CN114595380A - Medicine recommendation method and device, electronic equipment and storage medium - Google Patents
Medicine recommendation method and device, electronic equipment and storage medium Download PDFInfo
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- CN114595380A CN114595380A CN202210135011.5A CN202210135011A CN114595380A CN 114595380 A CN114595380 A CN 114595380A CN 202210135011 A CN202210135011 A CN 202210135011A CN 114595380 A CN114595380 A CN 114595380A
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Abstract
The embodiment of the disclosure provides a medicine recommendation method and device, electronic equipment and a storage medium. The medicine recommendation method comprises the following steps: acquiring target information which is input by a user and is associated with a target prescription drug; determining a target non-prescription drug that can replace the target prescription drug based on the target information; recommending the target over-the-counter medication. According to the embodiment of the method and the device, the target non-prescription medicine capable of replacing the target prescription medicine can be automatically recommended based on the inquiry information of the medicine user, the processing process is simpler and more convenient, and the user experience can be improved.
Description
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to a method and an apparatus for recommending a medicine, an electronic device, and a storage medium.
Background
In order to meet the medicine purchasing requirements of users, various online medicine purchasing software is developed, the users can purchase medicines through the online medicine purchasing software, and the online medicine purchasing software provides great convenience for the users.
When on-line medicine purchasing software is used for purchasing prescription medicines for a medicine user, the demand of knowing non-prescription medicines capable of replacing the prescription medicines exists in certain scenes, so that how to recommend the non-prescription medicines for the user is a technical problem to be solved urgently at present.
Disclosure of Invention
In view of the foregoing problems, embodiments of the present disclosure provide a drug recommendation method, device, electronic device, and storage medium, which can automatically recommend a target non-prescription drug that can replace a target prescription drug.
According to a first aspect of embodiments of the present disclosure, there is provided a medication recommendation method including:
acquiring target information which is input by a user and is associated with a target prescription drug;
determining a target non-prescription drug that can replace the target prescription drug based on the target information;
recommending the target over-the-counter medication.
Optionally, the acquiring target information associated with a target prescription drug input by a user includes: in response to receiving a search request for the target prescription drug, acquiring an identification of the target prescription drug input by a user, and determining the identification of the target prescription drug as the target information.
Optionally, the determining, based on the target information, a target non-prescription drug that can replace the target prescription drug includes: acquiring a pre-constructed drug knowledge base, wherein the drug knowledge base comprises prescription drug identifications and non-prescription drug identifications corresponding to the prescription drug identifications; and inquiring the non-prescription medicine identification corresponding to the identification of the target prescription medicine from the medicine knowledge base, and determining the non-prescription medicine corresponding to the inquired non-prescription medicine identification as the target non-prescription medicine.
Optionally, the acquiring target information associated with a target prescription drug input by a user includes: acquiring inquiry information of the medication user input by the user in response to determining that the target prescription medicine selected for the medication user is interrupted from being purchased, and determining the inquiry information of the medication user as the target information.
Optionally, the determining, based on the target information, a target non-prescription drug that can replace the target prescription drug includes: acquiring a pre-constructed drug knowledge base, wherein the drug knowledge base comprises drug efficacy and non-prescription drug identification corresponding to the drug efficacy; extracting the disease information of the medication user from the inquiry information of the medication user, inquiring the efficacy of a first target medicine matched with the disease information from the medicine knowledge base, acquiring an over-the-counter medicine identifier corresponding to the efficacy of the first target medicine, and determining the over-the-counter medicine corresponding to the acquired over-the-counter medicine identifier as the target over-the-counter medicine.
Optionally, the determining, based on the target information, a target non-prescription drug that can replace the target prescription drug includes: acquiring a pre-constructed medicine knowledge base, wherein the medicine knowledge base comprises medicine efficacy, a prescription medicine identification corresponding to the medicine efficacy and an over-the-counter medicine identification corresponding to the medicine efficacy; extracting the disease information of the medicine user and the identification of the target prescription medicine from the inquiry information of the medicine user, inquiring a second target medicine efficacy which is matched with the disease information and contains the identification of the target prescription medicine from the medicine knowledge base, acquiring the identification of the non-prescription medicine corresponding to the second target medicine efficacy, and determining the non-prescription medicine corresponding to the acquired identification of the non-prescription medicine as the target non-prescription medicine.
Optionally, the determining, based on the target information, a target non-prescription drug that can replace the target prescription drug includes: acquiring a pre-trained drug recommendation model; the drug recommendation model is obtained based on inquiry information of a sample drug user and non-prescription drug training actually purchased by the sample drug user; taking the inquiry information of the medication user as the input of the medicine recommendation model to obtain a first output of the medicine recommendation model, wherein the first output is used for indicating non-prescription medicines corresponding to the inquiry information of the medication user; and determining the non-prescription medicine corresponding to the inquiry information of the medication user as the target non-prescription medicine.
Optionally, the drug recommendation model is trained by: taking the inquiry information of the sample medicine user as the input of the recommended model of the medicine to be trained, and obtaining a second output of the recommended model of the medicine to be trained, wherein the second output is used for indicating the non-prescription medicine corresponding to the inquiry information of the sample medicine user; and after the training is determined to be completed based on the second output of the drug recommendation model to be trained and the non-prescription drugs actually purchased by the sample medication user, taking the trained model as the drug recommendation model.
Optionally, before acquiring the inquiry information of the medication user input by the user in response to determining that the target prescription drug selected for the medication user is interrupted from being purchased, the method further comprises: determining that a target prescription drug selected for the medication user is interrupted for purchase in response to a no-prescription-information instruction triggered on the inquiry-information-filling page; alternatively, it is determined that the target prescription drug selected for the medication user is interrupted from being purchased in response to an exit page instruction triggered on the inquiry information filling page.
