CN118380126B - Medical article circulation management method and system - Google Patents
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Abstract
The application relates to a method and a system for managing circulation of medical articles, which relate to the technical field of medical articles, and the method comprises the following steps: after at least one information input box in the information input boxes is filled with characters and a search button is clicked, obtaining a character combination to be matched based on all the filled characters; analyzing each character string characteristic in the character combination to be matched based on a text analysis library; sequentially comparing the analysis characteristics with information chains in a preset database to judge whether the analysis characteristics are in at least one information chain; judging whether drug information corresponding to the information chain obtained by comparison contains a feature recognition object or not; the feature recognition object comprises at least one of a package pattern of a medicine and an advertisement poster; and displaying the drug information corresponding to at least one information chain obtained by comparison in a display control in sequence, and generating a feature recognition object display area beside the information chain containing the feature recognition object so as to display the feature recognition object.
Description
Technical Field
The application relates to the technical field of medical supplies, in particular to a method and a system for managing circulation of medical objects.
Background
In modern medical systems, the management of the circulation of medical items is a very important ring. Such items include, but are not limited to, medical instruments, pharmaceuticals, medical consumables, etc., whose circulation involves a number of links, such as purchasing, storing, dispensing, use, etc. In order to effectively manage these medical items, an efficient and accurate circulation management system is needed. Because of the specificity of medical items, the requirements for a diversion management system are also higher, for example, information such as names, approval papers, specifications, manufacturers, prescription classifications, etc. of the items need to be queried quickly.
The existing medical article circulation management system requires that a user can input accurate information of articles to be inquired in an information input box, then click a search button, the system can search in a database according to the input accurate information, and a search result is displayed in a display control. When the information input by the user is not completely matched, the matching result cannot be displayed, and the use and the recognition of the common user are inconvenient.
Disclosure of Invention
In order to at least partially solve the technical problems, the application provides a method and a system for managing circulation of medical articles.
In a first aspect, the present application provides a method for managing circulation of medical articles, which adopts the following technical scheme.
The medical article circulation management method is applied to a medical article circulation management system, and the medical article circulation management system comprises an information input control and an information display control; the information input control comprises a plurality of information input boxes and search buttons, and the method comprises the following steps:
After at least one information input box in a plurality of information input boxes is filled with characters and the search button is clicked, obtaining a character combination to be matched based on all the filled characters; the filled characters in each information input box correspond to a character string characteristic of the character combination to be matched; the information input box comprises a name, an approval document, a specification, a manufacturer and a prescription classification;
Analyzing each character string feature in the character combination to be matched based on a text analysis library, obtaining corresponding analysis features for the character string features which can be analyzed, and storing corresponding filled characters for the character string features which cannot be analyzed;
Sequentially comparing the analysis characteristics with information chains in a preset database to judge whether the analysis characteristics are in at least one information chain; the information chain consists of a standard name of name, approval document, specification, manufacturer and prescription classification;
Judging whether drug information corresponding to the information chain obtained by comparison contains a feature recognition object or not; the feature recognition object comprises at least one of a package pattern of a medicine and an advertisement propaganda;
And displaying the drug information corresponding to at least one information chain obtained by comparison in the display control in sequence, and generating a feature recognition object display area beside the information chain containing the feature recognition object so as to display the feature recognition object.
By adopting the technical scheme, fuzzy information which cannot be completely matched and input by a user can be matched, so that the user can conveniently search medicine information; and the drug information with the feature recognition object is displayed by generating the feature object display area, so that a user of the system can quickly confirm the drug.
Optionally, the method further comprises: when character string features which cannot be analyzed exist and are matched with at least one information chain, storing filled characters and information chains corresponding to the character string features which cannot be analyzed into a management page of a text analysis library managed by the medical article circulation management system; the management page is used for an administrator to manage a text analysis library; judging whether the filled characters are matched with a certain part of an information chain or not in the management page by an administrator, if yes, after a confirmation adding button of the management page is clicked, adding the filled characters into the text analysis library after text preprocessing to be used as a matching feature of the text analysis library; the matching features are mapped to a portion of the information chain.
Optionally, the method further comprises:
Recording each searching action of the login account; the searching action comprises clicking conditions of filled characters and medicine information obtained by searching, wherein the clicking conditions are input in the information input box during searching;
analyzing user preferences based on the search action, the analysis features and the information chain displayed in the display control to analyze preferences of the login account on medicine types, brands and specifications;
And when the analysis features are compared with the information chains in the preset database in turn and the existence of at least two information chains is judged, the information chains displayed in the display control are ordered based on the analysis result of the user preference, so that the information chains conforming to the user preference are preferentially displayed.
Optionally, the medical article circulation management system further comprises a data collection page; the circulation management method further comprises the following steps: after the acquired data page is clicked, displaying the acquired data page; the data source frame, the description frame, the number of codes not to be matched and the total item number are displayed on the horizontal columns of the data acquisition page; the array of the collected data page is used for displaying serial numbers; the row of the acquisition data page further comprises: adding a marketing catalog button and a code result export button;
after the button for adding the marketing catalogue is clicked, adding the drug information of the corresponding course into the marketing catalogue;
Jumping to a first result display page at the export code matching result button; the first result display page is used for displaying opposite display results.
