Medical data sending method and device, equipment, medium and program product

Abstract

The embodiment of the invention discloses a medical data sending method and device, equipment, a medium and a program product. A specific embodiment of the method comprises the following steps: acquiring patient information corresponding to a target patient from a patient information base; determining a treatment scheme set corresponding to the chief complaint disease by using a pre-constructed knowledge graph; in response to determining that the target patient has the drug being taken, for each treatment scheme in the treatment scheme set, determining whether the treatment scheme conflicts with the patient behavior and the drug being taken, and obtaining conflict information; screening out at least one first available treatment scheme from the treatment scheme set according to the conflict information set; and sending treatment scheme information corresponding to the at least one first available treatment scheme to a corresponding terminal. The implementation mode is related to intelligent medical treatment, and on the premise that no treatment conflict exists between the target patient and the target patient, accurate query of treatment data can be assisted for disease treatment reference.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of medical big data technology, and specifically to a medical data sending method, apparatus, device, medium and program product. Background Art

[0002] At present, with the continuous development of artificial intelligence and big data, the application of artificial intelligence technology and big data technology to the medical field to assist doctors in daily medical diagnosis has become more and more widespread. For the confirmation of medical data, the usual method is: doctors manually query the patient's corresponding treatment data from the patient information database and disease diagnosis materials to inform the patient of the treatment data.

[0003] However, the inventors have found that when the above method is adopted, the following technical problems often occur:

[0004] Manual query of treatment data (eg, treatment plans) is inefficient and the query content may be relatively one-sided, making it difficult to generate accurate treatment data for the target patient's condition.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose medical data sending methods, devices, equipment, media and program products to solve the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a medical data sending method, comprising: acquiring patient information corresponding to a target patient from a patient information database, wherein the patient information includes: chief complaint symptoms and patient behavior; determining a treatment plan set corresponding to the chief complaint symptoms using a pre-constructed knowledge graph, wherein the knowledge graph characterizes the correspondence between symptoms, treatment plans and drugs; in response to determining that the target patient is currently taking medications, for each treatment plan in the treatment plan set, determining whether the treatment plan conflicts with the patient behavior and the medications, and obtaining conflict information; based on the obtained conflict information set, screening out at least one first available treatment plan from the treatment plan set; and sending the treatment plan information corresponding to the at least one first available treatment plan to a terminal corresponding to a first symptom treatment object.

[0009] Optionally, the conflict information includes: drug efficacy conflict information and behavior conflict information; and the determination of whether there is a conflict between the treatment plan and the patient behavior and the medications being taken, to obtain conflict information, includes: determining drug efficacy conflict information between the drug set corresponding to the treatment plan and the medications being taken, wherein the drug set is determined based on the knowledge graph; determining behavior conflict information between the treatment behavior corresponding to the treatment plan and the patient behavior.

[0010] Optionally, the above-mentioned screening out at least one first available treatment plan from the above-mentioned treatment plan set based on the obtained conflict information set includes: removing from the above-mentioned treatment plan set treatment plans whose corresponding drug efficacy conflict information indicates the existence of drug conflicts and / or whose corresponding behavior conflict information indicates the existence of behavior conflicts, to obtain a first remaining treatment plan set; and screening out at least one first available treatment plan from the above-mentioned first remaining treatment plan set.

[0011] Optionally, the patient information further includes: gastrointestinal condition information and liver and kidney metabolism information; and the screening out at least one first available treatment plan from the first set of remaining treatment plans includes: removing the first remaining treatment plans that affect gastrointestinal digestion and / or liver metabolism from the first set of remaining treatment plans based on the gastrointestinal condition information and the liver and kidney metabolism information, to obtain at least one first available treatment plan.

[0012] Optionally, the above-mentioned determination of the drug efficacy conflict information between the drug set corresponding to the above-mentioned treatment plan and the above-mentioned drugs being taken includes: for each drug in the above-mentioned drug set, executing the following generation steps: determining the drug ingredient information corresponding to the above-mentioned drug as the first drug ingredient information; determining the drug ingredient information corresponding to the above-mentioned drug being taken as the second drug ingredient information; using a pre-trained drug efficacy conflict information generation model to generate drug efficacy conflict information for the above-mentioned first drug ingredient information and the above-mentioned second drug ingredient information; generating the above-mentioned drug efficacy conflict information based on the obtained drug efficacy conflict information set.

[0013] Optionally, the method further includes: in response to determining that there is no corresponding medication being taken by the target patient, determining, for each treatment plan in the treatment plan set, behavior conflict information between the treatment behavior corresponding to the treatment plan and the patient behavior; removing treatment plans with behavior conflict information indicating that there is a behavior conflict from the treatment plan set to obtain a second set of remaining treatment plans; generating at least one second available treatment plan for the target patient based on the second set of remaining treatment plans; and sending the treatment plan information corresponding to the at least one second available treatment plan to a terminal corresponding to a second disease treatment object.

[0014] Optionally, after determining the treatment plan set corresponding to the chief complaint by using the pre-constructed knowledge graph, the method further includes: in response to determining that the treatment plan set is empty and that the target patient does not have corresponding medications, sending the chief complaint and the patient information to a terminal corresponding to a third disease treatment object; in response to determining that the treatment plan set is empty and that the target patient does have corresponding medications, sending the chief complaint, the drug information corresponding to the drugs being taken, and the patient information to a terminal corresponding to the third disease treatment object.