According to a second aspect of embodiments of the present disclosure, there is provided a medicine recommending apparatus including:
the acquisition module is used for acquiring target information which is input by a user and is related to a target prescription medicine;
a first determination module to determine a target non-prescription drug that can replace the target prescription drug based on the target information;
and the recommending module is used for recommending the target non-prescription medicine.
Optionally, the obtaining module includes: an identification obtaining unit, configured to obtain, in response to receiving a search request for the target prescription drug, an identification of the target prescription drug input by a user, and determine the identification of the target prescription drug as the target information.
Optionally, the first determining module includes: the fourth acquisition unit is used for acquiring a pre-constructed medicine knowledge base, wherein the medicine knowledge base comprises a prescription medicine identification and a non-prescription medicine identification corresponding to the prescription medicine identification; and the third query unit is used for querying an over-the-counter drug identifier corresponding to the identifier of the target prescription drug from the drug knowledge base and determining the over-the-counter drug corresponding to the queried over-the-counter drug identifier as the target over-the-counter drug.
Optionally, the obtaining module includes: an information acquisition unit, configured to acquire inquiry information of a medication user input by a user in response to determining that a target prescription drug selected for the medication user is interrupted from being purchased, and determine the inquiry information of the medication user as the target information.
Optionally, the first determining module includes: the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a pre-constructed medicine knowledge base, and the medicine knowledge base comprises medicine efficacy and non-prescription medicine identification corresponding to the medicine efficacy; the first query unit is used for extracting the disease information of the medication user from the inquiry information of the medication user, querying the efficacy of a first target drug matched with the disease information from the drug knowledge base, acquiring the non-prescription drug identification corresponding to the efficacy of the first target drug, and determining the non-prescription drug corresponding to the acquired non-prescription drug identification as the target non-prescription drug.
Optionally, the first determining module includes: the second acquisition unit is used for acquiring a pre-constructed medicine knowledge base, wherein the medicine knowledge base comprises medicine efficacy, prescription medicine identification corresponding to the medicine efficacy and non-prescription medicine identification corresponding to the medicine efficacy; the second query unit is used for extracting the disease information of the medication user and the identification of the target prescription medicine from the inquiry information of the medication user, querying a second target medicine which is matched with the disease information and contains the identification of the target prescription medicine in the corresponding identification of the prescription medicine from the medicine knowledge base, acquiring the identification of the non-prescription medicine corresponding to the efficacy of the second target medicine, and determining the non-prescription medicine corresponding to the acquired identification of the non-prescription medicine as the target non-prescription medicine.
Optionally, the first determining module includes: the third acquisition unit is used for acquiring a pre-trained medicine recommendation model; the drug recommendation model is obtained based on inquiry information of a sample drug user and non-prescription drug training actually purchased by the sample drug user; the prediction unit is used for taking the inquiry information of the medication user as the input of the medicine recommendation model to obtain a first output of the medicine recommendation model, and the first output is used for indicating non-prescription medicines corresponding to the inquiry information of the medication user; and the determining unit is used for determining the non-prescription medicine corresponding to the inquiry information of the medication user as the target non-prescription medicine.
Optionally, the drug recommendation model is trained through the following modules: the training module is used for taking the inquiry information of the sample medication user as the input of the recommended model of the drug to be trained to obtain a second output of the recommended model of the drug to be trained, and the second output is used for indicating the non-prescription drug corresponding to the inquiry information of the sample medication user; and the second determining module is used for determining that the training is finished based on the second output of the drug recommendation model to be trained and the non-prescription drugs actually purchased by the sample medication user, and then taking the model which is finished in training as the drug recommendation model.
Optionally, the apparatus further comprises: a third determination module, configured to determine that the target prescription drug selected for the medication user is interrupted for purchase in response to a no-prescription information instruction triggered on the inquiry information filling page; or, the fourth determination module is used for responding to an exit page instruction triggered on the inquiry information filling page and determining that the target prescription medicine selected by the medication user is interrupted to be purchased.
According to a third aspect of embodiments of the present disclosure, there is provided an electronic apparatus including: one or more processors; and one or more computer-readable storage media having instructions stored thereon; the instructions, when executed by the one or more processors, cause the processors to perform a drug recommendation method as any one of above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to execute the drug recommendation method as defined in any one of the above.
The embodiment of the disclosure provides a medicine recommendation method and device, electronic equipment and a storage medium. Target information which is input by a user and is associated with a target prescription medicine is obtained, a target non-prescription medicine which can replace the target prescription medicine is determined based on the target information, and the target non-prescription medicine is recommended. Therefore, in the embodiment of the disclosure, the target non-prescription drug capable of replacing the target prescription drug can be automatically recommended based on the target information associated with the target prescription drug, the processing process is simpler and more convenient, and the user experience can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments of the present disclosure will be briefly introduced below, and it is obvious that the drawings in the following description are only some drawings of the embodiments of the present disclosure, and other drawings can be obtained according to these drawings by those skilled in the art without inventive exercise.
FIG. 1 is a flow chart of a method of purchasing a prescription drug according to an embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating steps of a method for recommending a medicine according to an embodiment of the present disclosure.
FIG. 3 is a flowchart illustrating steps of another method for recommending medications according to an embodiment of the present disclosure.
Fig. 4 is a flowchart illustrating steps of a method for recommending a drug according to an embodiment of the present disclosure.
Fig. 5 is a schematic diagram of an inquiry information filling-in page according to an embodiment of the present disclosure.
Fig. 6 is a flowchart illustrating steps of a method for recommending a medicine according to an embodiment of the present disclosure.
Fig. 7 is a schematic diagram of a drug recommendation page of an embodiment of the present disclosure.
Fig. 8 is a flowchart illustrating steps of a method for recommending a medication according to an embodiment of the present disclosure.
