CN113223657A - Medicine information processing method and device, electronic equipment and storage medium - Google Patents
Medicine information processing method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113223657A CN113223657A CN202110610737.5A CN202110610737A CN113223657A CN 113223657 A CN113223657 A CN 113223657A CN 202110610737 A CN202110610737 A CN 202110610737A CN 113223657 A CN113223657 A CN 113223657A
- Authority
- CN
- China
- Prior art keywords
- medicine
- matched
- determining
- alternative
- drug
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/216—Parsing using statistical methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/237—Lexical tools
- G06F40/242—Dictionaries
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Abstract
The embodiment of the invention discloses a method and a device for processing medicine information, electronic equipment and a storage medium. The method comprises the following steps: acquiring at least two attribute characteristics of a medicine to be matched; wherein the attribute characteristics comprise at least one of the name, dosage form, specification and manufacturer of the medicine to be matched; determining a target medicine matched with the medicine to be matched in a pre-established medicine knowledge base based on the attribute characteristics; and determining the medicine information corresponding to the target medicine as the medicine information of the medicine to be matched. The technical scheme of the embodiment of the invention can effectively avoid the situation of wrong determination of the medicine information caused by different expression modes of the common names of the medicines or different writing habits, improve the accuracy of determining the medicine information and contribute to better acquiring the medical data.
Description
Technical Field
The present invention relates to data processing technologies, and in particular, to a method and an apparatus for processing medicine information, an electronic device, and a storage medium.
Background
The explosive growth of medical data and the rise of data mining cause more and more people to pay attention, in the process of regional hospital data management, data of multiple hospitals need to be collected to a uniform data platform, and the method for acquiring medication information from electronic medical records of the hospitals becomes a common method for acquiring medical data.
In the prior art, when acquiring actually used medicine information of a hospital based on an electronic medical record, the actually used medicine information is generally determined according to a medicine universal name, and then the medicine information of hospitals in various areas is collected to a unified data platform.
However, since there are many expression forms of medicine information and hospitals in different areas have different writing habits on the common names of medicines, when the medicine information is collected uniformly, the common names of different medicines obtained by hospitals in different areas may actually want to express the same medicine. Therefore, in the prior art, only the common name of the medicine cannot be used for accurately determining the medicine information, so that the acquired medical data has errors.
Disclosure of Invention
Embodiments of the present invention provide a method and an apparatus for processing drug information, an electronic device, and a storage medium, so as to determine drug information of a drug to be matched by using at least two attribute features, effectively avoid a situation of a drug information determination error caused by different expression modes of drug common names or different writing habits, improve accuracy of determining drug information, and facilitate better acquisition of medical data.
In a first aspect, an embodiment of the present invention provides a method for processing medicine information, which may include:
acquiring at least two attribute characteristics of a medicine to be matched; wherein the attribute characteristics comprise at least one of the drug name, the dosage form, the specification and the manufacturer of the drug to be matched;
determining a target medicine matched with the medicine to be matched in a pre-established medicine knowledge base based on the attribute characteristics;
and determining the medicine information corresponding to the target medicine as the medicine information of the medicine to be matched.
In a second aspect, an embodiment of the present invention further provides a device for processing medicine information, which may include:
the attribute feature acquisition module is used for acquiring at least two attribute features of the medicine to be matched; wherein the attribute characteristics comprise at least one of the drug name, the dosage form, the specification and the manufacturer of the drug to be matched;
a target medicine determining module, configured to determine, based on the attribute features, a target medicine that matches the medicine to be matched in a pre-established medicine knowledge base;
and the medicine information determining module is used for determining the medicine information corresponding to the target medicine as the medicine information of the medicine to be matched.
In a third aspect, an embodiment of the present invention further provides an electronic device, which may include:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for processing the drug information provided by any embodiment of the present invention.
In a fourth aspect, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for processing the medicine information provided in any embodiment of the present invention.
The method for processing the medicine information provided by the embodiment of the invention obtains at least two attribute characteristics of the medicine to be matched; wherein the attribute characteristics comprise at least one of the name, dosage form, specification and manufacturer of the medicine to be matched; the target medicine matched with the medicine to be matched is determined in a pre-established medicine knowledge base based on at least two attribute characteristics, and the medicine information corresponding to the target medicine is determined as the medicine information of the medicine to be matched. Therefore, the embodiment of the invention adopts at least two attribute characteristics to determine the medicine information of the medicine to be matched, effectively avoids the condition of wrong determination of the medicine information caused by different expression modes of the common names of the medicines or different writing habits, improves the accuracy of determining the medicine information, and is beneficial to better acquiring the medical data.
In addition, the medicine information processing device, the electronic equipment and the storage medium provided by the invention correspond to the method, and have the same beneficial effects.