[0015] In a second aspect, some embodiments of the present disclosure provide a medical data sending device, comprising: an acquisition unit, configured to acquire patient information corresponding to a target patient from a patient information database, wherein the patient information includes: chief complaint symptoms and patient behavior; a first determination unit, configured to determine a treatment plan set corresponding to the chief complaint symptoms using a pre-built knowledge graph, wherein the knowledge graph characterizes the correspondence between symptoms, treatment plans and drugs; a second determination unit, configured to, in response to determining that the target patient is taking drugs, determine, for each treatment plan in the treatment plan set, whether there is a conflict between the treatment plan and the patient behavior and the drugs being taken, and obtain conflict information; a screening unit, configured to screen out at least one first available treatment plan from the treatment plan set based on the obtained conflict information set; and a sending unit, configured to send the treatment plan information corresponding to the at least one first available treatment plan to a terminal corresponding to the first symptom treatment object.

[0016] Optionally, the above-mentioned conflict information includes: drug efficacy conflict information and behavior conflict information; and the second determination unit can be configured to: determine the drug efficacy conflict information between the drug set corresponding to the above-mentioned treatment plan and the above-mentioned drugs being taken, wherein the above-mentioned drug set is determined based on the above-mentioned knowledge graph; determine the behavior conflict information between the treatment behavior corresponding to the above-mentioned treatment plan and the above-mentioned patient behavior.

[0017] Optionally, the screening unit can be configured to: remove the treatment plans whose corresponding drug efficacy conflict information indicates the existence of drug conflicts and / or whose corresponding behavior conflict information indicates the existence of behavior conflicts from the above-mentioned treatment plan set to obtain a first set of remaining treatment plans; and screen out at least one first available treatment plan from the above-mentioned first set of remaining treatment plans.

[0018] Optionally, the above-mentioned patient information also includes: gastrointestinal condition information and liver and kidney metabolism information; and the screening unit can be configured to: remove the first remaining treatment plan that affects gastrointestinal digestion and / or liver metabolism from the above-mentioned first remaining treatment plan set based on the above-mentioned gastrointestinal condition information and the above-mentioned liver and kidney metabolism information, and obtain at least one first available treatment plan.

[0019] Optionally, the second determination unit can be configured to: for each drug in the above-mentioned drug set, perform the following generation steps: determine the drug ingredient information corresponding to the above-mentioned drug as the first drug ingredient information; determine the drug ingredient information corresponding to the above-mentioned drug in use as the second drug ingredient information; use a pre-trained drug efficacy conflict information generation model to generate drug efficacy conflict information for the above-mentioned first drug ingredient information and the above-mentioned second drug ingredient information; generate the above-mentioned drug efficacy conflict information based on the obtained drug efficacy conflict information set.

[0020] Optionally, the device also includes: in response to determining that the target patient does not have corresponding medications being taken, determining, for each treatment plan in the treatment plan set, behavioral conflict information between the treatment behavior corresponding to the treatment plan and the patient behavior; removing treatment plans whose behavioral conflict information indicates that there is a behavioral conflict from the treatment plan set to obtain a second set of remaining treatment plans; generating at least one second available treatment plan for the target patient based on the second set of remaining treatment plans; and sending the treatment plan information corresponding to the at least one second available treatment plan to a terminal corresponding to the second disease treatment object.

[0021] Optionally, the device also includes: in response to determining that the above-mentioned treatment plan set is empty and determining that the above-mentioned target patient has no corresponding medications being taken, sending the above-mentioned chief complaint and the above-mentioned patient information to a terminal corresponding to a third disease treatment object; in response to determining that the above-mentioned treatment plan set is empty and determining that the above-mentioned target patient has corresponding medications being taken, sending the above-mentioned chief complaint, the drug information corresponding to the above-mentioned drugs being taken and the above-mentioned patient information to a terminal corresponding to the third disease treatment object.

[0022] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0023] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0024] In a fifth aspect, some embodiments of the present disclosure provide a computer program product, including a computer program, which implements the method described in any implementation manner in the above-mentioned first aspect when executed by a processor.

[0025] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the medical data sending method of some embodiments of the present disclosure, under the premise that there is no treatment conflict with the target patient, it can assist in the accurate query of treatment data for reference of disease treatment. Specifically, the reason for the inaccuracy of the relevant treatment data query is that the efficiency of artificial query of treatment data (for example, treatment plan) is low and the query content may be relatively one-sided, which makes it difficult to generate accurate treatment data for the patient condition of the target patient. Based on this, the medical data sending method of some embodiments of the present disclosure first obtains the patient information corresponding to the target patient from the patient information library, wherein the above-mentioned patient information includes: the main complaint and patient behavior. Here, the key patient information corresponding to the target patient can be automatically extracted from the patient information library where the patient information is fully preserved, so as to facilitate the determination of subsequent conflict information and treatment plans. Then, the treatment plan set corresponding to the above-mentioned main complaint is determined by using the pre-constructed knowledge graph, wherein the above-mentioned knowledge graph represents the corresponding relationship between the disease, the treatment plan and the medicine. Here, by using the knowledge graph technology, each relevant treatment plan corresponding to the disease can be easily and accurately determined to avoid the problem that the content of the artificial query is relatively one-sided. At the same time, through the query of knowledge graph technology, the query efficiency can be greatly improved. And there is an effective basis for the query content. Furthermore, in response to determining that the target patient has a medication being taken, for each treatment plan in the treatment plan set, it is determined whether the treatment plan conflicts with the patient behavior and the medication being taken, and conflict information is obtained. Here, by judging the conflict information, the treatment data can be subsequently targeted and accurate according to the individual situation of the patient. Then, according to the obtained conflict information set, at least one accurate first available treatment plan is selected from the treatment plan set to ensure the accuracy of the treatment data. Finally, the treatment plan information corresponding to the at least one first available treatment plan is sent to the terminal corresponding to the first disease treatment object, so that the target patient receives the treatment related to the disease. In summary, through the knowledge graph technology, efficient query of treatment plans can be achieved, which greatly improves the query efficiency. By determining the conflict information and further screening of the treatment plan set, the accuracy of the treatment data returned to the target patient can be guaranteed. Therefore, on the basis of realizing efficient query of treatment data, with further accurate screening of treatment data, accurate treatment data can be sent to the target patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0027] Figure 1 is a schematic diagram of an application scenario of a medical data sending method according to some embodiments of the present disclosure;