Optionally, after the drug information corresponding to at least one information chain obtained by comparison is displayed in the display control in sequence, the method further includes:
After the medicine information corresponding to one information chain is clicked, generating a medicine recommendation list matched with the clicked medicine information through a trained medicine recommendation model;
The training method of the medicine recommendation model comprises the following steps:
S1, collecting data of drug combination use based on a historical prescription record and a clinical drug guide;
S2, selecting an FP-Growth algorithm and setting the confidence coefficient of the model;
and S3, training an initial medicine recommendation model through the collected data, and obtaining the medicine recommendation model when the confidence coefficient of the model obtained through training is larger than the set confidence coefficient.
Optionally, the medical article circulation management system further comprises a login verification module, wherein the login verification module is used for verifying when a user logs in; the circulation management method further comprises a login verification step; the login verification step comprises the following steps:
When the login interface passes the account password verification and the equipment information and the network address information of the login account are detected to be changed;
judging the risk level of the account based on the equipment information and the network address information;
matching corresponding information verification modes based on the account risk level; the risk level comprises a first level, a second level and a third level from large to small;
when the risk level is a first level, generating a first control in a login interface; a plurality of selection objects are included in the first control; at least one selected object is preset for a corresponding equipment account and is recorded as a preset object; when a preconfigured object in the first control is selected and a non-preconfigured object is not selected, finishing the secondary verification of the login account, and entering a medical article circulation management system;
When the risk level is the second level, generating a second control in the login interface; the second control is a sliding verification control; when the sliding block of the second control moves to a preset area range and the sliding track of the sliding block accords with a track algorithm, finishing the secondary verification of the login account, and entering a medical article circulation management system;
When the risk level is a third level, generating a third control in the login interface; the third control comprises a plurality of selection objects and a questioning text box; one of the selection objects is inconsistent with the answer in the questioning text box and is preset, and the selection object is marked as a confusing object; when only the confusing object is selected, finishing the secondary verification of the login account, and entering a medical article circulation management system; the preconfiguration object is a plurality of; and when the number of times of regenerating the third control for verification exceeds the preset number of times, locking the login account.
Optionally, the determining the account risk level based on the device information and the network address information includes:
Identifying an equipment identity identification code and an equipment network address in a login request based on the login request sent by equipment through account password verification at a login interface;
Judging whether the network address of the device is matched with the common address: if not, get the first abnormal condition
Judging whether the identity identification code is one of a white list or not; if not, a second abnormal condition is obtained;
when only the first abnormal condition exists, judging the risk level as a first level;
when only the second abnormal condition exists, judging the risk level as a second level;
And when the first abnormal condition and the second abnormal condition exist, judging that the risk level is a third level.
In a second aspect, the present application provides a system for managing circulation of medical articles, which adopts the following technical scheme.
A medical article circulation management system comprises an information input control and an information display control; the information input control comprises a plurality of information input boxes and search buttons; the system further comprises:
A first processing module for: after at least one information input box in a plurality of information input boxes is filled with characters and the search button is clicked, obtaining a character combination to be matched based on all the filled characters; the filled characters in each information input box correspond to a character string characteristic of the character combination to be matched; the information input box comprises a name, an approval document, a specification, a manufacturer and a prescription classification;
a second processing module for: analyzing each character string feature in the character combination to be matched based on a text analysis library, obtaining corresponding analysis features for the character string features which can be analyzed, and storing corresponding filled characters for the character string features which cannot be analyzed;
A third processing module for: sequentially comparing the analysis characteristics with information chains in a preset database to judge whether the analysis characteristics are in at least one information chain; the information chain consists of a standard name of name, approval document, specification, manufacturer and prescription classification;
a fourth processing module for: judging whether drug information corresponding to the information chain obtained by comparison contains a feature recognition object or not; the feature recognition object comprises at least one of a package pattern of a medicine and an advertisement propaganda;
A fifth processing module for: and displaying the drug information corresponding to at least one information chain obtained by comparison in the display control in sequence, and generating a feature recognition object display area beside the information chain containing the feature recognition object so as to display the feature recognition object.
In a third aspect, the application discloses an electronic device comprising a memory and a processor, the memory having stored thereon a computer program to be loaded by the processor and to perform any of the methods described above.
In a fourth aspect, the present application discloses a computer readable storage medium storing a computer program capable of being loaded by a processor and performing any of the methods described above.
Drawings
FIG. 1 is a flow chart of a method of managing the circulation of medical items according to an embodiment of the present application;
FIG. 2 is a system block diagram of a method of managing circulation of medical items according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an electronic device according to the present invention;
In the figure, 201, a first processing module; 202. a second processing module; 203. a third processing module; 204. a fourth processing module; 205. and a fifth processing module.