Drawings
In order to illustrate the embodiments of the present invention more clearly, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a method for processing drug information according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method for processing drug information according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a process for processing drug information according to an embodiment of the present invention;
fig. 4 is a structural diagram of a medicine information processing device according to an embodiment of the present invention;
fig. 5 is a structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
The core of the invention is to provide a method and a device for processing medicine information, electronic equipment and a storage medium. The medicine information of the medicine to be matched is determined by adopting at least two attribute characteristics, so that the condition of wrong determination of the medicine information caused by different expression modes of the common names of the medicines or different writing habits is effectively avoided, the accuracy of determining the medicine information is improved, and better acquisition of medical data is facilitated.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Example one
Fig. 1 is a flowchart of a method for processing medicine information according to an embodiment of the present invention. The method can be executed by a processing device of the medicine information provided by the embodiment of the invention, the device can be realized by software and/or hardware, and the device can be integrated on various user terminals or servers.
As shown in fig. 1, the method of the embodiment of the present invention specifically includes the following steps:
s101, acquiring at least two attribute characteristics of the medicine to be matched.
In a specific implementation, when a request for obtaining drug information of a drug to be matched is received, at least two attribute features of the drug to be matched may be obtained based on attribute information of the drug to be matched provided in the request. Wherein an attribute feature may be understood as a feature for distinguishing between different drugs. Optionally, the attribute characteristics include at least one of a drug name, a dosage form, a specification of the drug to be matched, and a manufacturer's home. The name of the medicine may include a general name of the medicine, a name of the Chinese product, a name of the English product, a name of the medicine trade, and the like. It should be noted that the greater the number of acquired attribute features, the more the accuracy of determining the medicine information of the medicine to be matched is improved. The skilled person can determine the number of the acquired attribute features according to the actual application.
S102, determining a target medicine matched with the medicine to be matched in a pre-established medicine knowledge base based on the attribute characteristics.
In specific implementation, a drug knowledge base may be pre-established, where the drug knowledge base stores the alternative drugs and the attribute characteristics of each alternative drug. The more alternative drugs are stored in the drug knowledge base, the more the accuracy of determining the drug information of the drug to be matched is improved. Optionally, the attribute characteristics of the alternative drugs stored in the drug knowledge base include drug universal names, Chinese product names, English product names, drug trade names, dosage forms, specifications, manufacturers, and the like of the alternative drugs.
Optionally, based on the obtained at least two attribute features of the drug to be matched, matching is performed with each alternative drug in a pre-established drug knowledge base, and the alternative drug matched with the drug to be matched is determined as the target drug. Specifically, for each alternative medicine, the matching degree between the medicine to be matched and the alternative medicines stored in the medicine knowledge base is determined based on each attribute characteristic; and determining a target medicine corresponding to the medicine to be matched according to the determined matching degree between the medicine to be matched and each alternative medicine.
Illustratively, the degree of matching may be expressed in a hierarchical manner, or may be expressed in a numerical manner. The embodiment of the present invention is not limited thereto. For example, the matching degree is divided into three degrees of a high matching level, a medium matching level, and a low matching level. When determining the alternative medicines corresponding to the medicines to be matched according to the matching degree, firstly determining the alternative medicines corresponding to the high matching level, and if only one alternative medicine with the high matching level exists, determining the alternative medicine as the target medicine; and if a plurality of alternative medicines with high matching grades exist, determining an alternative medicine with the medicine name consistent with the medicine to be matched as the target medicine in each alternative medicine with the high matching grade.
S103, determining the medicine information corresponding to the target medicine as the medicine information of the medicine to be matched.
Optionally, if the target medicine is matched with the medicine to be matched, it can be determined that the target medicine is the medicine to be matched. Correspondingly, the medicine information corresponding to the target medicine can be the medicine information of the medicine to be matched. Specifically, the medicine information includes a medicine universal name, a Chinese product name, an English product name, a medicine trade name, a formulation, a specification, a manufacturer, and the like of the medicine.
The method for processing the medicine information provided by the embodiment of the invention obtains at least two attribute characteristics of the medicine to be matched; wherein the attribute characteristics comprise at least one of the name, dosage form, specification and manufacturer of the medicine to be matched; the target medicine matched with the medicine to be matched is determined in a pre-established medicine knowledge base based on at least two attribute characteristics, and the medicine information corresponding to the target medicine is determined as the medicine information of the medicine to be matched. Therefore, the embodiment of the invention adopts at least two attribute characteristics to determine the medicine information of the medicine to be matched, effectively avoids the condition of wrong determination of the medicine information caused by different expression modes of the common names of the medicines or different writing habits, improves the accuracy of determining the medicine information, and is beneficial to better acquiring the medical data.
Example two
Fig. 2 is a flowchart of another method for processing drug information according to an embodiment of the present invention. The present embodiment is optimized based on the above technical solutions. Optionally, based on the attribute characteristics, determining a target drug matched with the drug to be matched in a pre-established drug knowledge base, including: for each alternative medicine, determining a matching score between the medicine to be matched and the alternative medicine stored in the medicine knowledge base based on each attribute characteristic; determining a total matching value between the medicine to be matched and the alternative medicine based on the matching score corresponding to each attribute characteristic; and determining the alternative medicine corresponding to the maximum matching total value in the matching total values as the target medicine. The same or corresponding terms as those in the above embodiments are not explained in detail herein.
As shown in fig. 2, the method of the embodiment of the present invention specifically includes the following steps:
s201, acquiring at least two attribute characteristics of the medicine to be matched.