Fig. 9 is a schematic diagram of a drug recommendation popup in an embodiment of the present disclosure.
Fig. 10 is a block diagram of a medicine recommendation device according to an embodiment of the present disclosure.
Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only a part of the embodiments of the present disclosure, and not all the embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The medicine recommending method of the embodiment of the disclosure can be applied to various clients supporting online medicine purchasing functions. The client may be an APP (application) or the like that supports an online medicine purchase function.
Referring to FIG. 1, a flow chart for purchasing a prescription drug is shown in accordance with an embodiment of the present disclosure. As shown in fig. 1, when a user purchases a prescription drug using a client supporting an online drug purchasing function, a process of purchasing the prescription drug may include the steps of: the user purchases prescription drugs on a drug purchasing page → enters an inquiry information filling page to fill inquiry information, and submits a medication application after filling is completed → an online doctor reviews the medication application, and an electronic prescription list is generated after the review is passed → a pharmacist reviews the electronic prescription list → a merchant performs an ordering process (including distribution, delivery and the like) after the review is passed.
Referring to fig. 2, a flowchart illustrating steps of a drug recommendation method according to an embodiment of the present disclosure is shown.
As shown in fig. 2, the drug recommendation method may include the following steps:
in step 201, target information associated with a target prescription drug, which is input by a user, is obtained.
In an alternative embodiment, when the user searches for the target prescription drug, the client may obtain target information input by the user and associated with the target prescription drug, and perform recommendation of the target non-prescription drug based on the target information.
In another alternative embodiment, the client may obtain target information associated with a target prescription drug entered by the user when a target prescription drug selected for the medication user is discontinued from being purchased, and make a recommendation for the target non-prescription drug based on the target information.
The client analyzes based on target information associated with the target prescription drug to determine a target non-prescription drug that can replace the target prescription drug. The specific process for determining the target over-the-counter medication will be described in detail in the examples below.
After the client determines the target non-prescription medicine, the target non-prescription medicine can be recommended so that the user can purchase the target non-prescription medicine.
It should be noted that the recommendations for over-the-counter drug information in the examples of the present application are in compliance with legal and regulatory requirements and in compliance with government regulatory guidelines.
According to the embodiment of the disclosure, the target non-prescription medicine capable of replacing the target prescription medicine can be automatically recommended based on the target information associated with the target prescription medicine, the processing process is simpler and more convenient, and the user experience can be improved.
Referring to fig. 3, a flow chart of steps of another method of drug recommendation of an embodiment of the present disclosure is shown.
As shown in fig. 3, the drug recommendation method may include the steps of:
When a user inputs an identifier of a target prescription drug in an input frame on a drug purchasing page of a client and triggers a search request carrying the identifier of the target prescription drug, or the user selects the target prescription drug on the drug purchasing page of the client and triggers a search request carrying the identifier of the target prescription drug, the client responds to the received search request for the target prescription drug, the identifier of the target prescription drug input by the user can be obtained through analysis from the search request, and the identifier of the target prescription drug is determined as the target information.
Illustratively, the identification may include, but is not limited to, a name, a number, and the like.
In an alternative embodiment, the drug knowledge base is pre-constructed, and the client queries for a target non-prescription drug that can replace the target prescription drug based on the drug knowledge base.
Optionally, the method includes analyzing based on an existing medicine database and a disease database, classifying medicines in the medicine database into prescription medicines and non-prescription medicines, analyzing the medicine efficacies of the prescription medicines and the non-prescription medicines, grouping the prescription medicines and the non-prescription medicines according to the medicine efficacies, dividing the prescription medicines and the non-prescription medicines with the same medicine efficacies into the same group, dividing the prescription medicines and the non-prescription medicines into two classes in the group, and then constructing a medicine knowledge base based on prescription medicine identifications and non-prescription medicine identifications corresponding to the prescription medicine identifications.
Illustratively, a prescription drug identifier and an over-the-counter drug identifier corresponding to the prescription drug identifier may be stored in the drug knowledge base in the form of a KV (Key-Value) data table or the like, where the prescription drug identifier may be stored as a Key and the over-the-counter drug identifier corresponding to the prescription drug identifier may be stored as a Value.
For such a drug knowledge base, determining a target non-prescription drug that can replace the target prescription drug based on the target information may include: acquiring a pre-constructed drug knowledge base; and inquiring the non-prescription drug identification corresponding to the identification of the target prescription drug from the drug knowledge base, and determining the non-prescription drug corresponding to the inquired non-prescription drug identification as the target non-prescription drug.
In an alternative embodiment, the client may generate and jump to a drug recommendation page after determining a target non-prescription drug that can replace the target prescription drug, and display the target non-prescription drug in the drug recommendation page.
In another alternative embodiment, the client may generate and pop up a drug recommendation popup window after determining a target non-prescription drug that can replace the target prescription drug, and display the target non-prescription drug in the drug recommendation popup window.
It should be noted that the recommendations for over-the-counter drug information in the examples of the present application are in compliance with legal and regulatory requirements and in compliance with government regulatory guidelines.
Referring to fig. 4, a flowchart illustrating steps of yet another method for drug recommendation according to an embodiment of the present disclosure is shown.
As shown in fig. 4, the drug recommendation method may include the following steps:
As shown in fig. 1, after the user purchases the prescription drug on the drug purchase page, the user will enter the inquiry information filling page to fill out the inquiry information of the drug user.
Referring to fig. 5, a schematic diagram of an inquiry information filling-in page of an embodiment of the present disclosure is shown. As shown in fig. 5, in the inquiry information filling page, basic information of the medication user (the medication user refers to a user who uses a medicine) needs to be set, a confirmed disease of the medication user who uses the medicine at this time is set, whether the medication user uses the medicine, whether there are no related contraindications, whether there are no allergic history and no adverse reaction, and prescription information of the medication user is supplemented.