Detailed Description
The application is further illustrated by the following description of the embodiments in conjunction with the accompanying figures 1-3:
First, what needs to be described here is: in the description of the present application, terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like are used for convenience of description only as regards orientation or positional relationship as shown in the accompanying drawings, and do not denote or imply that the apparatus or element in question must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present application; moreover, the numerical terms such as the terms "first," "second," "third," etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, unless explicitly stated or limited otherwise, the terms "mounted," "connected," and "connected" should be construed broadly, and may be, for example, a fixed connection, a releasable connection, an interference fit, a transition fit, or an integral connection; can be directly connected or indirectly connected through an intermediate medium; the specific meaning of the above terms in the present application will be understood by those skilled in the art according to the specific circumstances.
The embodiment of the application discloses a circulation management method of medical articles. Referring to fig. 1, as an embodiment of a method for managing circulation of medical articles, the method is applied to a medical article circulation management system, and the medical article circulation management system comprises an information input control and an information display control; the information input control comprises a plurality of information input boxes and search buttons; the circulation management method of the medical articles comprises the following steps:
Step 101, after at least one information input box in a plurality of information input boxes is filled with characters and the search button is clicked, obtaining a character combination to be matched based on all the filled characters; the filled characters in each information input box correspond to a character string characteristic of the character combination to be matched; the information input box comprises a name, an approval document, a specification, a manufacturer and a prescription classification.
Specifically, the information input control comprises a plurality of information input boxes, the information input boxes are used for receiving text information input by information input equipment (such as a keyboard), the information input boxes comprise names, approval text numbers, specifications, manufacturers and prescription classifications, and a user can fill text in one of the information input boxes or can fill text in a plurality of the information input boxes. Generally, the more information input boxes are filled with characters and the characters can be identified and confirmed, the fewer the search results are obtained, the more the results are matched, for example, the input medicine name is "AA", the approval name is "BB", the approval document number is "CC", the specification is "500ML", the manufacturer is "DD" and the prescription is classified as "non-prescription", and at this time, if the characters can be identified and matched, the number of the displayed results is basically unique; when the drug name "AA" is merely input, the number of results displayed is greatly increased over the former.
After the user clicks the search button, the system obtains a word combination to be matched based on all the filled words. In one embodiment, the system combines the character strings (one character segment) of each input character in the order of setting or numbering the information input boxes, and performs the blank processing for the information input boxes in which no character is input.
Step 102, analyzing each character string feature in the character combination to be matched based on a text analysis library, obtaining corresponding analysis features for the character string features which can be analyzed, and storing corresponding filled characters for the character string features which cannot be analyzed.
Specifically, the text parsing library is preconfigured for the system, and the text parsing library can be dynamically updated through a management background; the user may not know the specific information and the input text is ambiguous and unclear when inputting the information in the information input box, and if the ambiguous or wrong field cannot be recognized at all, the medicine is easy to exist in the system in practice but cannot be searched. In one embodiment of the system, the text parsing library has a mapping relationship, and has A1 and a2. A1 can be the correct manufacturer name, A2 can be the short name of the manufacturer name, A3 is the harmonic name with interference when the manufacturer name is input by an input method, and A4 is the common name of the manufacturer name. And analyzing each character string characteristic in the character combination to be matched based on the text analysis library, and obtaining corresponding analysis characteristics for the character string characteristics which can be analyzed. And storing corresponding filled characters for the character string features which cannot be analyzed, wherein the character string features cannot be identified, and at the moment, the character string features are emptied, and identification and matching are not carried out through the character string features which cannot be analyzed.
Step 103, comparing the analysis characteristics with information chains in a preset database in sequence to judge whether the analysis characteristics are in at least one information chain; the information chain consists of a standard name of name, approval document, specification, manufacturer and prescription classification.
Specifically, the system sequentially compares the analysis features with information chains in a preset database to judge, namely sequentially matches the analysis features to obtain the information chains.
104, Judging whether drug information corresponding to the information chain obtained by comparison contains a feature recognition object or not; the feature recognition object includes at least one of a packaging pattern of a pharmaceutical product and an advertising placard.
And 105, displaying the drug information corresponding to at least one information chain obtained by comparison in the display control in sequence, and generating a feature recognition object display area beside the information chain containing the feature recognition object so as to display the feature recognition object.
Specifically, the system displays the obtained medicine corresponding to at least one information chain in the display control, so that the user can search more conveniently through the processing of the input information, the user does not need to remember each characteristic of the medicine, and the medicine information can be searched more quickly.
As a specific embodiment of the circulation management method of a medical article, the method further includes: when character string features which cannot be analyzed exist and are matched with at least one information chain, storing filled characters and information chains corresponding to the character string features which cannot be analyzed into a management page of a text analysis library managed by the medical article circulation management system; the management page is used for an administrator to manage a text analysis library; and judging whether the filled-in characters are matched with a certain part of an information chain or not in the management page by an administrator, and if so, adding the filled-in characters into the text analysis library after a confirmation adding button of the management page is clicked.
Specifically, when there are unresolved string features, these features are saved to a text parsing library of the medical item flow management system. The management page provides the function of managing the text parsing library by an administrator. The administrator may determine in the administrative page whether the filled text matches a portion of the information chain. If so, the filled-in text will be added to the text parsing library after the confirm add button is clicked. Through the management page of the text analysis library, an administrator can conveniently check and update the text analysis library, and the analysis capability of the text analysis library is improved.