S202, aiming at each alternative medicine, determining a matching score between the medicine to be matched and the alternative medicine stored in the medicine knowledge base respectively based on each attribute characteristic.
In a specific implementation, the matching degree between the alternative medicine and the medicine to be matched can be represented by means of a matching numerical value. For each alternative medicine stored in the medicine knowledge base, calculating to obtain a matching score for each attribute characteristic; and calculating to obtain a total matching value between the alternative medicine and the medicine to be matched based on each matching score calculated by each attribute characteristic. For example, if the number of candidate drugs stored in the drug knowledge base is N, N matching scores may be calculated for each attribute feature, where N is a positive integer.
It should be noted that there is a one-to-one correspondence relationship between the attribute characteristics and the medicines to be matched, the alternative medicines, and the matching scores; for example, the obtained attribute characteristics are the name of the manufacturer and the name of the medicine, and for the attribute characteristics of the manufacturer, the matching score of the manufacturer between the medicine to be matched and each alternative medicine is calculated; and calculating the medicine name matching score between the medicine to be matched and each alternative medicine according to the attribute characteristic of the medicine name. And determining the total matching value between the medicine to be matched and each alternative medicine based on the matching score of the manufacturer and the matching score of the medicine name.
Optionally, determining a matching score between the drug to be matched and the alternative drug stored in the drug knowledge base based on each attribute feature respectively includes: respectively carrying out vectorization processing on each attribute feature of the medicine to be matched based on at least one vectorization algorithm to generate a feature vector to be matched corresponding to each attribute feature of the medicine to be matched; vectorizing each attribute feature of each alternative medicine respectively based on at least one vectorization algorithm to generate alternative feature vectors corresponding to each attribute feature of each alternative medicine; respectively calculating the alternative vector distance between the feature vector to be matched and each alternative feature vector aiming at each vectorization algorithm; and respectively determining the matching scores between the medicines to be matched and the alternative medicines based on the alternative vector distances corresponding to the anisotropic quantitative algorithm.
Optionally, the vectorization algorithm includes at least one of a word frequency-inverse file frequency algorithm, a text similarity algorithm, and a pre-training model algorithm. And respectively carrying out vectorization processing on each attribute feature of the medicine to be matched and each attribute feature of the alternative medicine based on a vectorization algorithm so as to generate a feature vector to be matched and an alternative feature vector. And respectively calculating the alternative vector distance between the feature vector to be matched corresponding to the attribute feature and each alternative feature vector aiming at each vectorization algorithm.
Further, when one vectorization algorithm is adopted, the calculated alternative vector distance can be directly determined as the matching score between the medicine to be matched and each alternative medicine; the distance between each candidate vector of the preset multiple may also be determined as a matching score between the drug to be matched and each candidate drug, which is not specifically limited in the embodiment of the present invention.
Further, when the adopted vectorization algorithm is two or more than two, respectively determining the matching scores between the medicines to be matched and the alternative medicines based on the alternative vector distances corresponding to the anisotropic quantization algorithm, including: determining vector matching scores between the medicines to be matched and the alternative medicines based on the determined alternative vector distances aiming at the anisotropic quantitative algorithm; and performing weighted summation calculation on the respective vector matching scores corresponding to the respective vector algorithms, and respectively determining the matching scores between the medicines to be matched and the alternative medicines based on the calculation results.
Optionally, any one of an euclidean distance algorithm, a manhattan distance algorithm, a chebyshev distance algorithm, a mahalanobis distance algorithm, and a cosine distance algorithm may be used to calculate the candidate vector distance between the feature vector to be matched and each candidate feature vector.
Optionally, before the step of respectively calculating the candidate vector distance between the feature vector to be matched and each candidate feature vector, the method further includes: and respectively carrying out dimension reduction processing on each candidate feature vector based on a dimension reduction algorithm, and updating the candidate feature vectors into the candidate feature vectors after dimension reduction. Specifically, the dimensionality reduction algorithm includes a factor analysis algorithm, a principal component analysis algorithm, an independent component analysis algorithm, a UMAP algorithm, a TNSE algorithm and the like. And the candidate feature vectors after dimension reduction are used for determining the matching score, so that the calculation amount is favorably reduced.
S203, determining a total matching value between the medicine to be matched and the alternative medicine based on the matching score corresponding to each attribute feature.
In specific implementation, determining a total matching value between the drug to be matched and the alternative drug based on the matching score corresponding to each attribute feature includes: and respectively determining the total matching value between the medicine to be matched and the alternative medicine based on the matching score corresponding to each attribute characteristic and a predetermined target fusion model.
Optionally, before determining the total matching value, a mesh parameter adjusting operation may be performed on the pre-established fusion model based on a predetermined sample drug set and each candidate drug, so as to train an optimal mesh parameter, and determine the target fusion model based on a parameter adjusting result. And inputting the matching scores corresponding to the attribute characteristics into the target fusion model so as to determine the total matching value between the medicine to be matched and each alternative medicine.
S204, determining the alternative medicine corresponding to the maximum matching total value in the matching total values as the target medicine.
