minimum conversion times required for converting the medicine name to be the same as the medicine universal name;
and determining the text similarity between the medicine name and the medicine universal name according to the editing distance.
4. The method for classifying drugs according to claim 3, wherein the step of determining the text similarity between the drug name and the drug common name according to the edit distance comprises:
calculating a similarity value Sim (a, b) of the drug name and the drug generic name according to the following formula:
wherein lev (a, b) represents an edit distance between the character string a of the drug name and the character string b of the drug common name, len (a) represents a character string length of the character string a, len (b) represents a character string length of the character string b, and max (len (a), len (b)) represents a longer character string length of the character string a and the character string b.
5. The method for classifying a drug according to claim 2, wherein the step of matching the drug name with a drug common name in a drug information standard library and determining a standard drug common name corresponding to the drug name further comprises:
if the text similarity between the medicine names and the medicine names reaches more than one medicine universal name with a preset similarity threshold, determining the medicine universal name corresponding to the text similarity between the medicine names and the medicine names as the undetermined universal name;
acquiring applicable symptom information of the to-be-classified medicine corresponding to the medicine name from the original medication record table;
acquiring applicable symptom information of the standard medicine corresponding to the undetermined universal name from the medicine information standard library;
and determining the undetermined universal name corresponding to the standard medicine with the same applicable symptom information as the medicine to be classified as the standard medicine universal name of the medicine to be classified.
6. The method for classifying a drug according to claim 1, wherein the step of matching the drug name with a drug universal name in a drug information standard library and determining a standard drug universal name corresponding to the drug name comprises:
matching the medicine name with a medicine universal name in a medicine information standard library;
the successfully matched medicine names are classified into a first class of medicine name set, and the matched medicine universal names are determined as standard medicine universal names corresponding to the medicine names in the first class of medicine name set;
the medicine names which are not successfully matched are classified into a second type medicine name set;
clustering the medicine names in the first kind of medicine name set according to a specified clustering algorithm to obtain a first clustering name subset, wherein the first clustering name subset comprises first clustering names obtained through clustering;
clustering the medicine names in the second type of medicine name set according to a specified clustering algorithm to obtain a second clustering name subset, wherein the second clustering name subset comprises second clustering names obtained through clustering;
and matching the first cluster name with the second cluster name, and determining a standard medicine universal name corresponding to the medicine name in the second medicine name set according to a matching result.
7. A drug sorting device, comprising:
the system comprises a medication information extraction unit, a medication information processing unit and a medication information processing unit, wherein the medication information extraction unit is used for acquiring an original medication record table and extracting medication information from the original medication record table;
the information cleaning unit is used for cleaning the medication information according to a preset cleaning rule to obtain a medicine name;
the universal name matching unit is used for matching the medicine name with the medicine universal name in a medicine information standard library and determining the standard medicine universal name corresponding to the medicine name;
the system comprises a classification requirement acquisition unit, a classification requirement acquisition unit and a classification requirement acquisition unit, wherein the classification requirement acquisition unit is used for acquiring a medicine classification requirement input by a user;
the classification label determining unit is used for determining a classification label corresponding to the medicine name according to the standard medicine universal name and the medicine classification requirement which are obtained through matching, wherein the classification label is a label predefined according to the medicine classification requirement and the standard medicine universal name in advance;
and the medicine classification unit classifies the medicines corresponding to the medicine names according to the classification labels.
8. The drug sorting device according to claim 7, wherein the common name matching unit includes:
the information matching module is used for matching the medicine name with the medicine universal name in the medicine information standard library;
the first medicine name set determining module is used for classifying the successfully matched medicine names into a first class medicine name set and determining the matched medicine universal names as standard medicine universal names corresponding to the medicine names in the first class medicine name set;
the second medicine name set determining module is used for classifying the medicine names which are not successfully matched into a second type medicine name set;
the first clustering module is used for clustering the medicine names in the first medicine name set according to a specified clustering algorithm to obtain a first clustering name subset, and the first clustering name subset comprises first clustering names obtained through clustering;
the second clustering module is used for clustering the medicine names in the second medicine name set according to a specified clustering algorithm to obtain a second clustering name subset, and the second clustering name subset comprises second clustering names obtained through clustering;
and the first cluster matching module is used for matching the first cluster name with the second cluster name and determining a standard medicine universal name corresponding to the medicine name in the second medicine name set according to a matching result.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of drug sorting according to any one of claims 1 to 6.
10. An intelligent device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the drug sorting method of any one of claims 1 to 6.
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| PCT/CN2020/119301 WO2021184729A1 (en) | 2020-03-17 | 2020-09-30 | Drug classification method and apparatus, storage medium, and intelligent device |
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| CN112182142A (en) * | 2020-09-29 | 2021-01-05 | 朱玮 | Mapping method and system for medicine name standardization |
| CN112668280A (en) * | 2020-12-29 | 2021-04-16 | 杭州依图医疗技术有限公司 | Medical data processing method and device and storage medium |
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| CN111475686A (en) * | 2020-03-17 | 2020-07-31 | 平安科技（深圳）有限公司 | Medicine classification method and device, storage medium and intelligent equipment |
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