Analyzing each character string characteristic in the character combination to be matched based on a text analysis library, including:
S1, removing punctuation marks and special words in character string characteristics;
s2, segmenting character strings into single words;
s3, mapping the non-standard vocabulary to the standard vocabulary;
the character string features are processed based on S1, S2 and S3 to obtain a to-be-matched speech segment;
Matching the to-be-matched speech segments based on the similarity sorting to select matching features of the first five similarity in a text analysis library; if the matching is complete, the analysis can be performed;
And obtaining corresponding information in the information chain based on the matched characteristics.
The information input control also comprises a code matching button; the circulation management method further comprises the following steps: and after the code matching button is clicked, code matching is carried out based on the code matching information acquired by the acquired data page.
As a specific embodiment of the circulation management method of a medical article, the method further includes:
Recording each searching action of the login account; the searching action comprises clicking conditions of filled characters and medicine information obtained by searching, wherein the clicking conditions are input in the information input box during searching;
analyzing user preferences based on the search action, the analysis features and the information chain displayed in the display control to analyze preferences of the login account on medicine types, brands and specifications;
And when the analysis features are compared with the information chains in the preset database in turn and the existence of at least two information chains is judged, the information chains displayed in the display control are ordered based on the analysis result of the user preference, so that the information chains conforming to the user preference are preferentially displayed.
Specifically, the system records each search action of one login account. And analyzing the preference of the user based on the search action of the user, including the input text and clicked medicine information, analyzing the characteristics and displaying the information chain displayed in the control. The purpose of the analysis is to determine the preferences of the login account for drug categories, brands and specifications. And in the next searching process, the system sequentially compares the analysis characteristics with information chains in a preset database. When there are not less than two information chains, the system orders the information chains displayed in the display control based on the analysis result of the user preference, so as to preferentially display the information chains conforming to the user preference. The following is a simple example: the user logs into the system and performs a drug search, entering "antibiotics" as search terms. The system records the search word of the user and tracks the clicking behavior of the user on different medicine information in the search result. By analyzing the clicking behavior of the user, the system identifies that the user has a higher click frequency on the "XX brand" antibiotic drug. The next time the user searches for "antibiotics", the system ranks the relevant drug information for "XX brand" in the front of the search results.
As a specific implementation mode of the medical article circulation management method, the medical article circulation management system also comprises a collected data page; the circulation management method further comprises the following steps: after the acquired data page is clicked, displaying the acquired data page; the data source frame, the description frame, the number of codes not to be matched and the total item number are displayed on the horizontal columns of the data acquisition page; the array of the collected data page is used for displaying serial numbers; the row of the acquisition data page further comprises: adding a marketing catalog button and a code result export button;
after the button for adding the marketing catalogue is clicked, adding the drug information of the corresponding course into the marketing catalogue;
Jumping to a first result display page at the export code matching result button; the first result display page is used for displaying opposite display results.
As one embodiment of the method for managing circulation of medical articles, after drug information corresponding to at least one information chain obtained by comparison is displayed in the display control in sequence, the method further comprises:
After the medicine information corresponding to one information chain is clicked, generating a medicine recommendation list matched with the clicked medicine information through a trained medicine recommendation model;
The training method of the medicine recommendation model comprises the following steps:
S1, collecting data of drug combination use based on a historical prescription record and a clinical drug guide;
S2, selecting an FP-Growth algorithm and setting the confidence coefficient of the model;
s3, training an initial medicine recommendation model through collected data, and obtaining the medicine recommendation model when the confidence coefficient of the model obtained through training is larger than the set confidence coefficient;
The FP-Growth algorithm in S2 comprises the following steps:
creating an empty FP-Tree and head pointer table;
taking one recommended prescription in each prescription record and clinical medicine guide as a transaction, and taking medicine combinations as item sets in the transaction; traversing each transaction, ordering items in the transaction according to frequency, and constructing an initial FP-Tree;
for each frequent item in the FP-Tree, creating a transaction set ending with the item;
the set of frequent items is recursively mined from the conditional pattern base until a minimum support threshold is met.
Specifically, during the model training phase, data is collected based on historical prescription records and clinical drug guidelines, including which drugs are often used in combination, as well as drug combinations that are typically recommended in clinical practice. The FP-Growth algorithm is good at identifying frequently occurring modes in mass data, namely the combined use condition of medicines, and reveals direct compatibility relation among medicines. In the training process, model parameters are continuously adjusted to optimize the prediction accuracy. By evaluating the confidence of the model, it is ensured that the model is able to provide accurate drug recommendations when it exceeds a set threshold. Therefore, the system can automatically generate the personalized medicine recommendation list, and the user can use the personalized medicine recommendation list conveniently.