In specific implementation, the total matching value is used for expressing the matching degree between the medicine to be matched and each alternative medicine, and the larger the total matching value is, the higher the matching degree between the medicine to be matched and the alternative medicine is; the candidate drug corresponding to the largest matching total value among the matching total values may be determined as the target drug.
S205, determining the medicine information corresponding to the target medicine as the medicine information of the medicine to be matched.
The method for processing the medicine information provided by the embodiment of the invention adopts a plurality of vectorization algorithms to carry out vectorization processing on the attribute characteristics, and carries out dimension reduction processing on the obtained characteristic vector to be matched and the alternative characteristic vector; and determining a matching total value between the medicine to be matched and the alternative medicine through a predetermined target fusion model, and determining the alternative medicine corresponding to the maximum matching total value in the matching total values as the target medicine. Therefore, the embodiment of the invention adopts at least two attribute characteristics and at least two vectorization algorithms, thereby effectively avoiding the situation of wrong determination of the medicine information caused by different expression modes of the medicine universal name or different writing habits, and improving the accuracy of determining the medicine information; through dimension reduction processing, the calculation amount is greatly reduced, and the efficiency of determining the target medicine is improved.
EXAMPLE III
In the above, detailed descriptions are given to the embodiment corresponding to the processing method of the drug information, and specific application scenarios are given below in order to make the technical solutions of the method further clear to those skilled in the art.
FIG. 3 is a schematic diagram of a process for processing drug information according to an embodiment of the present invention; as shown in fig. 3, in the embodiment of the present invention, a medicine dictionary information list is obtained, where the medicine dictionary information list includes medicine names and medicine identification numbers; and determining the attribute characteristics F1 and F2 … … Fn of each alternative medicine in a pre-established medicine knowledge base based on the medicine name or the medicine identification number in the medicine dictionary information list. The attribute features may include a number, including but not limited to: general name of medicine, product name, English product name, trade name of medicine, formulation, specification, manufacturer, etc.
And vectorizing the attribute features by using three different vectorizing algorithms. Specifically, a TF-IDF (term frequency-inverse file frequency) algorithm is adopted to carry out vectorization processing on each attribute feature to obtain an alternative feature vector drug _ Fi _ vec1_ 0; vectorizing each attribute feature by adopting a text similarity algorithm BM25 to obtain an alternative feature vector drug _ Fi _ vec2_ 0; and (3) carrying out vectorization processing on each attribute feature by adopting a PTMS (pre-trained-models) algorithm to obtain an alternative feature vector drug _ Fi _ vec3_ 0. When the PTMS algorithm is adopted for vectorization processing, fine-tuning operation is carried out on the electronic medical record data of the general pre-training model passing through the target hospital to tune the parameters, and posing operation is carried out on the pre-training model after parameter tuning and each attribute characteristic to obtain an alternative characteristic vector drug _ Fi _ vec3_ 0.
And respectively carrying out dimension reduction processing on the candidate feature vector drug _ Fi _ vec1_0, the candidate feature vector drug _ Fi _ vec2_0 and the candidate feature vector drug _ Fi _ vec3_0 to respectively form a candidate feature vector drug _ Fi _ vec1, a drug _ Fi _ vec2 and a drug _ Fi _ vec3 after dimension reduction. The dimensionality reduction algorithm comprises a factor analysis algorithm, a principal component analysis algorithm, an independent component analysis algorithm, a UMAP algorithm, a TNSE algorithm and the like.
For the input medicine to be matched, determining attribute features Ft1 and Ft2 … … Ftn consistent with the alternative medicine, and performing vectorization processing on each attribute feature by adopting a TF-IDF algorithm, a BM25 algorithm and a PTMS algorithm respectively to obtain a feature vector to be matched, i.e., a feature vector to be matched, i.e., a feature vector Fti, a feature, i.e., a feature vector Fti, i.e., a feature vector, a feature, i.e., a feature vector, i.e., a feature vector, i.e., a feature vector 3, a feature, and a feature vector 3, and a feature, and a feature vector, and a feature vector, i.e., a feature, and a feature vector, and a feature vector, i.e., a feature vector, and a feature, i.e., a feature, and a feature to be matched, and a feature vector, and a feature, and a feature, i.e., a feature, and a feature vector, and a feature vector, i.e., a feature, i.e.. And the feature vector to be matched, the drug _ Fti _ vec1, the feature vector to be matched, the drug _ Fti _ vec2 and the feature vector to be matched, the drug _ Fti _ vec3, are subjected to dimension reduction operation by the same dimension reduction algorithm as the alternative medicine, and the drug _ Fti _ vec1, the drug _ Fti _ vec2 and the drug _ Fti _ vec3 are updated based on the feature vector to be matched after dimension reduction.