[0028] Figure 2 is a flow chart of some embodiments of the medical data sending method according to the present disclosure;

[0029] Figure 3 are flow charts of other embodiments of the medical data sending method according to the present disclosure;

[0030] Figure 4 is a schematic diagram of the structure of some embodiments of the medical data sending device according to the present disclosure;

[0031] Figure 5 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0032] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0033] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0034] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0035] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0036] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0037] With regard to the collection, storage, and use of user personal information (such as chief complaints) involved in this disclosure, before performing the corresponding operations, the relevant organizations or individuals shall fulfill their obligations, including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subject, and obtaining the authorization and consent of the personal information subject in advance.

[0038] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0039] Figure 1 It is a schematic diagram of an application scenario of the medical data sending method according to some embodiments of the present disclosure.

[0040] exist Figure 1 In the application scenario, first, the electronic device 101 can obtain the patient information 104 corresponding to the target patient 103 from the patient information library 102. Among them, the above-mentioned patient information 104 includes: the main complaint and the patient's behavior. In this application scenario, the main complaint can be "headache, fever". The patient's behavior can be "the patient's daily behavior". Then, the electronic device 101 can use the pre-constructed knowledge graph 105 to determine the treatment plan set 106 corresponding to the above-mentioned main complaint. Among them, the above-mentioned knowledge graph 105 represents the correspondence between symptoms, treatment plans and drugs. In this application scenario, the knowledge graph 106 can be the knowledge graph K1. The treatment plan set 106 can include: treatment plan 1061, treatment plan 1062 and treatment plan 1063. Treatment plan 1061 can be "treatment plan for upper respiratory tract infection". Treatment plan 1062 can be "treatment plan for viral encephalitis". Treatment plan 1063 can be "treatment plan for suppurative encephalitis". Next, in response to determining that the target patient 103 is taking medications, for each treatment plan in the treatment plan set 106, the electronic device 101 can determine whether the treatment plan conflicts with the patient behavior and the medications being taken, and obtain conflict information. In this application scenario, treatment plan 1061 and treatment plan 1062 have corresponding conflict information of non-matching conflicts. Treatment plan 1063 has corresponding conflict information of matching non-conflicting conflicts. Furthermore, the electronic device 101 can filter out at least one first available treatment plan from the treatment plan set 106 according to the obtained conflict information set. In this application scenario, at least one first available treatment plan may include: treatment plan 1063. Finally, the electronic device 101 can send the treatment plan information 107 corresponding to the at least one first available treatment plan to the terminal 108 corresponding to the first disease treatment object. In this application scenario, the terminal 108 can be a terminal used by a doctor.

[0041] It should be noted that the electronic device 101 can be hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the electronic device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0042] It should be understood that Figure 1 The number of electronic devices in the embodiment is only for illustration. Any number of electronic devices may be provided according to implementation requirements.

[0043] Continue to refer Figure 2 , shows a process 200 of some embodiments of the medical data sending method according to the present disclosure. The medical data sending method comprises the following steps:

[0044] Step 201, obtaining patient information corresponding to a target patient from a patient information database.

[0045] In some embodiments, the execution subject of the above-mentioned treatment plan information sending method (for example Figure 1 The electronic device 101 shown can obtain the patient information corresponding to the target patient from the patient information database by wired or wireless means. Among them, the patient information database can be a database in which the patient information of each patient is registered with the authorization of the patient. The patient information database can be in the form of a database. The patient information in the patient information database includes but is not limited to at least one of the following: name, age, gender, current disease name, historical disease name, family disease name, name of the medication being taken, liver and kidney metabolism, gastrointestinal environment (for example, gastrointestinal motility, pH value, etc.), drinking, smoking, diet, daily life, exercise, and vaccines. In practice, the patient information can be information extracted by collecting information such as the patient's physical examination, laboratory tests, imaging evaluation, outpatient clinic, and smart devices over the years. The target patient can be a patient whose treatment plan is to be determined. The treatment plan can be a plan for treating the main complaint of the target patient. Patient information includes: main complaint and patient behavior. Among them, the main complaint can be the main complaint of the target patient. The target patient can be a patient who is to be treated for the disease. For example, the main complaint can be "fever, headache, sweating, and body pain." Patient behavior can be the target patient's daily routine.

[0046] Step 202, using the pre-constructed knowledge graph, determine the treatment plan set corresponding to the above-mentioned main complaint.