However, if prescription information is not provided for the medication user (e.g., the medication user has not used the prescription, has no prescription information, is not willing to provide prescription information, etc.), the prescription medication will be discontinued from being purchased. And the client side responds to the fact that the target prescription medicine selected for the medicine user is determined to be purchased in an interrupted mode, acquires the inquiry information of the medicine user filled in the inquiry information filling page, and determines the inquiry information of the medicine user as the target information.
The client analyzes based on the inquiry information of the medication user to determine a target non-prescription drug that can replace the target prescription drug. The specific process for determining the target over-the-counter medication will be described in detail in the examples below.
At step 403, the target non-prescription drug is recommended.
After determining the target non-prescription medicine, the client can recommend the target non-prescription medicine so as to purchase the target non-prescription medicine for the medication user.
It should be noted that the recommendations for over-the-counter drug information in the examples of the present application are in compliance with legal and regulatory requirements and in compliance with government regulatory guidelines.
In the embodiment of the disclosure, under the condition that the target prescription medicine selected for the medication user is interrupted to be purchased, the target non-prescription medicine capable of replacing the target prescription medicine can be automatically recommended based on the inquiry information of the medication user, so that the paying inquiry operation is not needed, the processing process is simpler and more convenient, and the user experience can be improved.
Referring to fig. 6, a flowchart illustrating steps of yet another method for drug recommendation in accordance with an embodiment of the present disclosure is shown.
As shown in fig. 6, the drug recommendation method may include the steps of:
The user refers to a user who purchases a medicine, and the user who takes medicine refers to a user who uses the medicine. In practical application, the user and the medication user may be the same user or different users, which is not limited in this embodiment.
The user selects a target prescription drug for the medication user in a drug purchasing page of the client, and then submits a prescription drug purchasing order and carries out payment.
Illustratively, order information for a prescription drug order may include, but is not limited to: an identification (name, number, etc.) of the target prescription drug, a price of the target prescription drug, a type of the target prescription drug (the type is the prescription drug), a time of purchase of the target prescription drug, and so forth.
And after the client receives the prescription medicine purchase order, the client jumps to an inquiry information filling page if the type of the target prescription medicine in the order information is judged to be the prescription medicine.
Illustratively, the inquiry information of the medication user may include, but is not limited to: basic information of a medication user, a definite disease of the medication user, whether the medication user uses a target prescription medicine, whether related contraindications, whether allergy history and adverse reactions do not exist, an identification of the target prescription medicine, prescription information of the target prescription medicine, and the like.
If the user has prescription information of a target prescription drug, the prescription information of the target prescription drug needs to be uploaded in an inquiry information filling page.
Illustratively, the manner of uploading the prescription information may include, but is not limited to: selecting a picture of prescription information from existing pictures, taking a picture of prescription information, and so forth.
As shown in fig. 5, the inquiry information filling page provides a "no-place information" button that can be clicked by the user. If the user does not have prescription information of the target prescription drug, the user can click a 'no-prescription-information' button on the inquiry information filling page, so that a no-prescription-information instruction is triggered and received by the client.
In step 607, the client determines that the target prescription drug selected for the medication user is interrupted for purchase in response to the no-prescription information instruction triggered on the inquiry information filling page.
The client-side responds to the no-prescription information instruction triggered by the user on the inquiry information filling page, and then the target prescription medicine selected by the user for the medicine user can be determined to be interrupted to be purchased.
At step 608, the client determines a target non-prescription drug that can replace the target prescription drug based on the interrogation information of the medication user in response to determining that the target prescription drug selected for the medication user is discontinued from being purchased.
The client acquires inquiry information of the medication user, and determines a target non-prescription drug capable of replacing a target prescription drug based on the inquiry information of the medication user.
In an alternative embodiment, the drug knowledge base is pre-constructed, and the client queries for a target non-prescription drug that can replace the target prescription drug based on the drug knowledge base.
Optionally, the method includes analyzing based on an existing medicine database and a disease database, classifying medicines in the medicine database into prescription medicines and non-prescription medicines, analyzing the medicine efficacy of each non-prescription medicine, grouping the non-prescription medicines according to the medicine efficacy, dividing the non-prescription medicines with the same medicine efficacy into the same group, and then constructing a medicine knowledge base based on the medicine efficacy and non-prescription medicine identifications corresponding to the medicine efficacy.
Illustratively, drug efficacy may include, but is not limited to: the disease being treated, the age group for which it is applicable, etc. For example, the age bracket of the medication user may be set as follows: infants between 0 (newborn) and 6 years old, children between 7 and 12 years old, teenagers between 13 and 17 years old, young adults between 18 and 45 years old, middle-aged adults between 46 and 69 years old, older adults over 69 years old, and so on.
Illustratively, the drug efficacy and the non-prescription drug corresponding to the drug efficacy may be stored in the drug knowledge base in the form of a KV (Key-Value) data table or the like, where the drug efficacy may be stored as a Key and the non-prescription drug identifier corresponding to the drug efficacy is stored as a Value.
For such a drug knowledge base, the process of determining a target non-prescription drug that can replace the target prescription drug based on the inquiry information of the medication user may include: acquiring a pre-constructed medicine knowledge base comprising medicine efficacy and non-prescription medicine identification corresponding to the medicine efficacy; extracting the disease information of the medication user from the inquiry information of the medication user, inquiring the efficacy of a first target medicine matched with the disease information from the medicine knowledge base, acquiring an over-the-counter medicine identifier corresponding to the efficacy of the first target medicine, and determining the over-the-counter medicine corresponding to the acquired over-the-counter medicine identifier as the target over-the-counter medicine.
Illustratively, the medical condition information of the medication user extracted from the inquiry information of the medication user may include, but is not limited to: the medication user is taking the medication this time to determine the disease, the age of the medication user, and so on.