Respectively calculating the vector distance between the drug _ Fi _ vec1_0 and the drug _ Fti _ vec1 of each attribute feature; the vector distance between the drug _ Fi _ vec2_0 and the drug _ Fti _ vec2, and the vector distance between the drug _ Fi _ vec3_0 and the drug _ Fti _ vec 3. Any one of an Euclidean distance algorithm, a Manhattan distance algorithm, a Chebyshev distance algorithm, a Mahalanobis distance algorithm and a cosine distance algorithm can be adopted to calculate the vector distance. And the vector distance is used as the vector matching Score of the medicine to be matched and the alternative medicine corresponding to the vectorization algorithm, namely Score (drug _ Fti _ vec1, drug _ Fi _ vec1), Score (drug _ Fti _ vec2, drug _ Fi _ vec2) and Score (drug _ Fti _ vec3, drug _ Fi _ vec 3).
And calculating the matching score between the medicine to be matched and the alternative medicine corresponding to the attribute characteristic based on the each component matching score through a matching score calculation formula. And for different attribute characteristics, calculating to obtain matching scores between the corresponding medicine to be matched and the alternative medicines, and inputting the matching scores corresponding to the attribute characteristics into the target fusion model for fusion operation to obtain a total matching value between the medicine to be matched and the alternative medicines.
The matching score calculation formula is as follows:
Score(i)＝αScore(drug_Fti_vec1，drug_Fi_vec1)+βScore(drug_Fti_vec2，drug_Fi_vec2)+γScore(drug_Fti_vec3，drug_Fi_vec3)
the target fusion model is as follows:
wherein alpha, beta and gamma are matching score coefficients, the value ranges are (alpha is more than or equal to 0, beta and gamma is less than 1), i represents the labels of the attribute features, n attribute features are selected in total, and Wi is the matching coefficient corresponding to the attribute feature with the label being i. The grid parameter adjustment can be carried out on the matching score coefficients alpha, beta and gamma and the matching coefficient Wi by labeling a small number of correct sample pair sets S, so as to obtain an optimal set of parameters Wi and alpha, beta and gamma.
The embodiment of the invention adopts at least two attribute characteristics and at least two vectorization algorithms, thereby effectively avoiding the situation of wrong determination of the medicine information caused by different expression modes of the medicine universal name or different writing habits, and improving the accuracy of determining the medicine information; through dimension reduction processing, the calculation amount is greatly reduced, and the efficiency of determining the target medicine is improved.
Example four
Fig. 4 is a structural diagram of a medicine information processing device according to an embodiment of the present invention; the device is used for executing the processing method of the medicine information provided by any of the above embodiments. The apparatus and the method for processing the medicine information in the embodiments belong to the same inventive concept, and details that are not described in detail in the embodiments of the apparatus for processing the medicine information may refer to the embodiments of the method for processing the medicine information. As shown in fig. 4, the apparatus may specifically include:
an attribute feature obtaining module 10, configured to obtain at least two attribute features of a drug to be matched; wherein the attribute characteristics comprise at least one of the name, dosage form, specification and manufacturer of the medicine to be matched;
a target medicine determining module 11, configured to determine, based on the attribute features, a target medicine that matches the medicine to be matched in a pre-established medicine knowledge base;
and a medicine information determining module 12, configured to determine medicine information corresponding to the target medicine as medicine information of the medicine to be matched.
On the basis of any alternative embodiment of the present invention, the apparatus for determining a target drug module 11 comprises:
the first determining unit is used for determining a matching score between the medicine to be matched and the alternative medicines stored in the medicine knowledge base respectively based on each attribute characteristic aiming at each alternative medicine; determining a total matching value between the medicine to be matched and the alternative medicine based on the matching score corresponding to each attribute characteristic; and determining the alternative medicine corresponding to the maximum matching total value in the matching total values as the target medicine.
On the basis of any one of the optional implementation manners of the embodiment of the present invention, the first determining unit includes:
and the second determining unit is used for respectively determining the total matching value between the medicine to be matched and the alternative medicine based on the matching score corresponding to each attribute characteristic and a predetermined target fusion model.
On the basis of any one of the optional implementation manners of the embodiment of the present invention, the first determining unit includes:
the third determining unit is used for respectively carrying out vectorization processing on each attribute feature of the medicine to be matched based on at least one vectorization algorithm to generate a feature vector to be matched corresponding to each attribute feature of the medicine to be matched; vectorizing each attribute feature of each alternative medicine respectively based on at least one vectorization algorithm to generate alternative feature vectors corresponding to each attribute feature of each alternative medicine; respectively calculating the alternative vector distance between the feature vector to be matched and each alternative feature vector aiming at each vectorization algorithm; and respectively determining the matching scores between the medicines to be matched and the alternative medicines based on the alternative vector distances corresponding to the anisotropic quantitative algorithm.
On the basis of any one of the optional embodiments of the present invention, the third determining unit includes:
the calculation unit is used for determining vector matching scores between the medicines to be matched and the alternative medicines based on the determined alternative vector distances aiming at the each-directional quantization algorithm when the adopted vectorization algorithm is two or more; carrying out weighted summation calculation on the each vector matching score corresponding to the each vector algorithm, and respectively determining the matching score between the medicine to be matched and each alternative medicine based on the calculation result; the vectorization algorithm comprises at least one of a word frequency-reverse file frequency algorithm, a text similarity algorithm and a pre-training model algorithm.