[0047] In some embodiments, the execution subject may use a pre-constructed knowledge graph to determine the treatment plan set corresponding to the main complaint. The knowledge graph represents the corresponding relationship between the disease, the treatment plan and the drug. The knowledge graph may be a graph constructed based on a treatment guideline knowledge base. The treatment guideline knowledge base may store a knowledge database of guideline knowledge for treating various diseases. The knowledge graph may be a graph constructed by a single treatment guideline knowledge base, or a graph constructed based on multiple information bases. The multiple information bases include: a treatment guideline knowledge base. For example, the multiple information bases may include: a treatment guideline knowledge base, a disease information base, a patient information base and a drug information base. The disease information base may be a knowledge base storing various disease information. The patient information base may be an information base storing patient information with the authorization of the patient. The drug information base may be an information base storing various drug-related information. The drug information base may be generated by the following steps: First, various preliminary drug information disclosed on the official website is collected (specifically, a web data crawler tool may be used to collect drug information). Then, various preliminary drug information is structured to generate various drug information. Among them, drug information may include but is not limited to at least one of the following: drug name, drug composition, indication, and drug market price. The construction method corresponding to the treatment guideline knowledge base can refer to the construction method corresponding to the drug information base. The guideline knowledge in the treatment guideline knowledge base includes but is not limited to at least one of the following: disease name, gender, age, name of medical indicator of symptoms, threshold of medical indicator of symptoms, registration of symptoms, name of drug treatment under different registered symptoms, steps of diagnosis and treatment plan corresponding to the disease, and precautions (including but not limited to contraindications to medication, implicit, other behavioral habits such as work and rest). The construction method corresponding to the disease information base can refer to the construction method corresponding to the drug information base. The disease information in the disease information base includes but is not limited to at least one of the following: symptom name, disease name, and disease classification. The construction method corresponding to the patient information base can refer to the construction method corresponding to the drug information base. The corresponding node of the knowledge graph can be one of the following: symptom node, treatment plan node, and drug node. The edge between nodes represents the corresponding relationship between the two nodes. The treatment plan in the treatment plan set can be a plan for treating the main complaint.

[0048] As an example, first, the execution subject can use the knowledge graph to find the symptom node corresponding to the main complaint. Then, obtain the treatment plan node set corresponding to the node. Finally, determine the treatment plan set corresponding to the treatment plan node set.

[0049] In some optional implementations of some embodiments, after step 202, the steps further include:

[0050] In the first step, in response to determining that the treatment plan set is empty and that the target patient does not have the corresponding medication being taken, the execution subject may send the patient information corresponding to the main complaint and the target patient to the terminal corresponding to the third symptom treatment object. The third symptom treatment object may be the same object as the first symptom treatment object and the second symptom treatment object, or may not be the same object. The fact that the treatment plan set is empty may indicate that the knowledge graph has not been used to retrieve a treatment plan for the main complaint.

[0051] In the second step, in response to determining that the treatment plan set is empty and that the target patient has corresponding drugs in use, the execution subject may send the main complaint, the drug information corresponding to the drugs in use, and the patient information corresponding to the target patient to a terminal corresponding to the third disease treatment object. The drug information may include: the price of the drugs in use, the name of the drugs in use, and the conflicting drug set of the drugs in use.

[0052] Step 203, in response to determining that the target patient is currently taking any medication, for each treatment plan in the treatment plan set, determine whether the treatment plan conflicts with the patient's behavior and the medication, and obtain conflict information.

[0053] In some embodiments, in response to determining that the target patient is taking any medication, the execution entity may determine, for each treatment plan in the treatment plan set, whether the treatment plan conflicts with the patient behavior and the medication being taken, and obtain conflict information. The medication being taken may be the medication currently being taken by the target patient. The conflict information may characterize whether there is a mismatch between the treatment plan, the patient behavior, and the medication being taken. In practice, the conflict information may be one of the following: information characterizing the existence of a mismatch conflict, and information characterizing the existence of a match without conflict.

[0054] In some optional implementations of some embodiments, the above-mentioned conflict information includes: drug efficacy conflict information and behavior conflict information. Among them, the drug efficacy conflict information can represent the information whether there is a conflict between the drug efficacy corresponding to the various drugs involved in the treatment plan and the drug efficacy corresponding to the drugs being taken. The behavior conflict information can represent the information whether there is a behavioral conflict between the patient's behavior and the treatment behavior mentioned in the treatment plan. That is, the drug efficacy conflict information can be one of the following: information representing the existence of drug efficacy conflicts, and information representing the absence of drug efficacy conflicts. The behavior conflict information can be one of the following: information representing the existence of behavioral conflicts, and information representing the absence of behavioral conflicts. The treatment behavior can represent what kind of behavior the target patient is recommended to take in the treatment plan to achieve the best treatment effect.

[0055] Optionally, the execution subject may determine whether the treatment plan conflicts with the patient's behavior and the medication being taken, and obtain conflict information, including the following steps:

[0056] The first step is to determine the drug efficacy conflict information between the drug set corresponding to the above treatment plan and the above-mentioned drugs being taken. The above-mentioned drug set is determined based on the above-mentioned knowledge graph. The drug set can be determined by the following steps: First, determine the corresponding drug node set according to the treatment plan node in the knowledge graph. Then, determine the drug set corresponding to the drug node set.

[0057] The second step is to determine the behavioral conflict information between the treatment behavior corresponding to the above treatment plan and the above patient behavior.

[0058] In some optional implementations of some embodiments, the execution subject may determine the drug efficacy conflict information between the drug set corresponding to the treatment plan and the drug being taken, including the following steps:

[0059] In the first step, for each drug in the above drug set, perform the following generation steps:

[0060] Sub-step 1, determining the drug ingredient information corresponding to the above-mentioned drug as the first drug ingredient information. The drug ingredient information may be the actual ingredients corresponding to the drug. For example, for the drug Yin*** Pill, the corresponding drug ingredient information may include: honeysuckle, forsythia, mint, schizonepeta, light black beans, burdock, platycodon, light bamboo leaves, liquorice and excipients.

[0061] Sub-step 2, determining the drug ingredient information corresponding to the above-mentioned drug being taken as the second drug ingredient information.

