Internet-based medical resource distribution system

Abstract

The invention relates to the technical field of electronic information, and particularly discloses a medical resource allocation system based on the Internet. By setting the user registration module, the intelligent hospital guide appointment module, the family member guard module, the intelligent question and answer module and the permission granting module, a user can fill basic information through the user registration module, and when the user needs to see a doctor, symptom description is performed through the intelligent hospital guide appointment module; according to the method and the system, a user can select a hospital with rapid reception or good diagnosis and treatment service according to the treatment condition, so that the patient can be helped to rapidly and effectively obtain the medical service, limited medical resources can be efficiently utilized, and in addition, the system and the method can be widely applied to the field of medical treatment. And the users with the family relationship can mutually know the doctor seeing dynamic state, so that the users can care family members in time and know the doctor seeing condition of the family members.

Description

Technical Field

[0001] The present invention belongs to the field of electronic information technology, and in particular relates to an Internet-based medical resource allocation system. Background Art

[0002] With the advancement of medicine, the division of disease areas has become more and more refined, and most hospitals have set up more and more departments. Each department corresponds to a different treatment area, such as internal medicine, surgery, oncology, etc. Faced with so many departments with different specialties, patients are often at a loss when seeking medical treatment. Not only do they not know which department to make an appointment for, but they also flock to large medical institutions due to panic about illness, instead of going to some small medical institutions with the ability to treat corresponding diseases. This leads to an overload of medical resources in large hospitals, and both mild and severe patients scramble for limited medical resources, which in turn makes some patients unable to receive timely treatment and endangers their lives.

[0003] In order to cope with the problem of difficulty in seeing a doctor, most hospitals have enabled online medical treatment systems. Most patients can make appointments with the corresponding departments online and freely choose the appointment time for treatment. Moreover, the in-hospital medical treatment systems of most hospitals are now very complete. As long as patients enter the hospital, they can get very complete medical services. However, how to reasonably choose the hospital and department to see a doctor is still the primary problem that patients need to face and needs to be solved urgently. Summary of the Invention

[0004] The purpose of the present invention is to provide an Internet-based medical resource allocation system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An Internet-based medical resource allocation system, specifically comprising:

[0007] User registration module, used to collect basic user information and make intelligent medical consultation plans based on the collected information;

[0008] The smart medical guidance and appointment module is used to collect user-reported symptoms, make a preliminary diagnosis based on the symptoms, and then recommend the hospital and department for treatment based on the preliminary diagnosis;

[0009] The family protection module is used to connect family members to obtain the user's medical information;

[0010] Intelligent question-and-answer module, used to implement intelligent question-and-answer between users and AI intelligent models;

[0011] The permission granting module is used to configure system function permissions.

[0012] Preferably, the basic user information collected by the user registration module includes name, gender, age, past medical history, contact information, home address, names and contact information of family members.

[0013] Preferably, the intelligent diagnosis guidance and appointment module includes:

[0014] A symptom description sub-module for collecting the symptoms described by the patient. The collection methods are set to text collection and picture collection. The user describes the symptoms by inputting text and confirms the lesion by clicking on the corresponding part of the human body picture;

[0015] A preliminary disease diagnosis module with a preliminary cause analysis algorithm built in, which is used to preliminarily diagnose the user's cause based on the symptoms and lesions confirmed by the symptom description sub-module, and determine the department for medical treatment according to the cause;

[0016] A department recommendation module for hospitals and departments, with a department recommendation algorithm built in, which is used to recommend hospitals and departments according to the corresponding determined department for medical treatment;

[0017] An appointment registration module for the user to make an appointment registration in the selected corresponding hospital department.

[0018] Preferably, the specific establishment steps of the preliminary cause analysis algorithm include:

[0019] a1. Knowledge extraction: Use a crawler program to crawl disease information in the public domain, collect text data, clean the text data to remove noise and clutter, perform entity recognition on the preprocessed text, identify meaningful entities from the text information, including diagnostic examination items, departments for medical treatment, diseases, drugs, diseased parts, disease symptoms, and disease severity, perform relation extraction on the preprocessed text data. The relation extraction methods include constructing a dependency syntactic tree from the text and extracting relation templates from the text, including belonging to, common drugs for diseases, corresponding departments for diseases, disease aliases, required examinations for diseases, disease parts, disease symptoms, and disease complications, and identify the relationships between entities;

[0020] a2. Knowledge fusion: Integrate the knowledge to form a unified and consistent knowledge graph, and match and merge the same entities in different data sources;

[0021] a3. Further process the extracted and fused knowledge to improve the quality and usability of the knowledge graph, check whether the knowledge in the knowledge graph is correct and consistent, derive new knowledge using the existing knowledge, and complete the missing information in the knowledge graph;

[0022] a4. Knowledge update: Use new data and changing data to update the knowledge graph regularly to maintain the timeliness and accuracy of the knowledge graph.