On the basis of any optional implementation of the embodiment of the present invention, the third determining unit further includes:
and the dimension reduction unit is used for respectively carrying out dimension reduction processing on each candidate feature vector based on a dimension reduction algorithm before respectively calculating the candidate vector distance between the feature vector to be matched and each candidate feature vector, and updating the candidate feature vectors into the candidate feature vectors after dimension reduction.
On the basis of any optional implementation of the embodiment of the present invention, the second determining unit further includes:
a parameter adjusting unit, configured to perform a grid parameter adjusting operation on a pre-established fusion model based on a pre-determined sample drug set and each candidate drug before determining each matching total value;
and determining a target fusion model based on the parameter adjusting result.
The device for processing the medicine information provided by the embodiment of the invention can execute the method for processing the medicine information provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, in the embodiment of the device for processing medicine information, the units and modules included in the device are merely divided according to the functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
EXAMPLE five
Fig. 5 is a structural diagram of an electronic device according to an embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary electronic device 20 suitable for use in implementing embodiments of the present invention. The illustrated electronic device 20 is merely an example and should not be used to limit the functionality or scope of embodiments of the present invention.
As shown in fig. 5, the electronic device 20 is embodied in the form of a general purpose computing device. The components of the electronic device 20 may include, but are not limited to: one or more processors or processing units 201, a system memory 202, and a bus 203 that couples the various system components (including the system memory 202 and the processing unit 201).
The system memory 202 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)204 and/or cache memory 205. The electronic device 20 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system 206 may be used to read from and write to non-removable, nonvolatile magnetic media. A magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a floppy disk "), and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 203 by one or more data media interfaces. Memory 202 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 208 having a set (at least one) of program modules 207 may be stored, for example, in memory 202, such program modules 207 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 207 generally perform the functions and/or methodologies of embodiments of the present invention as described herein.
The electronic device 20 may also communicate with one or more external devices 209 (e.g., keyboard, pointing device, display 210, etc.), with one or more devices that enable a user to interact with the electronic device 20, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 20 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 211. Also, the electronic device 20 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 212. As shown, the network adapter 212 communicates with other modules of the electronic device 20 over the bus 203. It should be understood that other hardware and/or software modules may be used in conjunction with electronic device 20, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 201 executes various functional applications and data processing by running a program stored in the system memory 202.
The electronic equipment provided by the invention can realize the following method: acquiring at least two attribute characteristics of a medicine to be matched; wherein the attribute characteristics comprise at least one of the name, dosage form, specification and manufacturer of the medicine to be matched; the target medicine matched with the medicine to be matched is determined in a pre-established medicine knowledge base based on at least two attribute characteristics, and the medicine information corresponding to the target medicine is determined as the medicine information of the medicine to be matched. Therefore, the embodiment of the invention adopts at least two attribute characteristics to determine the medicine information of the medicine to be matched, effectively avoids the condition of wrong determination of the medicine information caused by different expression modes of the common names of the medicines or different writing habits, improves the accuracy of determining the medicine information, and is beneficial to better acquiring the medical data.
EXAMPLE six
An embodiment of the present invention provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method of processing drug information, the method comprising:
acquiring at least two attribute characteristics of a medicine to be matched; wherein the attribute characteristics comprise at least one of the name, dosage form, specification and manufacturer of the medicine to be matched; the target medicine matched with the medicine to be matched is determined in a pre-established medicine knowledge base based on at least two attribute characteristics, and the medicine information corresponding to the target medicine is determined as the medicine information of the medicine to be matched. Therefore, the embodiment of the invention adopts at least two attribute characteristics to determine the medicine information of the medicine to be matched, effectively avoids the condition of wrong determination of the medicine information caused by different expression modes of the common names of the medicines or different writing habits, improves the accuracy of determining the medicine information, and is beneficial to better acquiring the medical data.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also perform related operations in the method for processing medicine information provided by any embodiment of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the-C "programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for processing medicine information is characterized by comprising the following steps:
acquiring at least two attribute characteristics of a medicine to be matched; wherein the attribute characteristics comprise at least one of the drug name, the dosage form, the specification and the manufacturer of the drug to be matched;
determining a target medicine matched with the medicine to be matched in a pre-established medicine knowledge base based on the attribute characteristics;
and determining the medicine information corresponding to the target medicine as the medicine information of the medicine to be matched.
2. The method of claim 1, wherein the determining a target drug matching the drug to be matched in a pre-established drug knowledge base based on the attribute features comprises:
for each alternative medicine, determining a matching score between the medicine to be matched and the alternative medicine stored in the medicine knowledge base respectively based on each attribute feature;
determining a total matching value between the medicine to be matched and the alternative medicine based on the matching score corresponding to each attribute feature;
and determining the alternative medicine corresponding to the maximum matching total value in the matching total values as the target medicine.
3. The method of claim 2, wherein the determining a total value of the match between the drug to be matched and the drug candidate based on the match score corresponding to each attribute feature comprises:
and respectively determining the total matching value between the medicine to be matched and the alternative medicine based on the matching score corresponding to each attribute characteristic and a predetermined target fusion model.