As one embodiment of a method for managing circulation of medical items, training an initial drug recommendation model by collected data includes:
removing noise data and filling missing data from the data in the step S1;
the initial drug recommendation model is provided with an input layer: receiving drug information and historical prescription information; an embedding layer: converting the drug information and the historical prescription information into low-dimensional vectors; interaction layer: capturing potential relationships between drugs using an attention mechanism or matrix decomposition; output layer: predicting the confidence of the drug combination;
Selecting data with noise data removed and data with missing data filled in, inputting the data into an initial medicine recommendation model, and minimizing a loss function through an optimizer;
calculating F1 fraction; wherein F1 Score = ; Wherein P is the precision, and the characterization predicts that the sample is positive, and the actual ratio is positive; p= (true case)/(true case + false case); r recall, representing a positive proportion predicted in the sample; r= (true case)/(true case + false negative case); true examples, i.e., the number of samples for which the model predicts to be positive and is actually positive; true negative example: model prediction is negative and actually negative sample: false positive example: a sample where the model predicts positive but actually negative; false negative example: samples where the model predicts negative but is actually positive;
And obtaining the confidence of the model through the F1 score.
Specifically, by identifying and rejecting outliers or irrelevant data, the data quality is improved and the interference in the model training process is reduced. By filling in the missing values, the integrity of the data is ensured, and the model can learn the characteristics of the data more comprehensively. The input layer receives drug information and historical prescription information, providing the necessary input data for the model. The embedded layer converts the medicine information and the historical prescription information into low-dimensional vectors, so that the dimensionality of data is reduced, key information is reserved, and the calculation efficiency and generalization capability of the model are improved. The interaction layer captures potential relationships between drugs using an attention mechanism or matrix decomposition, helping the model understand the complexity and interactions of different drug combinations.
The output layer predicts the confidence of the drug combination and provides a quantified evaluation criterion for drug recommendation.
Model parameters are adjusted through optimizers (such as SGD, adam and the like) so that the prediction result of the model is as close to the real situation as possible, and therefore the accuracy of the model is improved. The F1 fraction comprehensively considers the accuracy and the recall rate, provides a balanced evaluation index for the model, and is beneficial to evaluating the comprehensive performance of the model on positive samples and negative samples. The confidence of the model, namely the reliability of model prediction, can be obtained through the F1 score. The high F1 score means that the model performs well in terms of accuracy and recall, and the recommended results are more reliable.
As one embodiment of the medical article circulation management method, the medical article circulation management system further comprises a login verification module, wherein the login verification module is used for verifying when a user logs in; the circulation management method further comprises a login verification step; the login verification step comprises the following steps:
When the login interface passes the account password verification and the equipment information and the network address information of the login account are detected to be changed;
judging the risk level of the account based on the equipment information and the network address information;
matching corresponding information verification modes based on the account risk level; the risk level comprises a first level, a second level and a third level from large to small;
when the risk level is a first level, generating a first control in a login interface; a plurality of selection objects are included in the first control; at least one selected object is preset for a corresponding equipment account and is recorded as a preset object; when a preconfigured object in the first control is selected and a non-preconfigured object is not selected, finishing the secondary verification of the login account, and entering a medical article circulation management system;
When the risk level is the second level, generating a second control in the login interface; the second control is a sliding verification control; when the sliding block of the second control moves to a preset area range and the sliding track of the sliding block accords with a track algorithm, finishing the secondary verification of the login account, and entering a medical article circulation management system;
When the risk level is a third level, generating a third control in the login interface; the third control comprises a plurality of selection objects and a questioning text box; one of the selection objects is inconsistent with the answer in the questioning text box and is preset, and the selection object is marked as a confusing object; when only the confusing object is selected, finishing the secondary verification of the login account, and entering a medical article circulation management system; the preconfiguration object is a plurality of; and when the number of times of regenerating the third control for verification exceeds the preset number of times, locking the login account.
Specifically, through the login verification module, the system performs verification when a user logs in, including account password verification and detection of equipment information and network address information. The method is helpful for preventing malicious users from performing illegal operations by counterfeiting legal user identities, and improves the security of the system. The risk level of the account is judged based on the equipment information and the network address information, and corresponding information verification modes are matched according to the risk level, so that the system can take different secondary verification measures according to users with different risk levels, and the security of the login account is improved. When the risk level is the first level, the system generates a first control in the login interface, wherein the first control comprises a plurality of selection objects, and at least one selection object is a pre-configuration object. When the user selects the preconfigured object and does not select the non-preconfigured object, the user can finish the secondary verification and enter the medical article circulation management system. And when the risk level is the second level, the system generates a second control in the login interface, and the second control is a sliding verification control. And when the sliding track of the sliding block is in accordance with the track algorithm and the sliding track of the sliding block is required to be moved to the preset area range by a user, the second verification can be completed and the sliding block enters the medical article circulation management system. When the risk level is a third level, the system generates a third control in the login interface, wherein the third control comprises a selection object and a questioning text box. The user needs to select only the confusing object and answer correctly to complete the secondary verification and enter the medical item circulation management system. When the number of times of regenerating the third control for verification exceeds the preset number of times, the system locks the login account. This helps prevent malicious users from gaining improper benefits or doing other malicious acts by frequently attempting to crack the authentication means. Through the arrangement, the login security of the user is improved, different verification measures are adopted according to different risk levels, multiple verification modes are provided, malicious behaviors are limited, and therefore the security of the system is ensured.
As one embodiment of the method for managing circulation of medical articles, the determining the account risk level based on the device information and the network address information includes:
Identifying an equipment identity identification code and an equipment network address in a login request based on the login request sent by equipment through account password verification at a login interface;
Judging whether the network address of the device is matched with the common address: if not, get the first abnormal condition
Judging whether the identity identification code is one of a white list or not; if not, a second abnormal condition is obtained;
when only the first abnormal condition exists, judging the risk level as a first level;
when only the second abnormal condition exists, judging the risk level as a second level;
And when the first abnormal condition and the second abnormal condition exist, judging that the risk level is a third level.
The application also provides a medical article circulation management system which is one implementation mode of the medical article circulation management method, and comprises an information input control and an information display control; the information input control comprises a plurality of information input boxes and search buttons; the system further comprises:
a first processing module 201, configured to: after at least one information input box in a plurality of information input boxes is filled with characters and the search button is clicked, obtaining a character combination to be matched based on all the filled characters; the filled characters in each information input box correspond to a character string characteristic of the character combination to be matched; the information input box comprises a name, an approval document, a specification, a manufacturer and a prescription classification;
A second processing module 202 for: analyzing each character string feature in the character combination to be matched based on a text analysis library, obtaining corresponding analysis features for the character string features which can be analyzed, and storing corresponding filled characters for the character string features which cannot be analyzed;
a third processing module 203, configured to: sequentially comparing the analysis characteristics with information chains in a preset database to judge whether the analysis characteristics are in at least one information chain; the information chain consists of a standard name of name, approval document, specification, manufacturer and prescription classification;
A fourth processing module 204 for: judging whether drug information corresponding to the information chain obtained by comparison contains a feature recognition object or not; the feature recognition object comprises at least one of a package pattern of a medicine and an advertisement propaganda;
a fifth processing module 205, configured to: and displaying the drug information corresponding to at least one information chain obtained by comparison in the display control in sequence, and generating a feature recognition object display area beside the information chain containing the feature recognition object so as to display the feature recognition object.
The inventive embodiments also provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is for causing the computer to perform the method of the inventive embodiments.
The inventive embodiments also provide a computer program product comprising a computer program, wherein the computer program is for causing a computer to carry out the method of the inventive embodiments when executed by a processor of the computer.
The embodiment of the invention also provides an electronic device, which comprises: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor for causing an electronic device to perform the method of the inventive embodiments when executed by the at least one processor.
With reference to fig. 3, a block diagram of an electronic device that may be a server or a client of an embodiment of the invention will now be described, which is an example of a hardware device that may be applied to aspects of the invention. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device includes a computing unit 401 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in the electronic device are connected to the I/O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 may be any type of device capable of inputting information to an electronic device, and the input unit 406 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device. The output unit 407 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 408 may include, but is not limited to, magnetic disks, optical disks. The communication unit 409 allows the electronic device to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and/or wireless communication transceivers, such as bluetooth devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
The computing unit 401 may be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 401 include, but are not limited to, a CPU, a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing units, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), as well as any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the respective methods and processes described above. For example, in some embodiments, the inventive method embodiments may be implemented as a computer program tangibly embodied on a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device via the ROM 402 and/or the communication unit 409. In some embodiments, the computing unit 401 may be configured to perform the above-described methods by any other suitable means (e.g., by means of firmware).
A computer program for implementing a method of an inventive embodiment may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the inventive embodiments, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
It should be noted that the term "comprising" and its variants as used in the embodiments of the invention are open-ended, i.e. "including but not limited to". The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. The references to "one" or "a plurality" of modifications in the embodiments of the invention are intended to be illustrative rather than limiting, and it will be understood by those of ordinary skill in the art that "one or more" is intended to be interpreted as "one or more" unless the context clearly indicates otherwise.
The steps recited in the method embodiments provided by the inventive examples may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of protection created by the present invention is not limited in this respect.
The term "embodiment" in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive. The various embodiments in this specification are described in a related manner, with identical and similar parts being referred to each other. In particular, for apparatus, devices, system embodiments, the description is relatively simple as it is substantially similar to method embodiments, see for relevant part of the description of method embodiments.
The above examples merely represent several embodiments of the invention, which are described in more detail and are not to be construed as limiting the scope of protection. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. The scope of the invention should, therefore, be determined with reference to the appended claims.
Claims (8)
1. The medical article circulation management method is applied to a medical article circulation management system, and the medical article circulation management system comprises an information input control and an information display control; the information input control comprises a plurality of information input boxes and search buttons; characterized in that the method comprises:
After at least one information input box in a plurality of information input boxes is filled with characters and the search button is clicked, obtaining a character combination to be matched based on all the filled characters; the filled characters in each information input box correspond to a character string characteristic of the character combination to be matched; the information input box comprises a name, an approval document, a specification, a manufacturer and a prescription classification;
Analyzing each character string feature in the character combination to be matched based on a text analysis library, obtaining corresponding analysis features for the character string features which can be analyzed, and storing corresponding filled characters for the character string features which cannot be analyzed;
Sequentially comparing the analysis characteristics with information chains in a preset database to judge whether the analysis characteristics are in at least one information chain; the information chain consists of a standard name of name, approval document, specification, manufacturer and prescription classification;
Judging whether drug information corresponding to the information chain obtained by comparison contains a feature recognition object or not; the feature recognition object comprises at least one of a package pattern of a medicine and an advertisement propaganda;
Sequentially displaying drug information corresponding to at least one information chain obtained through comparison in the display control, and generating a feature recognition object display area beside the information chain containing the feature recognition object so as to display the feature recognition object;
After the drug information corresponding to at least one information chain obtained through comparison is displayed in the display control in sequence, the method further comprises the steps of:
After the medicine information corresponding to one information chain is clicked, generating a medicine recommendation list matched with the clicked medicine information through a trained medicine recommendation model;
The training method of the medicine recommendation model comprises the following steps:
S1, collecting data of drug combination use based on a historical prescription record and a clinical drug guide;
S2, selecting an FP-Growth algorithm and setting the confidence coefficient of the model;
s3, training an initial medicine recommendation model through collected data, and obtaining the medicine recommendation model when the confidence coefficient of the model obtained through training is larger than the set confidence coefficient;
The FP-Growth algorithm in S2 comprises the following steps:
creating an empty FP-Tree and head pointer table;
taking one recommended prescription in each prescription record and clinical medicine guide as a transaction, and taking medicine combinations as item sets in the transaction; traversing each transaction, ordering items in the transaction according to frequency, and constructing an initial FP-Tree;
for each frequent item in the FP-Tree, creating a transaction set ending with the item;
recursively mining the frequent item set from the conditional pattern base until a minimum support threshold is met;
training an initial drug recommendation model from the collected data, comprising:
removing noise data and filling missing data from the data in the step S1;
the initial drug recommendation model is provided with an input layer: receiving drug information and historical prescription information; an embedding layer: converting the drug information and the historical prescription information into low-dimensional vectors; interaction layer: capturing potential relationships between drugs using an attention mechanism or matrix decomposition; output layer: predicting the confidence of the drug combination;
Selecting data with noise data removed and data with missing data filled in, inputting the data into an initial medicine recommendation model, and minimizing a loss function through an optimizer;
calculating F1 fraction; wherein F1 Score = ; Wherein P is the precision, and the characterization predicts that the sample is positive, and the actual ratio is positive; p= (true case)/(true case + false case); r recall, representing a positive proportion predicted in the sample; r= (true case)/(true case + false negative case); true examples, i.e., the number of samples for which the model predicts to be positive and is actually positive; true negative example: model prediction is negative and actually negative sample: false positive example: a sample where the model predicts positive but actually negative;
false negative example: samples where the model predicts negative but is actually positive;
And obtaining the confidence of the model through the F1 score.
2. The method for managing the circulation of medical articles according to claim 1, characterized in that the method further comprises: when character string features which cannot be analyzed exist and are matched with at least one information chain, storing filled characters and information chains corresponding to the character string features which cannot be analyzed into a management page of a text analysis library managed by the medical article circulation management system; the management page is used for an administrator to manage a text analysis library; judging whether the filled characters are matched with a certain part of an information chain or not in the management page by an administrator, if yes, after a confirmation adding button of the management page is clicked, adding the filled characters into the text analysis library after text preprocessing to be used as a matching feature of the text analysis library; the matching features are mapped to a portion of the information chain.
3. A method of managing the circulation of medical articles according to claim 2, characterized in that the method further comprises:
Recording each searching action of the login account; the searching action comprises clicking conditions of filled characters and medicine information obtained by searching, wherein the clicking conditions are input in the information input box during searching;
analyzing user preferences based on the search action, the analysis features and the information chain displayed in the display control to analyze preferences of the login account on medicine types, brands and specifications;
And when the analysis features are compared with the information chains in the preset database in turn and the existence of at least two information chains is judged, the information chains displayed in the display control are ordered based on the analysis result of the user preference, so that the information chains conforming to the user preference are preferentially displayed.
4. A method of managing the circulation of medical articles according to claim 3, wherein the medical article circulation management system further comprises collecting data pages; the circulation management method further comprises the following steps: after the acquired data page is clicked, displaying the acquired data page; the data source frame, the description frame, the number of codes not to be matched and the total item number are displayed on the horizontal columns of the data acquisition page; the array of the collected data page is used for displaying serial numbers; the row of the acquisition data page further comprises: adding a marketing catalog button and a code result export button;
after the button for adding the marketing catalogue is clicked, adding the drug information of the corresponding course into the marketing catalogue;
Jumping to a first result display page at the export code matching result button; the first result display page is used for displaying opposite display results.
5. The method for circulation management of medical articles according to claim 4, wherein the medical article circulation management system further comprises a login verification module for verification when a user logs in; the circulation management method further comprises a login verification step; the login verification step comprises the following steps:
When the login interface passes the account password verification and the equipment information and the network address information of the login account are detected to be changed;
judging the risk level of the account based on the equipment information and the network address information;
matching corresponding information verification modes based on the account risk level; the risk level comprises a first level, a second level and a third level from large to small;
when the risk level is a first level, generating a first control in a login interface; a plurality of selection objects are included in the first control; at least one selected object is preset for a corresponding equipment account and is recorded as a preset object; when a preconfigured object in the first control is selected and a non-preconfigured object is not selected, finishing the secondary verification of the login account, and entering a medical article circulation management system;
When the risk level is the second level, generating a second control in the login interface; the second control is a sliding verification control; when the sliding block of the second control moves to a preset area range and the sliding track of the sliding block accords with a track algorithm, finishing the secondary verification of the login account, and entering a medical article circulation management system;
When the risk level is a third level, generating a third control in the login interface; the third control comprises a plurality of selection objects and a questioning text box; one of the selection objects is inconsistent with the answer in the questioning text box and is preset, and the selection object is marked as a confusing object; when only the confusing object is selected, finishing the secondary verification of the login account, and entering a medical article circulation management system; the preconfiguration object is a plurality of; and when the number of times of regenerating the third control for verification exceeds the preset number of times, locking the login account.
6. The medical article circulation management system is characterized by comprising an information input control and an information display control; the information input control comprises a plurality of information input boxes and search buttons; the system further comprises:
A first processing module for: after at least one information input box in a plurality of information input boxes is filled with characters and the search button is clicked, obtaining a character combination to be matched based on all the filled characters; the filled characters in each information input box correspond to a character string characteristic of the character combination to be matched; the information input box comprises a name, an approval document, a specification, a manufacturer and a prescription classification;
a second processing module for: analyzing each character string feature in the character combination to be matched based on a text analysis library, obtaining corresponding analysis features for the character string features which can be analyzed, and storing corresponding filled characters for the character string features which cannot be analyzed;
A third processing module for: sequentially comparing the analysis characteristics with information chains in a preset database to judge whether the analysis characteristics are in at least one information chain; the information chain consists of a standard name of name, approval document, specification, manufacturer and prescription classification;
a fourth processing module for: judging whether drug information corresponding to the information chain obtained by comparison contains a feature recognition object or not; the feature recognition object comprises at least one of a package pattern of a medicine and an advertisement propaganda;
A fifth processing module for: sequentially displaying drug information corresponding to at least one information chain obtained through comparison in the display control, and generating a feature recognition object display area beside the information chain containing the feature recognition object so as to display the feature recognition object;
After the drug information corresponding to at least one information chain obtained through comparison is displayed in the display control in sequence, the method further comprises the following steps:
After the medicine information corresponding to one information chain is clicked, generating a medicine recommendation list matched with the clicked medicine information through a trained medicine recommendation model;
The training method of the medicine recommendation model comprises the following steps:
S1, collecting data of drug combination use based on a historical prescription record and a clinical drug guide;
S2, selecting an FP-Growth algorithm and setting the confidence coefficient of the model;
s3, training an initial medicine recommendation model through collected data, and obtaining the medicine recommendation model when the confidence coefficient of the model obtained through training is larger than the set confidence coefficient;
The FP-Growth algorithm in S2 comprises the following steps:
creating an empty FP-Tree and head pointer table;
taking one recommended prescription in each prescription record and clinical medicine guide as a transaction, and taking medicine combinations as item sets in the transaction; traversing each transaction, ordering items in the transaction according to frequency, and constructing an initial FP-Tree;
for each frequent item in the FP-Tree, creating a transaction set ending with the item;
recursively mining the frequent item set from the conditional pattern base until a minimum support threshold is met;
training an initial drug recommendation model from the collected data, comprising:
removing noise data and filling missing data from the data in the step S1;
the initial drug recommendation model is provided with an input layer: receiving drug information and historical prescription information; an embedding layer: converting the drug information and the historical prescription information into low-dimensional vectors; interaction layer: capturing potential relationships between drugs using an attention mechanism or matrix decomposition; output layer: predicting the confidence of the drug combination;
Selecting data with noise data removed and data with missing data filled in, inputting the data into an initial medicine recommendation model, and minimizing a loss function through an optimizer;
calculating F1 fraction; wherein F1 Score = ; Wherein P is the precision, and the characterization predicts that the sample is positive, and the actual ratio is positive; p= (true case)/(true case + false case); r recall, representing a positive proportion predicted in the sample; r= (true case)/(true case + false negative case); true examples, i.e., the number of samples for which the model predicts to be positive and is actually positive; true negative example: model prediction is negative and actually negative sample: false positive example: a sample where the model predicts positive but actually negative;
false negative example: samples where the model predicts negative but is actually positive;
And obtaining the confidence of the model through the F1 score.
7. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program for loading and executing by the processor the method of any of claims 1 to 5.
8. A computer readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which performs the method according to any of claims 1 to 5.
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| CN117112772A (en) * | 2023-08-22 | 2023-11-24 | 康键信息技术（深圳）有限公司 | Medicine searching method and device, storage medium and computer equipment |
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| WO2018235076A1 (en) * | 2017-06-21 | 2018-12-27 | Hadasit Medical Research Services And Development Ltd. | Method and system for predicting response to pharmacological treatment from eeg |
| CN111813946A (en) * | 2020-09-14 | 2020-10-23 | 平安国际智慧城市科技股份有限公司 | Medical information feedback method, device, equipment and readable storage medium |
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| CN117112772A (en) * | 2023-08-22 | 2023-11-24 | 康键信息技术（深圳）有限公司 | Medicine searching method and device, storage medium and computer equipment |
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