[0062] Sub-step 3, using the pre-trained active ingredient conflict information generation model, generate active ingredient conflict information for the above-mentioned first drug ingredient information and the above-mentioned second drug ingredient information. Among them, the active ingredient conflict information generation model can be a model for generating active ingredient conflict information. In practice, the active ingredient conflict information generation model can be a machine learning model or a deep learning model. The active ingredient conflict information can characterize whether there is a drug ingredient conflict between the first drug ingredient information and the above-mentioned second drug ingredient information. For example, the active ingredient conflict information generation model can be a logistic regression model or a random forest model. The active ingredient conflict information generation model can be generated based on a conventional model training method.

[0063] The second step is to generate the above-mentioned drug efficacy conflict information based on the obtained drug efficacy component conflict information set.

[0064] As an example, in response to determining that there is information in the drug efficacy component conflict information set that indicates whether there is a drug ingredient conflict between the first drug ingredient information and the second drug ingredient information, information indicating that there is a drug efficacy conflict between the drug set and the drug being taken is generated. In response to determining that there is no information in the drug efficacy component conflict information set that indicates whether there is a drug ingredient conflict between the first drug ingredient information and the second drug ingredient information, information indicating that there is no drug efficacy conflict between the drug set and the drug being taken is generated.

[0065] Step 204: Filter out at least one first available treatment plan from the treatment plan set according to the obtained conflict information set.

[0066] In some embodiments, the execution subject may select at least one first available treatment plan from the treatment plan set according to the obtained conflict information set, wherein the first available treatment plan may be a plan that can be used by the target patient to treat the main complaint.

[0067] As an example, the execution subject may remove treatment plans with corresponding conflict information indicating mismatching conflicts from the treatment plan set, and obtain at least one treatment plan as at least one first available treatment plan.

[0068] Step 205: Send treatment plan information corresponding to the at least one first available treatment plan to a terminal corresponding to the first disease treatment target.

[0069] In some embodiments, the execution subject may send the treatment plan information corresponding to the at least one first available treatment plan to the terminal corresponding to the first disease treatment object. The treatment plan information may be the summary information of the plan corresponding to the at least one first available treatment plan. In practice, the treatment plan information may include: at least one available treatment plan information corresponding to the at least one first available treatment plan. Among them, there is an information correspondence between the first available treatment plan and the available treatment plan information. The available treatment plan information may include: the drug set corresponding to the first available treatment plan, the drug price set corresponding to the drug set, the total cost corresponding to the first available treatment plan, and the treatment behavior corresponding to the first available treatment plan. The first disease treatment object may be an object that determines the actual treatment plan corresponding to the target patient and treats the disease. In practice, the first disease treatment object may be an attending physician. The terminal may be a mobile terminal used by the attending physician.

[0070] In some optional implementations of some embodiments, after step 205, the steps further include:

[0071] In the first step, in response to determining that the target patient does not have a corresponding medication, for each treatment plan in the treatment plan set, behavior conflict information between the treatment behavior corresponding to the treatment plan and the patient behavior corresponding to the target patient is determined. The specific implementation method will not be repeated here.

[0072] The second step is to remove the treatment plans with behavior conflict information from the treatment plan set to obtain a second set of remaining treatment plans, wherein the second set of remaining treatment plans can be a subset of treatment plans after removing the treatment plans with treatment behavior conflict information from the treatment plan set.

[0073] The third step is to generate at least one second available treatment plan for the target patient based on the second remaining treatment plan set. The second available treatment plan is an available treatment plan provided to the second disease treatment object for reference to generate an actual treatment plan. The specific implementation method will not be repeated here, and can refer to the generation of at least one first available treatment plan.

[0074] The fourth step is to send the treatment plan information corresponding to the at least one second available treatment plan to the terminal corresponding to the second disease treatment object. The second disease treatment object may be the same object as the first disease treatment object, or may not be the same object. Specific explanations are not repeated here.

[0075] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the medical data sending method of some embodiments of the present disclosure, under the premise that there is no treatment conflict with the target patient, it can assist in the accurate query of treatment data for reference of disease treatment. Specifically, the reason for the inaccuracy of the relevant treatment data query is that the efficiency of artificial query of treatment data (for example, treatment plan) is low and the query content may be relatively one-sided, which makes it difficult to generate accurate treatment data for the patient condition of the target patient. Based on this, the medical data sending method of some embodiments of the present disclosure first obtains the patient information corresponding to the target patient from the patient information library, wherein the above-mentioned patient information includes: the main complaint and patient behavior. Here, the key patient information corresponding to the target patient can be automatically extracted from the patient information library where the patient information is fully preserved, so as to facilitate the determination of subsequent conflict information and treatment plans. Then, the treatment plan set corresponding to the above-mentioned main complaint is determined by using the pre-constructed knowledge graph, wherein the above-mentioned knowledge graph represents the corresponding relationship between the disease, the treatment plan and the medicine. Here, by using the knowledge graph technology, each relevant treatment plan corresponding to the disease can be easily and accurately determined to avoid the problem that the content of the artificial query is relatively one-sided. At the same time, through the query of knowledge graph technology, the query efficiency can be greatly improved. And there is an effective basis for the query content. Furthermore, in response to determining that the target patient has a medication being taken, for each treatment plan in the treatment plan set, it is determined whether the treatment plan conflicts with the patient behavior and the medication being taken, and conflict information is obtained. Here, by judging the conflict information, the treatment data can be subsequently targeted and accurate according to the individual situation of the patient. Then, according to the obtained conflict information set, at least one accurate first available treatment plan is selected from the treatment plan set to ensure the accuracy of the treatment data. Finally, the treatment plan information corresponding to the at least one first available treatment plan is sent to the terminal corresponding to the first disease treatment object, so that the target patient receives the treatment related to the disease. In summary, through the knowledge graph technology, efficient query of treatment plans can be achieved, which greatly improves the query efficiency. By determining the conflict information and further screening of the treatment plan set, the accuracy of the treatment data returned to the target patient can be guaranteed. Therefore, on the basis of realizing efficient query of treatment data, with further accurate screening of treatment data, accurate treatment data can be sent to the target patient.

[0076] Further references Figure 3 , shows a process 300 of another embodiment of the method for sending treatment plan information according to the present disclosure. The method for sending treatment plan information includes the following steps:

[0077] Step 301, obtaining patient information corresponding to a target patient from a patient information database.

[0078] Step 302, using the pre-constructed knowledge graph, determine the treatment plan set corresponding to the above-mentioned main complaint.

[0079] Step 303, in response to determining that the target patient is currently taking any medication, for each treatment plan in the treatment plan set, determine whether the treatment plan conflicts with the patient's behavior and the medication, and obtain conflict information.

[0080] Step 304 , removing treatment plans whose corresponding drug efficacy conflict information indicates that there is a drug conflict and / or whose corresponding behavior conflict information indicates that there is a behavior conflict from the above treatment plan set, to obtain a first remaining treatment plan set.

[0081] In some embodiments, the execution entity (eg, electronic device) may remove treatment plans whose corresponding drug efficacy conflict information indicates drug conflicts and / or whose corresponding behavior conflict information indicates behavior conflicts from the above treatment plan set to obtain a first set of remaining treatment plans.

[0082] Step 305: Screen out at least one first available treatment plan from the first set of remaining treatment plans.

[0083] In some embodiments, the execution entity may filter out at least one first available treatment plan from the first set of remaining treatment plans.

[0084] As an example, the execution entity may directly filter out the first remaining treatment plan set to obtain the first remaining treatment plans whose usage frequencies are in the front target number, and obtain at least one first available treatment plan.

[0085] In some optional implementations of some embodiments, based on the above-mentioned gastrointestinal condition information and the above-mentioned liver and kidney metabolism information, the first remaining treatment schemes that affect gastrointestinal digestion and / or liver metabolism are removed from the above-mentioned first remaining treatment scheme set to obtain at least one first available treatment scheme. Wherein, the above-mentioned target patient information includes: gastrointestinal condition information and liver and kidney metabolism information. The gastrointestinal condition information can characterize the gastrointestinal condition of the target patient. For example, the gastrointestinal condition information can be that the target patient has gastroenteritis symptoms, or that the target patient does not have a gastrointestinal condition. The liver and kidney metabolism information can characterize the liver and kidney metabolism of the target patient. For example, the liver and kidney metabolism information can be that the liver and kidney metabolism is poor due to the presence of uremia in the target patient, or that the liver metabolism of the target patient is normal. Wherein, the effect on gastrointestinal digestion can be that the drugs corresponding to the treatment scheme affect the patient's gastrointestinal digestion. The effect on liver metabolism can be that the drugs corresponding to the treatment scheme affect the patient's liver metabolism.

[0086] As an example, in response to determining that the gastrointestinal condition information indicates that the target patient has a gastrointestinal condition and the liver and kidney metabolism information indicates that the liver and kidney metabolism is normal, the first remaining treatment plan corresponding to the drug set that affects gastrointestinal digestion is removed from the above-mentioned first remaining treatment plan set to obtain at least one first available treatment plan.

[0087] As another example, in response to determining that the gastrointestinal condition information indicates that the target patient has no gastrointestinal condition and the liver and kidney metabolism information indicates that liver and kidney metabolism is normal, the execution entity may determine the first remaining treatment plan set as at least one first available treatment plan.

[0088] As another example, in response to determining that the gastrointestinal condition information indicates that the target patient has a gastrointestinal condition and the liver and kidney metabolism information indicates that the liver and kidney metabolism is poor, the above-mentioned execution entity can remove the first remaining treatment plans whose corresponding drug sets affect gastrointestinal digestion and whose drug sets affect liver and kidney metabolism from the first remaining treatment plan set to obtain at least one first available treatment plan.

[0089] As another example, in response to determining that the gastrointestinal condition information indicates that the target patient has no gastrointestinal condition and the liver and kidney metabolism information indicates that the liver and kidney metabolism is poor, the above-mentioned execution entity can remove the first remaining treatment plan corresponding to the drug set that affects liver and kidney metabolism from the first remaining treatment plan set to obtain at least one first available treatment plan.

[0090] Step 306: Send treatment plan information corresponding to the at least one first available treatment plan to a terminal corresponding to the first disease treatment target.

[0091] In some embodiments, the specific implementation of steps 301-303 and 306 and the technical effects thereof can be referred to in Figure 2 The corresponding steps 201-203 and 205 in the embodiment are not described in detail here.

[0092] from Figure 3 It can be seen that Figure 2 Compared with the description of some corresponding embodiments, Figure 3 In the corresponding process 300 of the medical data sending method in some embodiments, further efficient screening of treatment plans is performed from the aspects of drug efficacy conflict information and behavior conflict information, which not only ensures the screening efficiency but also improves the screening speed. Thus, at least one more accurate first available treatment plan can be screened out.

[0093] Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a medical data sending device, and these device embodiments are Figure 2 Corresponding to the method embodiments shown, the medical data sending device can be specifically applied to various electronic devices.

[0094] like Figure 4 As shown, a medical data sending device 400 includes: an acquisition unit 401, a first determination unit 402, a second determination unit 403, a screening unit 404 and a sending unit 405. The acquisition unit 401 is configured to acquire patient information corresponding to a target patient from a patient information database, wherein the patient information includes: a main complaint symptom and a patient behavior; the first determination unit 402 is configured to determine a treatment plan set corresponding to the main complaint symptom by using a pre-built knowledge graph, wherein the knowledge graph represents the correspondence between symptoms, treatment plans and drugs; the second determination unit 403 is configured to, in response to determining that the target patient has a drug being taken, determine whether the treatment plan conflicts with the patient behavior and the drug being taken for each treatment plan in the treatment plan set, and obtain conflict information; the screening unit 404 is configured to screen out at least one first available treatment plan from the treatment plan set according to the obtained conflict information set; and the sending unit 405 is configured to send the treatment plan information corresponding to the at least one first available treatment plan to a terminal corresponding to the first symptom treatment object.

[0095] In some optional implementations of some embodiments, the above-mentioned conflict information includes: drug efficacy conflict information and behavior conflict information; and the second determination unit 403 can be further configured to: determine the drug efficacy conflict information between the drug set corresponding to the above-mentioned treatment plan and the above-mentioned drugs being taken, wherein the above-mentioned drug set is determined based on the above-mentioned knowledge graph; determine the behavior conflict information between the treatment behavior corresponding to the above-mentioned treatment plan and the above-mentioned patient behavior.

[0096] In some optional implementations of some embodiments, the screening unit 404 may be further configured to: remove from the above-mentioned treatment plan set treatment plans whose corresponding drug efficacy conflict information indicates the existence of drug conflicts and / or whose corresponding behavior conflict information indicates the existence of behavior conflicts, to obtain a first set of remaining treatment plans; and screen out at least one first available treatment plan from the above-mentioned first set of remaining treatment plans.

[0097] In some optional implementations of some embodiments, the above-mentioned patient information also includes: gastrointestinal condition information and liver and kidney metabolism information; and the screening unit 404 can be further configured to: according to the above-mentioned gastrointestinal condition information and the above-mentioned liver and kidney metabolism information, remove the first remaining treatment plans that affect gastrointestinal digestion and / or liver metabolism from the above-mentioned first remaining treatment plan set to obtain at least one first available treatment plan.

[0098] In some optional implementations of some embodiments, the second determination unit 403 may be further configured to: for each drug in the above-mentioned drug set, perform the following generation steps: determine the drug ingredient information corresponding to the above-mentioned drug as the first drug ingredient information; determine the drug ingredient information corresponding to the above-mentioned drug in use as the second drug ingredient information; use a pre-trained drug efficacy conflict information generation model to generate drug efficacy conflict information for the above-mentioned first drug ingredient information and the above-mentioned second drug ingredient information; and generate the above-mentioned drug efficacy conflict information based on the obtained drug efficacy conflict information set.

[0099] In some optional implementations of some embodiments, the above-mentioned device 400 also includes: a third determination unit, a scheme removal unit, a scheme generation unit and a first terminal sending unit (not shown in the figure). Among them, the above-mentioned third determination unit can be configured to: in response to determining that the above-mentioned target patient does not have corresponding medication, for each treatment plan in the above-mentioned treatment plan set, determine the behavior conflict information between the treatment behavior corresponding to the above-mentioned treatment plan and the above-mentioned patient behavior. The scheme removal unit can be configured to: remove the treatment plans whose behavior conflict information indicates that there is a behavior conflict from the above-mentioned treatment plan set, and obtain a second set of remaining treatment plans. The scheme generation unit can be configured to: generate at least one second available treatment plan for the above-mentioned target patient based on the above-mentioned second remaining treatment plan set. The first terminal sending unit can be configured to: send the treatment plan information corresponding to the above-mentioned at least one second available treatment plan to the terminal corresponding to the second disease treatment object.

[0100] In some optional implementations of some embodiments, the device 400 further includes: a first terminal sending unit and a second terminal sending unit (not shown in the figure). The first terminal sending unit may be configured to: in response to determining that the treatment plan set is empty and that the target patient does not have corresponding medications being taken, send the main complaint and the patient information to a terminal corresponding to a third symptom treatment object. The second terminal sending unit may be configured to: in response to determining that the treatment plan set is empty and that the target patient has corresponding medications being taken, send the main complaint, the medication information corresponding to the medications being taken, and the patient information to a terminal corresponding to a third symptom treatment object.

[0101] It is understandable that the units described in the medical data transmitting device 400 are similar to those described in the reference Figure 2 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the medical data sending device 400 and the units included therein, and will not be described in detail here.

[0102] Reference below Figure 5, which shows an electronic device (eg, Figure 1 Schematic diagram of the structure of the electronic device 101)500. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0103] like Figure 5 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory 502 or a program loaded from a storage device 508 to a random access memory 503. Various programs and data required for the operation of the electronic device 500 are also stored in the random access memory 503. The processing device 501, the read-only memory 502, and the random access memory 503 are connected to each other via a bus 504. An input / output interface 505 is also connected to the bus 504.

[0104] Typically, the following devices may be connected to the input / output interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0105] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the read-only memory 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0106] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0107] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0108] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains patient information corresponding to the target patient from the patient information database, wherein the patient information includes: the main complaint and the patient behavior; determines the treatment plan set corresponding to the main complaint using a pre-constructed knowledge graph, wherein the knowledge graph represents the correspondence between the disease, the treatment plan and the medicine; in response to determining that the target patient is taking medicine, for each treatment plan in the treatment plan set, determines whether the treatment plan conflicts with the patient behavior and the medicine, and obtains conflict information; based on the obtained conflict information set, selects at least one first available treatment plan from the treatment plan set; and sends the treatment plan information corresponding to the at least one first available treatment plan to the terminal corresponding to the first disease treatment object.

[0109] Computer program code for performing the operation of some embodiments of the present disclosure can be written in one or more programming languages ​​or a combination thereof, the programming languages ​​including object-oriented programming languages, and also including conventional procedural programming languages. The program code can be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0110] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0111] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be set in a processor, for example, it may be described as: a processor includes an acquisition unit, a first determination unit, a second determination unit, a screening unit, and a sending unit. The names of these units do not constitute a limitation on the units themselves in some cases. For example, the acquisition unit may also be described as "a unit for acquiring patient information corresponding to a target patient from a patient information database."

[0112] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0113] Some embodiments of the present disclosure also provide a computer program product, including a computer program, which implements any of the above-mentioned medical data sending methods when executed by a processor.

[0114] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for sending medical data, comprising: Acquire patient information corresponding to the target patient from a patient information database, wherein the patient information includes: main complaint symptoms and patient behavior; Determine a set of treatment plans corresponding to the main complaint using a pre-constructed knowledge graph, wherein the knowledge graph represents the correspondence between symptoms, treatment plans, and drugs; In response to determining that the target patient is taking any medication, for each treatment plan in the treatment plan set, determining whether the treatment plan conflicts with the patient's behavior and the medication, and obtaining conflict information; Screening out at least one first available treatment plan from the treatment plan set according to the obtained conflict information set; The treatment plan information corresponding to the at least one first available treatment plan is sent to a terminal corresponding to the first disease treatment object.

2. The method according to claim 1, wherein: The conflict information includes: drug efficacy conflict information and behavior conflict information; and The determining whether the treatment plan conflicts with the patient's behavior and the medication being taken, and obtaining conflict information includes: Determine the drug efficacy conflict information between the drug set corresponding to the treatment plan and the drug being taken, wherein the drug set is determined based on the knowledge graph; Determine behavior conflict information between the treatment behavior corresponding to the treatment plan and the patient behavior.

3. The method according to claim 2, wherein: The step of selecting at least one first available treatment plan from the treatment plan set according to the obtained conflict information set includes: Remove treatment plans for which drug conflicts are indicated by drug efficacy conflict information and / or behavioral conflicts are indicated by behavioral conflict information from the treatment plan set, to obtain a first set of remaining treatment plans; At least one first available treatment regimen is selected from the first set of remaining treatment regimens.

4. The method according to claim 3, wherein: The patient information also includes: gastrointestinal condition information and liver and kidney metabolism information; and The step of selecting at least one first available treatment regimen from the first set of remaining treatment regimens comprises: According to the gastrointestinal condition information and the liver and kidney metabolism information, the first remaining treatment plans that affect gastrointestinal digestion and / or liver metabolism are removed from the first remaining treatment plan set to obtain at least one first available treatment plan.

5. The method according to claim 2, wherein: The determining of the drug efficacy conflict information between the drug set corresponding to the treatment plan and the drug being taken includes: For each drug in the drug set, perform the following generation steps: Determine drug ingredient information corresponding to the drug as first drug ingredient information; Determine the drug ingredient information corresponding to the drug being taken as the second drug ingredient information; Generate the pharmacodynamic component conflict information for the first drug component information and the second drug component information using a pre-trained pharmacodynamic component conflict information generation model; The drug efficacy conflict information is generated according to the obtained drug efficacy component conflict information set.

6. The method according to claim 1, wherein: The method further comprises: In response to determining that the target patient does not have a corresponding medication being taken, for each treatment plan in the treatment plan set, determining behavior conflict information between a treatment behavior corresponding to the treatment plan and a patient behavior; Removing treatment plans with behavior conflict information indicating that there is a behavior conflict from the treatment plan set to obtain a second set of remaining treatment plans; generating at least one second available treatment plan for the target patient according to the second remaining treatment plan set; The treatment plan information corresponding to the at least one second available treatment plan is sent to a terminal corresponding to the second disease treatment object.

7. The method according to claim 1, wherein: After determining the treatment plan set corresponding to the main complaint by using the pre-built knowledge graph, the method further includes: In response to determining that the treatment plan set is empty and determining that the target patient does not have a corresponding medication being taken, sending the main complaint and the patient information to a terminal corresponding to a third symptom treatment object; In response to determining that the treatment plan set is empty and determining that the target patient has corresponding medications being taken, the main complaint, medication information corresponding to the medications being taken, and the patient information are sent to a terminal corresponding to a third symptom treatment target.

8. A medical data transmitting device, comprising: An acquisition unit is configured to acquire patient information corresponding to a target patient from a patient information database, wherein the patient information includes: a main complaint symptom and a patient behavior; A first determination unit is configured to determine a set of treatment plans corresponding to the main complaint by using a pre-built knowledge graph, wherein the knowledge graph represents a correspondence between symptoms, treatment plans, and drugs; A second determination unit is configured to, in response to determining that the target patient is taking any medication, determine, for each treatment plan in the treatment plan set, whether the treatment plan conflicts with the patient's behavior and the medication being taken, and obtain conflict information; a screening unit, configured to screen out at least one first available treatment plan from the treatment plan set according to the obtained conflict information set; The sending unit is configured to send the treatment plan information corresponding to the at least one first available treatment plan to a terminal corresponding to the first disease treatment object.

9. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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