4. The method of claim 2, wherein the determining a match score between the drug to be matched and the alternative drugs stored in the drug knowledge base based on each attribute feature comprises:
vectorizing each attribute feature of the medicine to be matched based on at least one vectorization algorithm to generate a feature vector to be matched corresponding to each attribute feature of the medicine to be matched;
vectorizing each attribute feature of each alternative medicine respectively based on the at least one vectorization algorithm to generate alternative feature vectors corresponding to each attribute feature of each alternative medicine;
respectively calculating the alternative vector distance between the feature vector to be matched and each alternative feature vector aiming at each vectorization algorithm;
and respectively determining the matching scores between the medicines to be matched and the alternative medicines based on the alternative vector distances corresponding to the vectorization algorithms.
5. The method of claim 4, wherein when the vectorization algorithms used are two or more, the determining the matching score between the drug to be matched and each of the candidate drugs based on the candidate vector distance corresponding to each of the vectorization algorithms respectively comprises:
for each vectorization algorithm, determining a vector matching score between the medicine to be matched and each alternative medicine based on the determined distance of each alternative vector;
carrying out weighted summation calculation on the vector matching scores corresponding to the vector algorithms, and respectively determining the matching scores between the medicine to be matched and the alternative medicines based on the calculation results;
the vectorization algorithm comprises at least one of a word frequency-reverse file frequency algorithm, a text similarity algorithm and a pre-training model algorithm.
6. The method according to claim 4, further comprising, before said separately calculating candidate vector distances between the feature vector to be matched and each of the candidate feature vectors:
and respectively carrying out dimension reduction processing on each candidate feature vector based on a dimension reduction algorithm, and updating the candidate feature vectors into the candidate feature vectors after dimension reduction.
7. The method of claim 3, further comprising, prior to said separately determining each of said matching total values:
carrying out grid parameter adjustment operation on a pre-established fusion model based on a predetermined sample medicine set and each alternative medicine;
and determining the target fusion model based on the parameter adjusting result.
8. A device for processing medicine information, comprising:
the attribute feature acquisition module is used for acquiring at least two attribute features of the medicine to be matched; wherein the attribute characteristics comprise at least one of the drug name, the dosage form, the specification and the manufacturer of the drug to be matched;
a target medicine determining module, configured to determine, based on the attribute features, a target medicine that matches the medicine to be matched in a pre-established medicine knowledge base;
and the medicine information determining module is used for determining the medicine information corresponding to the target medicine as the medicine information of the medicine to be matched.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of processing drug information as recited in any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing a method for processing drug information according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110610737.5A CN113223657A (en) | 2021-06-01 | 2021-06-01 | Medicine information processing method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110610737.5A CN113223657A (en) | 2021-06-01 | 2021-06-01 | Medicine information processing method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113223657A true CN113223657A (en) | 2021-08-06 |
Family
ID=77082254
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110610737.5A Pending CN113223657A (en) | 2021-06-01 | 2021-06-01 | Medicine information processing method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113223657A (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011243140A (en) * | 2010-05-21 | 2011-12-01 | Nec Corp | Medical information processing device, medical information processing system, medical information processing method, and medical information processing program |
| CN103455608A (en) * | 2013-09-05 | 2013-12-18 | 广东医药价格协会 | Management and inquiry system based on medicine coding |
| US20160055313A1 (en) * | 2014-08-22 | 2016-02-25 | Drfirst.Com, Inc. | Method and System For Recommending Prescription Strings |
| CN107103048A (en) * | 2017-03-31 | 2017-08-29 | 苏州艾隆信息技术有限公司 | Medicine information matching process and system |
| CN107103046A (en) * | 2017-03-31 | 2017-08-29 | 苏州艾隆信息技术有限公司 | Medicine information data access method and system |
| CN107194143A (en) * | 2017-03-31 | 2017-09-22 | 苏州艾隆信息技术有限公司 | Medicine information data processing method and system |
| CN107203686A (en) * | 2017-03-31 | 2017-09-26 | 苏州艾隆信息技术有限公司 | medicine information difference processing method and system |
| KR20170110268A (en) * | 2016-03-23 | 2017-10-11 | 연세대학교 원주산학협력단 | Drug search apparatus and method |
| CN107818124A (en) * | 2017-03-03 | 2018-03-20 | 平安医疗健康管理股份有限公司 | Data matching method and device |
| CN108804423A (en) * | 2018-05-30 | 2018-11-13 | 平安医疗健康管理股份有限公司 | Medical Text character extraction and automatic matching method and system |