In the process of inquiring the efficacy of the first target medicine, matching the efficacy of each medicine stored in a medicine knowledge base with the disease information of the medicine user, and if the disease treated in the efficacy of the medicine is the same as the confirmed disease of the medicine user who takes the medicine this time and the applicable age group in the efficacy of the medicine comprises the age of the medicine user, determining that the efficacy of the medicine is the efficacy of the first target medicine matched with the disease information of the medicine user.
Optionally, the method includes analyzing based on an existing medicine database and a disease database, classifying medicines in the medicine database into prescription medicines and non-prescription medicines, analyzing the medicine efficacies of the prescription medicines and the non-prescription medicines, grouping the prescription medicines and the non-prescription medicines according to the medicine efficacies, dividing the prescription medicines and the non-prescription medicines with the same medicine efficacies into the same group, dividing the prescription medicines and the non-prescription medicines into two classes in the group, and then constructing a medicine knowledge base based on the medicine efficacies, the prescription medicine identifications corresponding to the medicine efficacies and the non-prescription medicine identifications corresponding to the medicine efficacies.
Illustratively, the drug efficacy, the prescription drug identity corresponding to the drug efficacy, and the non-prescription drug identity corresponding to the drug efficacy may be stored in the drug knowledge base in the form of a KV (Key-Value) data table or the like, where the drug efficacy may be stored as a Key (Key), and the prescription drug identity corresponding to the drug efficacy and the non-prescription drug identity corresponding to the drug efficacy are stored as a Value (Value).
For such a drug knowledge base, the process of determining a target non-prescription drug that can replace the target prescription drug based on the inquiry information of the medication user may include: acquiring a pre-constructed medicine knowledge base comprising medicine efficacy, a prescription medicine identification corresponding to the medicine efficacy and an over-the-counter medicine identification corresponding to the medicine efficacy; extracting the disease information of the medicine user and the identification of the target prescription medicine from the inquiry information of the medicine user, inquiring a second target medicine efficacy which is matched with the disease information and contains the identification of the target prescription medicine from the medicine knowledge base, acquiring an over-the-counter medicine identification corresponding to the second target medicine efficacy, and determining the over-the-counter medicine corresponding to the acquired over-the-counter medicine identification as the target over-the-counter medicine.
In the process of inquiring the efficacy of a second target drug, firstly, matching the efficacy of each drug stored in a drug knowledge base with the disease information of the drug user, and if the disease to be treated in the efficacy of the drug is the same as the confirmed disease of the drug user who takes the drug this time and the applicable age group in the efficacy of the drug contains the age of the drug user, determining that the efficacy of the drug is the efficacy of the drug matched with the disease information of the drug user; then, aiming at the drug efficacy matched with the disease information of the medication user, judging whether the prescription drug identification corresponding to the drug efficacy contains the identification of the target prescription drug; and finally, if the medicine is judged to be contained, determining the medicine efficacy matched with the disease information of the medicine user as a second target medicine efficacy matched with the disease information and containing the target prescription medicine identification in the corresponding prescription medicine identification.
In another alternative embodiment, the drug recommendation model is pre-trained, and the client determines a target non-prescription drug that can replace the target prescription drug based on the drug recommendation model.
Optionally, the training process of the drug recommendation model includes the following steps a 1-a 5:
step a1, obtaining the inquiry information of the sample medication user and the non-prescription drugs actually purchased by the sample medication user.
Sample data can be constructed based on massive historical inquiry information, historical medicine purchasing information and the like. Wherein the sample data comprises inquiry information of a sample medication user and non-prescription drugs actually purchased by the sample medication user.
Illustratively, the over-the-counter medication actually purchased by the sample medication user may contain one or more. The inquiry information of the sample medication user may include, but is not limited to: basic information of a sample medication user, a definite disease when the sample medication user purchases an over-the-counter medicine, whether the sample medication user has no related contraindications, whether the sample medication user has no allergic history or adverse reaction, and the like.
Step a2, taking the inquiry information of the sample medication user as the input of the drug recommendation model to be trained, and obtaining a second output of the drug recommendation model to be trained.
In implementation, any applicable AI (Artificial Intelligence) model can be used as the drug recommendation model to be trained, and the drug recommendation model to be trained has a multi-objective classification function or a multi-objective probability prediction function. Illustratively, the drug recommendation model to be trained may include, but is not limited to: RNN (Recurrent Neural Network), SVM (Support Vector Machine), LSTM (Long Short-Term Memory) model, naive bayes model, logistic regression model, decision tree model, and the like.
And the second output is used for indicating non-prescription drugs corresponding to the inquiry information of the sample medication user. Illustratively, the second output may include the predicted probability of each over-the-counter drug, and when the predicted probability of a certain over-the-counter drug exceeds a preset threshold, it may be determined that the over-the-counter drug is the over-the-counter drug corresponding to the inquiry information of the sample medication user.
Step a3, determining whether training is completed based on the second output of the drug recommendation model to be trained and the non-prescription drugs actually purchased by the sample medication user. If yes, executing step a 4; if not, go to step a 5.
And calculating a loss function of the drug recommendation model to be trained according to the second output of the drug recommendation model to be trained and the non-prescription drugs actually purchased by the sample medication user.
In implementation, any applicable loss function may be used as the loss function of the drug recommendation model to be trained. Illustratively, the loss function of the drug recommendation model to be trained may include, but is not limited to: cross entropy Loss (cross entropy Loss), 0-1 Loss (zero-one Loss), logarithmic Loss (Logistic Loss), Hinge Loss (hind Loss), Exponential Loss (explicit Loss), square Loss (Squared Loss), Absolute Loss (Absolute Loss), Huber Loss (Huber Loss), and the like.
Step a4, in response to determining that training is complete, taking the trained model as the drug recommendation model.
Step a5, in response to determining that training is not complete, adjusting the model parameters to continue training.
The process of determining a target non-prescription drug capable of replacing the target prescription drug based on the inquiry information of the medication user using the trained drug recommendation model may include: acquiring a pre-trained drug recommendation model; taking the inquiry information of the medication user as the input of the medicine recommendation model to obtain a first output of the medicine recommendation model, wherein the first output is used for indicating non-prescription medicines corresponding to the inquiry information of the medication user; and determining the non-prescription medicine corresponding to the inquiry information of the medicine user as the target non-prescription medicine.
Illustratively, the first output may include a predicted probability of each over-the-counter drug, and when the predicted probability of a certain over-the-counter drug exceeds a preset threshold, it may be determined that the over-the-counter drug is the over-the-counter drug corresponding to the inquiry information of the medication user.
When the user clicks a 'no prescription information' button on the inquiry information filling page, the client determines that the target prescription medicine selected for the medication user is interrupted to be purchased, and after determining the target non-prescription medicine capable of replacing the target prescription medicine based on the inquiry information of the medication user, the client jumps to a medicine recommendation page and displays the target non-prescription medicine in the medicine recommendation page.
In an optional embodiment, after determining the target non-prescription drugs capable of replacing the target prescription drugs, the client may further perform a sorting operation on the target non-prescription drugs, and then select at least one target non-prescription drug sorted in the top for display.
Any suitable sorting method may be selected for the specific process of the sorting operation. Illustratively, the sorting may include, but is not limited to: sorting by using a recommended sorting model, sorting based on the similarity between the target non-prescription drugs and the target non-prescription drugs, random sorting, and the like. Wherein, the similarity can be obtained by weighting and calculating the parameters of drug efficacy, drug components, drug contraindications, adverse reactions, in-store sales, manufacturers and the like.
Referring to fig. 7, a schematic diagram of a drug recommendation page of an embodiment of the present disclosure is shown. As shown in fig. 7, in the medicine recommendation page, target non-prescription medicines such as medicine 1 and medicine 2 may be displayed, and the on-sale stores and prices of the target non-prescription medicines, such as store 1 and price, store 2 and price, store 3 and price, and the like in fig. 7, may be displayed. It should be noted that the medicine recommendation page shown in fig. 7 is only an exemplary page, and other information may be included in the medicine recommendation page in actual application, which is not limited in this embodiment.
At step 610, the user selects a target non-prescription drug in the drug recommendation page and submits a non-prescription drug order for purchase.
The user may select a target non-prescription drug to purchase on the drug recommendation page, and then submit the non-prescription drug purchase order and make payment.
Illustratively, order information for an over-the-counter drug order may include, but is not limited to: an identification (name, number, etc.) of the target over-the-counter drug, a price of the target over-the-counter drug, a type of the target over-the-counter drug (type is over-the-counter drug), a time of purchase of the target over-the-counter drug, and so forth.
In step 611, the client confirms that the order is complete.
After the user uploads the prescription information of the target prescription drug in step 605, the client confirms that the ordering of the drug is completed.
In step 610, after the medicine purchasing user selects the target non-prescription medicine in the medicine recommendation page and submits the non-prescription medicine purchasing order, the client confirms that the medicine ordering is completed.
Referring to fig. 8, a flowchart illustrating steps of yet another method for drug recommendation in accordance with an embodiment of the present disclosure is shown.
As shown in fig. 8, the drug recommendation method may include the steps of:
at step 801, a user selects a target prescription drug for a medication user and submits a prescription drug order for purchase.
At step 804, the user is presented with prescription information for the target prescription drug. If yes, go to step 805; if not, go to step 806.
If the user does not provide prescription information for the target prescription drug, the interrogation information filling page may be exited, thereby triggering an exit page instruction, which may be received by the client.
In step 807, the client determines that the target prescription drug selected for the medication user is interrupted for purchase in response to an exit page instruction triggered on the inquiry information filling page.
And the client responds to an exit page instruction triggered by the user on the inquiry information filling page, so that the target prescription medicine selected by the user for the medicine user can be determined to be interrupted for purchase.
At step 808, the client determines a target non-prescription drug that can replace the target prescription drug based on the interrogation information of the administering user in response to determining that the target prescription drug selected for the administering user is discontinued from being purchased.
And step 809, popping up a medicine recommendation pop-up window by the client, and displaying the target non-prescription medicine in the medicine recommendation pop-up window.
And under the condition that the user exits the inquiry information filling page, the client determines that the target prescription medicine selected for the medication user is interrupted to be purchased, and pops up a medicine recommendation popup window after determining a target non-prescription medicine capable of replacing the target prescription medicine based on the inquiry information of the medication user, and displays the target non-prescription medicine in the medicine recommendation popup window.
Referring to fig. 9, a schematic diagram of a medicine recommendation popup of an embodiment of the present disclosure is shown. As shown in fig. 9, in the medicine recommendation pop-up window, target non-prescription medicines such as medicine 1, medicine 2, medicine 3, etc. may be displayed, and a "go to purchase" button may be displayed. The user may select a target non-prescription drug to purchase and click the "go to buy" button to purchase the selected target non-prescription drug. It should be noted that the medicine recommendation pop-up window shown in fig. 9 is only an exemplary pop-up window, and other information may be included in the medicine recommendation pop-up window in practical application, which is not limited in this embodiment.
The user may select a target non-prescription drug to purchase in the drug recommendation pop, and then submit the non-prescription drug order and make payment.
After the user uploads the prescription information of the target prescription drug in step 805 above, the client confirms that ordering the drug is complete.
In step 810, after the user selects the target non-prescription drug in the drug recommendation pop-up window and submits the non-prescription drug order, the client confirms that the ordering of the drug is completed.
For the parts of the embodiment shown in fig. 8 similar to those of the embodiment shown in fig. 6, reference is made to the related description of the embodiment shown in fig. 6, and the embodiment will not be discussed in detail here.
Referring to fig. 10, a block diagram of a medicine recommendation device according to an embodiment of the present disclosure is shown.
As shown in fig. 10, the medicine recommending apparatus may include the following modules:
an obtaining module 1001, configured to obtain an over-the-counter drug identifier corresponding to an efficacy of the second target drug, and identify the obtained over-the-counter drug;
a first determining module 1002 for determining a target non-prescription drug that can replace the target prescription drug based on the target information;
a recommending module 1003, configured to recommend the target non-prescription drug.
Optionally, the obtaining module 1001 includes: an identification obtaining unit, configured to obtain, in response to receiving a search request for the target prescription drug, an identification of the target prescription drug input by a user, and determine the identification of the target prescription drug as the target information.
Optionally, the first determining module 1002 includes: the fourth acquisition unit is used for acquiring a pre-constructed medicine knowledge base, wherein the medicine knowledge base comprises a prescription medicine identification and an over-the-counter medicine identification corresponding to the prescription medicine identification; and the third query unit is used for querying an over-the-counter drug identifier corresponding to the identifier of the target prescription drug from the drug knowledge base and determining the over-the-counter drug corresponding to the queried over-the-counter drug identifier as the target over-the-counter drug.
Optionally, the obtaining module 1001 includes: an information acquisition unit, configured to acquire inquiry information of a medication user input by a user in response to determining that a target prescription drug selected for the medication user is interrupted from being purchased, and determine the inquiry information of the medication user as the target information.
Optionally, the first determining module 1002 includes: the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a pre-constructed medicine knowledge base, and the medicine knowledge base comprises medicine efficacy and non-prescription medicine identification corresponding to the medicine efficacy; the first query unit is used for extracting the disease information of the medication user from the inquiry information of the medication user, querying the efficacy of a first target drug matched with the disease information from the drug knowledge base, acquiring the non-prescription drug identification corresponding to the efficacy of the first target drug, and determining the non-prescription drug corresponding to the acquired non-prescription drug identification as the target non-prescription drug.
Optionally, the first determining module 1002 includes: the second acquisition unit is used for acquiring a pre-constructed medicine knowledge base, wherein the medicine knowledge base comprises medicine efficacy, a prescription medicine identification corresponding to the medicine efficacy and an over-the-counter medicine identification corresponding to the medicine efficacy; the second query unit is used for extracting the disease information of the medication user and the identification of the target prescription medicine from the inquiry information of the medication user, querying a second target medicine which is matched with the disease information and contains the identification of the target prescription medicine in the corresponding identification of the prescription medicine from the medicine knowledge base, acquiring the identification of the non-prescription medicine corresponding to the efficacy of the second target medicine, and determining the non-prescription medicine corresponding to the acquired identification of the non-prescription medicine as the target non-prescription medicine.
Optionally, the first determining module 1002 includes: the third acquisition unit is used for acquiring a pre-trained medicine recommendation model; the drug recommendation model is obtained based on inquiry information of a sample drug user and non-prescription drug training actually purchased by the sample drug user; the prediction unit is used for taking the inquiry information of the medication user as the input of the medicine recommendation model to obtain a first output of the medicine recommendation model, and the first output is used for indicating non-prescription medicines corresponding to the inquiry information of the medication user; and the determining unit is used for determining the non-prescription medicine corresponding to the inquiry information of the medication user as the target non-prescription medicine.
Optionally, the drug recommendation model is trained through the following modules: the training module is used for taking the inquiry information of the sample medication user as the input of the recommended model of the drug to be trained to obtain a second output of the recommended model of the drug to be trained, and the second output is used for indicating the non-prescription drug corresponding to the inquiry information of the sample medication user; and the second determining module is used for determining that the training is finished based on the second output of the drug recommendation model to be trained and the non-prescription drugs actually purchased by the sample medication user, and then taking the trained model as the drug recommendation model.
Optionally, the apparatus further comprises: a third determination module, configured to determine that the target prescription drug selected for the medication user is interrupted for purchase in response to a no-prescription information instruction triggered on the inquiry information filling page; or, the fourth determination module is used for responding to an exit page instruction triggered on the inquiry information filling page and determining that the target prescription medicine selected by the medication user is interrupted to be purchased.
According to the embodiment of the disclosure, the target non-prescription medicine capable of replacing the target prescription medicine can be automatically recommended based on the target information associated with the target prescription medicine, the processing process is simpler and more convenient, and the user experience can be improved.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
In an embodiment of the present disclosure, an electronic device is also provided. The electronic device may include one or more processors, and one or more computer-readable storage media having instructions, such as an application program, stored thereon. The instructions, when executed by the one or more processors, cause the processors to perform a method of drug recommendation as in any of the embodiments above.
Referring to fig. 11, a schematic diagram of an electronic device structure according to an embodiment of the present disclosure is shown. As shown in fig. 11, the electronic device includes a processor 1101, a communication interface 1102, a memory 1103, and a communication bus 1104. The processor 1101, the communication interface 1102 and the memory 1103 complete communication with each other through the communication bus 1104.
A memory 1103 for storing a computer program.
The processor 1101 is configured to implement the medicine recommending method according to any of the embodiments described above when executing the program stored in the memory 1103.
The communication interface 1102 is used for communication between the electronic apparatus and other apparatuses.
The communication bus 1104 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The aforementioned processor 1101 may include, but is not limited to: a Central Processing Unit (CPU), a Network Processor (NP), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and so on.
The aforementioned memory 1103 may include, but is not limited to: read Only Memory (ROM), Random Access Memory (RAM), Compact Disc Read Only Memory (CD-ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), hard disk, floppy disk, flash Memory, and the like.
In an embodiment of the present disclosure, there is also provided a non-transitory computer readable storage medium having stored thereon a computer program executable by a processor of an electronic device, the computer program, when executed by the processor, causing the processor to perform a drug recommendation method as described in any of the embodiments above.
It should be noted that all actions of acquiring signals, information or data in the present application are performed under the premise of complying with the corresponding data protection regulation policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present disclosure are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the embodiments of the present disclosure as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the embodiments of the present disclosure.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the embodiments of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, claimed embodiments of the disclosure require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this disclosure.
Those skilled in the art will appreciate that the modules in the devices in an embodiment may be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
The various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be understood by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a motion picture generating device according to an embodiment of the present disclosure. Embodiments of the present disclosure may also be implemented as an apparatus or device program for performing a portion or all of the methods described herein. Such programs implementing embodiments of the present disclosure may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit embodiments of the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above description is only a specific implementation of the embodiments of the present disclosure, but the scope of the embodiments of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present disclosure, and all the changes or substitutions should be covered by the scope of the embodiments of the present disclosure.
Claims (12)
1. A method for recommending medications, comprising:
acquiring target information which is input by a user and is associated with a target prescription drug;
determining a target non-prescription drug that can replace the target prescription drug based on the target information;
recommending the target over-the-counter medication.
2. The method of claim 1, wherein obtaining target information input by a user associated with a target prescription drug comprises:
in response to receiving a search request for the target prescription drug, acquiring an identification of the target prescription drug input by a user, and determining the identification of the target prescription drug as the target information.
3. The method of claim 2, wherein the determining a target non-prescription drug that can replace the target prescription drug based on the target information comprises: acquiring a pre-constructed drug knowledge base, wherein the drug knowledge base comprises prescription drug identifications and non-prescription drug identifications corresponding to the prescription drug identifications;
and inquiring the non-prescription medicine identification corresponding to the identification of the target prescription medicine from the medicine knowledge base, and determining the non-prescription medicine corresponding to the inquired non-prescription medicine identification as the target non-prescription medicine.
4. The method of claim 1, wherein obtaining target information associated with a target prescription drug entered by a user comprises:
acquiring inquiry information of the medication user input by the user in response to determining that the target prescription medicine selected for the medication user is interrupted from being purchased, and determining the inquiry information of the medication user as the target information.
5. The method of claim 4, wherein determining a target non-prescription drug that can replace the target prescription drug based on the target information comprises:
acquiring a pre-constructed drug knowledge base, wherein the drug knowledge base comprises drug efficacy and non-prescription drug identification corresponding to the drug efficacy;
extracting the disease information of the medication user from the inquiry information of the medication user, inquiring the efficacy of a first target medicine matched with the disease information from the medicine knowledge base, acquiring an over-the-counter medicine identifier corresponding to the efficacy of the first target medicine, and determining the over-the-counter medicine corresponding to the acquired over-the-counter medicine identifier as the target over-the-counter medicine.
6. The method of claim 4, wherein determining a target non-prescription drug that can replace the target prescription drug based on the target information comprises:
acquiring a pre-constructed medicine knowledge base, wherein the medicine knowledge base comprises medicine efficacy, a prescription medicine identification corresponding to the medicine efficacy and an over-the-counter medicine identification corresponding to the medicine efficacy;
extracting the disease information of the medicine user and the identification of the target prescription medicine from the inquiry information of the medicine user, inquiring a second target medicine efficacy which is matched with the disease information and contains the identification of the target prescription medicine from the medicine knowledge base, acquiring an over-the-counter medicine identification corresponding to the second target medicine efficacy, and determining the over-the-counter medicine corresponding to the acquired over-the-counter medicine identification as the target over-the-counter medicine.
7. The method of claim 4, wherein determining a target non-prescription drug that can replace the target prescription drug based on the target information comprises:
acquiring a pre-trained drug recommendation model; the drug recommendation model is obtained based on inquiry information of a sample drug user and non-prescription drug training actually purchased by the sample drug user;
taking the inquiry information of the medication user as the input of the medicine recommendation model to obtain a first output of the medicine recommendation model, wherein the first output is used for indicating non-prescription medicines corresponding to the inquiry information of the medication user;
and determining the non-prescription medicine corresponding to the inquiry information of the medication user as the target non-prescription medicine.
8. The method of claim 7, wherein the drug recommendation model is trained by:
taking the inquiry information of the sample medicine user as the input of the recommended model of the medicine to be trained, and obtaining a second output of the recommended model of the medicine to be trained, wherein the second output is used for indicating the non-prescription medicine corresponding to the inquiry information of the sample medicine user;
and after the training is determined to be completed based on the second output of the drug recommendation model to be trained and the non-prescription drugs actually purchased by the sample medication user, taking the trained model as the drug recommendation model.
9. The method of claim 4, further comprising, prior to obtaining user-entered interrogation information for the medication user in response to determining that the target prescription drug selected for the medication user was discontinued from being purchased,:
determining that a target prescription drug selected for the medication user is interrupted for purchase in response to a no-prescription-information instruction triggered on the inquiry-information-filling page;
alternatively, the first and second electrodes may be,
in response to an exit page instruction triggered on the inquiry information filling page, it is determined that the target prescription drug selected for the medication user is interrupted from being purchased.
10. A medication recommendation device, comprising:
the acquisition module is used for acquiring target information which is input by a user and is related to a target prescription medicine;
a first determination module to determine a target non-prescription drug that can replace the target prescription drug based on the target information;
and the recommending module is used for recommending the target non-prescription medicine.
11. An electronic device, comprising:
one or more processors; and
one or more computer-readable storage media having instructions stored thereon;
the instructions, when executed by the one or more processors, cause the processors to perform the drug recommendation method of any of claims 1-9.
12. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, causes the processor to execute the drug recommendation method of any one of claims 1 to 9.
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| CN114595380B (en) | 2024-03-26 |
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