[0023] Preferably, the specific steps of the department recommendation algorithm are as follows:

[0024] b1. Select multiple medical experts in the corresponding region, estimate the scores of hospitals and departments in this region. Each expert has the same scoring weight. Accumulate the scores of multiple medical experts and rank the strength of hospital departments.

[0025] b2. Starting from the location of the patient, search for hospitals with the ability to treat within a specified range according to the initially diagnosed cause of the user. Calculate the arrival time at the hospital based on the user's selected travel mode for seeing a doctor. At the same time, the system connects to the appointment system of the corresponding hospital, queries the number of queuing numbers in the corresponding department, calculates the sum of the queuing time and the arrival time at the hospital, and conducts sorting. The formula is expressed as:

[0026] T = t1 + βC

[0027] Where, T is the medical treatment time, t1 is the travel time to the hospital, β is the number of numbers before the current appointment number that have not been treated, and C is the treatment time for a single number, which is set as a constant.

[0028] b3. The user selects one of the two sorting methods according to the urgency and professionalism of the medical treatment, and selects the hospital and department for medical treatment from them.

[0029] Preferably, the family guardian module is used to establish family connections between different users. Family members can query each other's medical treatment status, medical treatment time, end time of medical treatment, and location.

[0030] Preferably, the permission granting module is used to configure the application permissions between the system and the device, including device location, network data, address book list, microphone, camera, and picture gallery.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] By setting up a user registration module, a smart medical guidance and appointment module, a family guardian module, an intelligent Q&A module, and a permission granting module, users can fill in basic information through the user registration module. When users need medical treatment, they can describe their symptoms through the smart medical guidance and appointment module, conduct a preliminary diagnosis of the condition and recommend relevant hospitals and departments. Users can select a hospital with a quick reception or better medical treatment services according to their medical treatment conditions, which can not only help patients obtain medical services quickly and effectively, but also make efficient use of limited medical resources. In addition, users with family relationships can understand each other's medical treatment dynamics, which is convenient for timely care of family members and understanding of their family members' medical treatment situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a structural block diagram of the medical treatment system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0034] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0035] Embodiment 1:

[0036] Referring to Figure 1 , a medical resource allocation system based on the Internet specifically includes:

[0037] A user registration module, which is used to collect basic user information and perform intelligent medical treatment planning according to the collected information. The basic user information collected by the user registration module includes name, gender, age, past medical records, contact information, home address, names and contact information of family members;

[0038] A smart diagnosis and appointment module, which is used to collect the self-reported symptoms of the user's condition, make a preliminary diagnosis of the condition according to the symptoms, and then recommend the department and section for medical treatment according to the preliminary diagnosed condition;

[0039] A family care module, which is used to associate family members to obtain the user's medical treatment information;

[0040] An intelligent Q&A module, which is used to realize intelligent Q&A between the user and the AI intelligent model;

[0041] A permission granting module, which is used to configure the system function permissions.

[0042] As can be seen from the above, after the user enters the system, first use the user registration module to register information, input some basic information of the registered user to the system. At the same time, the permission granting module will also obtain relevant permissions for calling the permissions of system devices to collect relevant data. When the user needs medical treatment, the smart diagnosis and appointment module will collect the self-reported symptoms of the user's condition, make a preliminary diagnosis of the condition according to the symptoms, and then recommend the department and section for medical treatment according to the preliminary diagnosed condition, saving the trouble for the user to find the corresponding hospital department for the condition and helping the user obtain fast and effective medical treatment services. Through the family care module, the associated family members with family connections can also understand each other's medical treatment information. In addition, when the user doesn't know how to operate, they can use the intelligent Q&A module to ask the AI intelligent model to help the user quickly understand the usage or find the knowledge they want to know. The AI intelligent model can select an existing and mature general model.

[0043] The smart diagnosis and appointment module includes:

[0044] Symptom description sub-module, which is used to collect the symptoms described by the patient. The collection methods are set as text collection and picture collection. Users can describe symptoms by inputting text and confirm the lesion by clicking on the corresponding part of the human body picture. The user can manually input text or input text by voice, which is convenient for some users who cannot write to use the system. After the text input is completed, the system will read the text aloud to verify with the user. When there is an error in the text description, the user can modify it. After verification, the system pops up a human body picture, and the user can click on the corresponding position of the picture to mark the lesion, and make a preliminary identification of the patient's cause through the combination of pictures and text;

[0045] Initial disease diagnosis module, which has an initial cause analysis algorithm built in, is used to initially diagnose the user's cause based on the symptoms and lesions confirmed by the symptom description sub-module, and determine the department for medical treatment according to the cause;

[0046] Department and hospital recommendation module, which has a department and hospital recommendation algorithm built in, is used to recommend hospitals and departments according to the corresponding determined department for medical treatment;

[0047] Reservation registration module, which is used for users to make reservation registrations in the selected corresponding hospital departments. The reservation registration module connects to the medical treatment system program of the corresponding hospital according to the user's authorization, saving the trouble for users to find the reservation registration link separately.

[0048] The specific establishment steps of the initial cause analysis algorithm include:

[0049] a1. Knowledge extraction: Use a crawler program to crawl disease information in the public domain, collect text data, and perform text cleaning on the text data to remove noise and messy information. Perform entity recognition on the preprocessed text to identify meaningful entities from the text information, including diagnostic examination items, departments for medical treatment, diseases, drugs, diseased parts, disease symptoms, and disease severity. Perform relationship extraction on the preprocessed text data. The relationship extraction methods include constructing a dependency syntax tree from the text and extracting relationship templates from the text, including belonging to, commonly used drugs for diseases, corresponding departments for diseases, disease aliases, required examinations for diseases, disease parts, disease symptoms, and disease complications, and identify the relationships between entities;

[0050] a2. Knowledge fusion: Integrate the knowledge to form a unified and consistent knowledge graph, and match and merge the same entities in different data sources;

[0051] a3. Further process the extracted and fused knowledge to improve the quality and usability of the knowledge graph, check whether the knowledge in the knowledge graph is correct and consistent, use the existing knowledge to derive new knowledge, and supplement the missing information in the knowledge graph;

[0052] a4. Knowledge update: Regularly update the knowledge graph using new and changing data to maintain its timeliness and accuracy.

[0053] Based on the established knowledge graph, the initial disease diagnosis module identifies relevant entity words from the user's self-reported symptoms and confirmed lesion locations. According to the relationships in the knowledge graph, it queries other entities based on these entity words, thereby determining the cause entities and related department entities corresponding to the symptom entities, and outputs the corresponding causes and departments. The user can then seek medical treatment at a hospital according to the department.

[0054] The specific steps of the department recommendation algorithm are as follows:

[0055] b1. Select multiple experts in the medical field in the corresponding region to estimate the scores of hospitals and departments in that region. Each expert has the same scoring weight. Accumulate the scores of multiple medical field experts to rank the strength of hospital departments.

[0056] b2. Starting from the location of the patient, search for hospitals with the ability to treat within a specified range according to the initially diagnosed cause of the user. Calculate the time to the hospital based on the user's selected travel mode. At the same time, the system connects to the appointment system of the corresponding hospital to query the number of available appointments in the corresponding department, and calculate the sum of the appointment time and the time to the hospital for sorting. The formula is expressed as:

[0057] T = t1 + βC

[0058] Where T is the appointment time, t1 is the travel time to the hospital, β is the number of unappointed numbers before the current appointment number, and C is the appointment time for a single number, which is set as a constant.

[0059] b3. The user selects one of the two sorting methods according to the urgency and professionalism of the appointment, and then selects the hospital and department for the appointment.

[0060] Selecting multiple local medical field experts to evaluate the hospital departments in the region can effectively ensure the accuracy of the evaluation results, making the ranking of hospital department strength more in line with the actual situation. In addition, the system will also calculate the time to each hospital based on the user's current location, including travel time and reception time. The user can choose a hospital department with fast appointment or strong medical strength according to their needs.

[0061] The family guardian module is used to establish family connections between different users. Family members can query each other's appointment status, appointment time, end time of the appointment, and location.

[0062] The permission granting module is used to configure the application permissions between the system and the device, including device location, network data, address book list, microphone, camera, and photo gallery. Device location is used to reflect the user's current location, network data is used to transmit data, the address book list is used to obtain the contact information of the user's family members, the microphone is used for voice inquiries, and the camera is used to take pictures of the medical treatment scene. The user can grant different permissions to the system according to needs.

[0063] A smart medical treatment method is applied to an Internet-based medical resource allocation system, which specifically includes the following steps:

[0064] S1. The user describes the symptoms and determines the lesion location to the system by inputting text and clicking on the human body picture. The system supports the user to describe the symptoms by voice.

[0065] S2. The system makes a preliminary diagnosis of the condition based on the patient's self-reported symptoms and lesion location, using the preliminary analysis algorithm for the cause of the disease, differentiates the cause of the user's disease, and determines the corresponding department for medical treatment according to the diagnosed cause of the disease and the department recommendation algorithm.

[0066] S3. After the system recommends the corresponding hospital department through the algorithm, the user selects the corresponding department for medical treatment according to the time and medical treatment needs, and can choose the department with rapid reception or the department with better medical resources.

[0067] S4. The system sends the patient's medical treatment information to the associated family members, and the family members share the information with each other to facilitate timely care of the patient.

[0068] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0069] In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. Other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

Claims

1. An Internet-based medical resource allocation system, characterized in that, Specifically include: User registration module, which is used to collect basic user information and make intelligent medical treatment plans based on the collected information; Intelligent medical guidance and appointment module, which is used to collect the self-reported symptoms of the user's illness, make a preliminary diagnosis of the illness based on the symptoms, and then recommend the department and section for medical treatment according to the preliminary diagnosed illness; Family guardianship module, which is used to associate family members to obtain user medical information; Intelligent Q&A module, which is used to realize intelligent Q&A between the user and the AI intelligent model; Permission granting module, which is used to configure system function permissions.

2. The medical resource allocation system based on the Internet according to claim 1, wherein: The basic user information collected by the user registration module includes name, gender, age, previous medical records, contact information, home address, names and contact information of family members.

3. The medical resource allocation system based on the Internet according to claim 2, characterized in that: The intelligent medical guidance and appointment module includes: Symptom description sub-module, which is used to collect the self-reported symptoms of the patient. The collection methods are set as text collection and picture collection. The user describes the symptoms by inputting text and clicks on the corresponding part of the human body picture to confirm the lesion; Preliminary illness diagnosis module, which has a preliminary cause analysis algorithm built in, and is used to preliminarily diagnose the user's cause of illness based on the symptoms and lesions confirmed by the symptom description sub-module, and determine the department for medical treatment according to the cause of illness; Department and section recommendation module, which has a department and section recommendation algorithm built in, and is used to recommend hospitals and departments according to the corresponding determined department for medical treatment; Reservation registration module, which is used for the user to make a reservation registration in the selected corresponding hospital department.

4. The medical resource allocation system based on the Internet according to claim 3, characterized in that: The specific establishment steps of the preliminary cause analysis algorithm include: a1. Knowledge extraction: Use a crawler program to crawl disease information in the public domain, collect text data, clean the text data to remove noise and clutter, perform entity recognition on the preprocessed text, and identify meaningful entities from the text information, including diagnostic examination items, departments for medical treatment, diseases, drugs, diseased parts, disease symptoms, and disease severity. Perform relationship extraction on the preprocessed text data. The relationship extraction methods include constructing a dependency syntax tree from the text and extracting relationship templates from the text, including belonging to, commonly used drugs for diseases, corresponding departments for diseases, disease aliases, required examinations for diseases, disease parts, disease symptoms, and disease complications, and identify the relationships between entities; a2. Knowledge fusion: Integrate the knowledge to form a unified and consistent knowledge graph, and match and merge the same entities in different data sources; a3. Further process the extracted and fused knowledge to improve the quality and usability of the knowledge graph, check whether the knowledge in the knowledge graph is correct and consistent, derive new knowledge using the existing knowledge, and supplement the missing information in the knowledge graph; a4. Knowledge update: Use new data and changed data to update the knowledge graph regularly to maintain the timeliness and accuracy of the knowledge graph.

5. The medical resource allocation system based on the Internet according to claim 4, wherein: The specific steps of the department and section recommendation algorithm include: b1. Select multiple medical experts in the corresponding region, estimate the scores of hospitals and departments in the region, and the scoring weights of each expert are the same. Accumulate the scores of multiple medical experts in different fields, and sort the strength of hospital departments; b2. Starting from the location of the patient, search for hospitals with treatment capabilities within the specified range according to the initially diagnosed cause of the user. Calculate the arrival time at the hospital based on the user's selected transportation mode for seeing a doctor. At the same time, the system connects to the appointment system of the corresponding hospital, queries the number of queued numbers in the corresponding department, calculates the sum of the queuing time and the arrival time, and performs sorting. The formula is expressed as: T = t1 + βC Where, T is the appointment time, t1 is the transportation arrival time, β is the number of numbers before the appointment number that have not been seen, and C is the appointment time for a single number, which is set as a constant; b3. The user selects one of the two sorting methods according to the urgency and professionalism of seeing a doctor, and selects the hospital and department for seeing a doctor from them.

6. The medical resource allocation system based on the Internet according to claim 1, characterized in that: The family guardian module is used to establish family connections between different users. Family members can query each other's appointment status, appointment time, appointment end time, and location.

7. The medical resource allocation system based on the Internet according to claim 1, wherein: The permission granting module is used to configure the application permissions between the system and the device, including device location, network data, address book list, microphone, camera, and photo gallery.

Images