| CN109145192A (en) * | 2018-09-17 | 2019-01-04 | 珠海横琴盛达兆业科技投资有限公司 | A method of the automatic rate of exchange of drug are realized based on internet platform |
| CN110289068A (en) * | 2019-06-20 | 2019-09-27 | 北京百度网讯科技有限公司 | Drug recommended method and equipment |
| CN110781677A (en) * | 2019-10-12 | 2020-02-11 | 平安医疗健康管理股份有限公司 | Medicine information matching processing method and device, computer equipment and storage medium |
| CN111180086A (en) * | 2019-12-12 | 2020-05-19 | 平安医疗健康管理股份有限公司 | Data matching method and device, computer equipment and storage medium |
| 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 |
| CN111680165A (en) * | 2020-04-28 | 2020-09-18 | 中汇信息技术（上海）有限公司 | Information matching method and device, readable storage medium and electronic equipment |
| CN111798969A (en) * | 2020-06-29 | 2020-10-20 | 平安国际智慧城市科技股份有限公司 | Medical medicine matching method and device, electronic equipment and storage medium |
| 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 |
-
2021
- 2021-06-01 CN CN202110610737.5A patent/CN113223657A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011243140A (en) * | 2010-05-21 | 2011-12-01 | Nec Corp | Medical information processing device, medical information processing system, medical information processing method, and medical information processing program |
| CN103455608A (en) * | 2013-09-05 | 2013-12-18 | 广东医药价格协会 | Management and inquiry system based on medicine coding |
| US20160055313A1 (en) * | 2014-08-22 | 2016-02-25 | Drfirst.Com, Inc. | Method and System For Recommending Prescription Strings |
| KR20170110268A (en) * | 2016-03-23 | 2017-10-11 | 연세대학교 원주산학협력단 | Drug search apparatus and method |
| CN107818124A (en) * | 2017-03-03 | 2018-03-20 | 平安医疗健康管理股份有限公司 | Data matching method and device |
| CN107103046A (en) * | 2017-03-31 | 2017-08-29 | 苏州艾隆信息技术有限公司 | Medicine information data access method and system |
| CN107203686A (en) * | 2017-03-31 | 2017-09-26 | 苏州艾隆信息技术有限公司 | medicine information difference processing method and system |
| CN107194143A (en) * | 2017-03-31 | 2017-09-22 | 苏州艾隆信息技术有限公司 | Medicine information data processing method and system |
| CN107103048A (en) * | 2017-03-31 | 2017-08-29 | 苏州艾隆信息技术有限公司 | Medicine information matching process and system |
| CN108804423A (en) * | 2018-05-30 | 2018-11-13 | 平安医疗健康管理股份有限公司 | Medical Text character extraction and automatic matching method and system |
| CN109145192A (en) * | 2018-09-17 | 2019-01-04 | 珠海横琴盛达兆业科技投资有限公司 | A method of the automatic rate of exchange of drug are realized based on internet platform |
| CN110289068A (en) * | 2019-06-20 | 2019-09-27 | 北京百度网讯科技有限公司 | Drug recommended method and equipment |
| CN110781677A (en) * | 2019-10-12 | 2020-02-11 | 平安医疗健康管理股份有限公司 | Medicine information matching processing method and device, computer equipment and storage medium |
| CN111180086A (en) * | 2019-12-12 | 2020-05-19 | 平安医疗健康管理股份有限公司 | Data matching method and device, computer equipment and storage medium |
| 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 |
| CN111680165A (en) * | 2020-04-28 | 2020-09-18 | 中汇信息技术（上海）有限公司 | Information matching method and device, readable storage medium and electronic equipment |
| CN111798969A (en) * | 2020-06-29 | 2020-10-20 | 平安国际智慧城市科技股份有限公司 | Medical medicine matching method and device, electronic equipment and storage medium |
| 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 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111581976B (en) | Medical term standardization method, device, computer equipment and storage medium | |
| US11308286B2 (en) | Method and device for retelling text, server, and storage medium | |
| CN109783490B (en) | Data fusion method and device, computer equipment and storage medium | |
| US11249960B2 (en) | Transforming data for a target schema | |
| US11797593B2 (en) | Mapping of topics within a domain based on terms associated with the topics | |
| CN110750654A (en) | Knowledge graph acquisition method, device, equipment and medium | |
| US20190354596A1 (en) | Similarity matching systems and methods for record linkage | |
| US8880526B2 (en) | Phrase clustering | |
| CN111627512A (en) | Recommendation method and device for similar medical records, electronic equipment and storage medium | |
| CN110598200B (en) | Semantic recognition method and device | |
| CN114490642A (en) | Patient master index generation method, apparatus and medium | |
| CN108304381B (en) | Entity edge establishing method, device and equipment based on artificial intelligence and storage medium | |
| WO2023178978A1 (en) | Prescription review method and apparatus based on artificial intelligence, and device and medium | |
| CN113254673B (en) | Data management method, system, equipment and medium based on knowledge graph | |
| CN109033456B (en) | Condition query method and device, electronic equipment and storage medium | |
| US20240143605A1 (en) | System And Method For Improved State Identification And Prediction In Computerized Queries | |
| WO2022227171A1 (en) | Method and apparatus for extracting key information, electronic device, and medium | |
| CN113314207A (en) | Object recommendation method and device, storage medium and electronic equipment | |
| CN115620886B (en) | Data auditing method and device | |
| CN110990406B (en) | Fuzzy query method, device, equipment and medium | |
| CN109471918B (en) | Intermediate field tracing method, device and medium | |
| CN115759040A (en) | Electronic medical record analysis method, device, equipment and storage medium | |
| CN115762704A (en) | Prescription auditing method, device, equipment and storage medium | |
| CN113223657A (en) | Medicine information processing method and device, electronic equipment and storage medium | |
| CN113780675A (en) | Consumption prediction method and device, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |