Medical pre-diagnosis system based on artificial intelligence and big data

Abstract

The invention belongs to the technical field of medical treatment, and particularly relates to a medical pre-diagnosis system based on artificial intelligence and big data, and the system comprises a patient login module which is used for enabling a patient to log in a client; the registration module is used for registering the patient on the registration platform and uploading the registered data to the client; the inquiry time calculation module is used for calculating and predicting the required inquiry time according to the illness state of the patient; and the registration information acquisition module is used for acquiring registration information of the patient. By arranging the inquiry time calculation module and the doctor-seeing time calculation module, the doctor-seeing time of the patient can be predicted according to the inquiry time of other patients during pre-diagnosis, so that the phenomenon that the patient queues for inquiry can be avoided to a certain extent, and the number of people in a hospital can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a medical pre-diagnosis system based on artificial intelligence and big data. Background Art

[0002] With the development of network technology, all offline hospitals have online medical diagnosis systems. However, patients still need to go to the hospital to wait for consultation. If the number of people waiting for consultation is large, patients need to queue up for their number, which delays the patient's time. Especially in large cities, the number of people waiting for diagnosis occupies a large area of the hospital, which affects the normal movement of the hospital and the movement of equipment. During the busy process of the hospital, the congestion phenomenon exacerbates the medical fatigue of medical staff and reduces the hospital's medical enthusiasm. Moreover, when patients pile up in the queue, the communication between patients is prone to respiratory cross-infection, which aggravates the deterioration of their condition. Therefore, a medical pre-diagnosis system based on artificial intelligence and big data is invented. Summary of the Invention

[0003] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0004] A medical pre-diagnosis system based on artificial intelligence and big data, comprising:

[0005] Patient login module, used for patients to log in to the client;

[0006] The registration module is used for patients to register on the registration platform and upload the registered data to the client;

[0007] The consultation time calculation module is used to calculate and predict the required consultation time based on the patient's condition;

[0008] The registration information acquisition module is used to obtain the patient's registration information;

[0009] The consultation time calculation module is used to calculate the consultation time before the registration number obtained by the registration information acquisition module;

[0010] A display module, used to display the consultation time calculated by the consultation time calculation module;

[0011] The medical treatment status module is used to display the patient's medical treatment status;

[0012] A medical consultation record storage module is used to store the patient's medical consultation records;

[0013] Follow-up consultation module, which is used to conduct online follow-up consultations with patients when they return for follow-up consultations;

[0014] The patient evaluation and feedback module is used to evaluate the doctor's service attitude, professional level, and treatment effect after the consultation. At the same time, it collects patients' opinions and suggestions to provide reference for system optimization. Hospital managers and doctors can view patients' evaluations and feedback to identify problems and make improvements in a timely manner.

[0015] The disease risk prediction module is used to build a disease risk prediction model based on the patient's basic information and their previous medical records using machine learning algorithms. This allows the patient to assess their risk of developing a specific disease in the future and provide personalized prevention recommendations.

[0016] The intelligent medication management module is used to automatically link patients' diagnosis results with prescription information, provide basic drug information query, and use time reminders to ensure that patients take their medications on time and in the correct dosage. It also uses drug interaction detection to avoid safety issues caused by improper drug combinations. At the same time, it collects patient feedback after medication use to provide reference for doctors to adjust treatment plans.

[0017] The consultation time calculation module includes:

[0018] Symptom description module, used to enable patients to describe their own symptoms in words;

[0019] The disease condition storage module is used to store the data entered by the disease condition description module;

[0020] A storage module is used to store various medical conditions and the consultation time required;

[0021] A comparison module, for comparing the data stored in the condition storage module with the data stored in the storage module;

[0022] The judgment module is used to judge whether the data stored in the condition storage module is similar to the data in the storage module. If so, the patient's consultation time will be obtained and uploaded to the client.

[0023] As a preferred solution of the medical pre-diagnosis system based on artificial intelligence and big data described in the present invention, the consultation time calculation module includes:

[0024] The first analysis module is used to analyze the patient's number according to the registration information obtained by the registration information acquisition module to obtain the patient's registration number.

[0025] As a preferred solution of the medical pre-diagnosis system based on artificial intelligence and big data described in the present invention, the consultation time calculation module further includes:

[0026] A calculation module, configured to calculate the consultation time before the registration number analyzed by the first analysis module;

[0027] The time statistics module is used to count the patient's consultation time based on the data calculated by the calculation module.

[0028] As a preferred solution of the medical pre-diagnosis system based on artificial intelligence and big data described in the present invention, the medical status module includes:

[0029] The reporting module is used to automatically report the patient on the client when the patient is consulting, so that other patients can understand the progress of the consultation;

[0030] The delay module is used to postpone the patient's consultation when the patient is late, and upload the postponed data to the client.

[0031] As a preferred solution of the medical pre-diagnosis system based on artificial intelligence and big data described in the present invention, the medical status module further includes:

[0032] The end module is used to automatically end the patient's consultation on the client when the patient finishes the consultation, so that other patients can understand the progress of the consultation;

[0033] The consultation record storage module is used to store the patient's consultation records for the convenience of doctors' later inquiries.

[0034] As a preferred solution of the medical pre-diagnosis system based on artificial intelligence and big data described in the present invention, the follow-up consultation module includes:

[0035] Follow-up data entry module, used to enter follow-up data into the client;

[0036] The second analysis module is used to analyze the doctor information based on the registration information obtained by the registration information acquisition module to obtain the doctor information.

[0037] As a preferred solution of the medical pre-diagnosis system based on artificial intelligence and big data described in the present invention, the follow-up consultation module further includes:

[0038] A transmission module, configured to upload the data in the medical consultation record storage module and the follow-up consultation data entry module to the doctor platform based on the data analyzed by the second analysis module;

[0039] Doctor login module, used for doctors to log in to the doctor platform.

[0040] As a preferred solution of the medical pre-diagnosis system based on artificial intelligence and big data described in the present invention, the follow-up consultation module further includes:

[0041] Integration module, used to organize and analyze the data transmitted by the transmission module;

[0042] The upload module is used to upload the data integrated by the integration module to the message list of the corresponding doctor;

[0043] The answer module is used to enable the doctor to answer the data entered by the follow-up data entry module in combination with the data stored in the consultation record storage module.

[0044] Compared with existing technologies:

[0045] By setting up a consultation time calculation module and a consultation time calculation module, it is possible to predict one's own consultation time based on the consultation time of other patients during pre-diagnosis, thereby avoiding the phenomenon of patients queuing for consultation to a certain extent, thereby reducing the number of people in the hospital; in addition, by setting up a follow-up consultation module, it is possible to conduct online follow-up consultations during follow-up consultations, which not only avoids the impact of offline follow-up consultations on the predicted consultation time, but also brings convenience to patients to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0048] The present invention provides a medical pre-diagnosis system based on artificial intelligence and big data. Figure 1 ;

[0049] It includes: a patient login module for patients to log in to the client; a registration module for patients to register on the registration platform and upload the registered data to the client; a consultation time calculation module for calculating and predicting the required consultation time according to the patient's condition; a registration information acquisition module for acquiring the information registered by the patient; a consultation time calculation module for calculating the consultation time before the registration number obtained by the registration information acquisition module; a display module for displaying the consultation time calculated by the consultation time calculation module; a consultation status module for displaying the patient's consultation status; a consultation record storage module for storing the patient's consultation record; a follow-up module for conducting online follow-up consultations with patients when they come for follow-up consultations; a patient evaluation feedback module for providing feedback to the doctor after the consultation. The system evaluates the service attitude, professional level and treatment effect of the patients, collects their opinions and suggestions, and provides a reference for the optimization of the system. Hospital managers and doctors can view the patients' evaluations and feedback, find problems in time and make improvements; the disease risk prediction module is used to use machine learning algorithms to build a disease risk prediction model based on the patient's basic information and his / her medical data in previous years, so as to assess the patient's risk of developing a specific disease in the future and give personalized prevention suggestions; the intelligent medication management module is used to automatically associate the patient's diagnosis results with prescription information, and provide basic drug information query, and can use the time reminder function to ensure that the patient takes the medicine on time and in the right amount, and avoid safety problems caused by improper drug combination through drug interaction detection. At the same time, it collects feedback from patients after taking the medicine to provide a reference for doctors to adjust the treatment plan.

[0050] Among them, setting up a patient evaluation feedback module can promote doctors to improve service quality and enhance patients' trust in the hospital. By analyzing patient feedback data, the system functions and hospital service processes can be continuously optimized to enhance patients' medical experience.

[0051] The disease risk prediction module can help patients understand their disease risks in advance, enhance disease prevention awareness, change bad living habits, reduce disease incidence, achieve a shift from disease treatment to disease prevention, and reduce the medical burden.

[0052] By setting up an intelligent medication management module, the accuracy and compliance of patients' medication can be improved, and medical accidents caused by improper medication can be reduced. By continuously tracking patients' medication status, doctors can be helped to optimize treatment plans and improve treatment effects.

[0053] The consultation time calculation module includes: a symptom description module, which is used to enable patients to describe their own symptoms in text; a condition storage module, which is used to store the data entered in the symptom description module; a storage module, which is used to store various conditions and their required consultation times; a comparison module, which is used to compare the data stored in the condition storage module with the data stored in the storage module; and a judgment module, which is used to judge whether the data stored in the condition storage module is similar to that in the storage module. If so, the patient's consultation time will be obtained and uploaded to the client.

[0054] The consultation time calculation module includes: a first analysis module, which is used to analyze the patient's number according to the registration information obtained by the registration information acquisition module to obtain the patient's registration number; a calculation module, which is used to calculate the consultation time before the registration number analyzed by the first analysis module; and a time statistics module, which is used to count the patient's consultation time according to the data calculated by the calculation module.

[0055] The consultation status module includes: a reporting module, which is used to automatically report the patient on the client when the patient is consulting, so that other patients can understand the progress of the consultation; a delay module, which is used to postpone the consultation when the patient is late, and upload the postponed data to the client; an ending module, which is used to automatically end the patient's consultation on the client when the patient ends, so that other patients can understand the progress of the consultation; a consultation record storage module, which is used to store the patient's consultation record for the doctor to query later.

[0056] The follow-up module includes: a follow-up data entry module, which is used to enter the follow-up data into the client; a second analysis module, which is used to analyze the doctor information based on the registration information obtained by the registration information acquisition module to obtain the doctor information; a transmission module, which is used to upload the data of the consultation record storage module and the follow-up data entry module to the doctor platform based on the data analyzed by the second analysis module; a doctor login module, which is used for the doctor to log in to the doctor platform; an integration module, which is used to organize and analyze the data transmitted by the transmission module; an upload module, which is used to upload the data integrated by the integration module to the message list of the corresponding doctor; and an answer module, which is used to enable the doctor to answer the data entered by the follow-up data entry module in combination with the data stored in the consultation record storage module.

[0057] During specific use, the operating steps of those skilled in the art are as follows:

[0058] Step 1: The patient logs in to the client through the patient login module. After logging in, the patient will register on the registration platform through the registration module and upload the registered data to the client;

[0059] Step 2: The patient uses the symptom description module to describe his or her own symptom in words. After the description is completed, the data entered in the symptom description module will be stored in the symptom storage module. After storage, the data stored in the symptom storage module will be compared with the data stored in the storage module through the comparison module. After comparison, the judgment module will determine whether the data stored in the symptom storage module is similar to the data in the storage module. If so, the patient's consultation time will be obtained and uploaded to the client;

[0060] Step 3: Obtain the patient's registration information through the registration information acquisition module;

[0061] Step 4: The first analysis module analyzes the patient's number according to the registration information obtained by the registration information acquisition module to obtain the patient's registration number. After obtaining the number, the calculation module calculates the consultation time before the number according to the registration number analyzed by the first analysis module. After the calculation, the time statistics module calculates the patient's consultation time according to the data calculated by the calculation module. After the statistics, the display module displays the consultation time calculated by the consultation time calculation module.

[0062] Step 5: When a patient is consulting, the reporting module can automatically report the patient on the client so that other patients can understand the progress of the consultation. At the same time, the ending module can automatically end the patient's consultation on the client when the patient finishes the consultation so that other patients can understand the progress of the consultation. In particular, when a patient is late for consultation, the delay module can postpone the consultation and upload the postponed data to the client. Afterwards, the consultation record storage module will store the patient's consultation record for the doctor's later query.

[0063] Step 6: The follow-up data is entered into the client through the follow-up data entry module. After entry, the doctor information will be analyzed by the second analysis module based on the registration information obtained by the registration information acquisition module to obtain the doctor information. After that, the data of the consultation record storage module and the follow-up data entry module will be uploaded to the doctor platform through the transmission module according to the data analyzed by the second analysis module. After uploading, the doctor will log in to the doctor platform through the doctor login module. After that, the data transmitted by the transmission module will be sorted and analyzed through the integration module. After sorting and analyzing, the data integrated by the integration module will be uploaded to the message list of the corresponding doctor through the upload module. After that, the doctor will answer the data entered by the follow-up data entry module in combination with the data stored in the consultation record storage module through the answer module.

[0064] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A medical pre-diagnosis system based on artificial intelligence and big data, characterized in that: include: Patient login module, used for patients to log in to the client; The registration module is used for patients to register on the registration platform and upload the registered data to the client; The consultation time calculation module is used to calculate and predict the required consultation time based on the patient's condition; The registration information acquisition module is used to obtain the patient's registration information; The consultation time calculation module is used to calculate the consultation time before the registration number obtained by the registration information acquisition module; A display module, used to display the consultation time calculated by the consultation time calculation module; The medical treatment status module is used to display the patient's medical treatment status; A medical consultation record storage module is used to store the patient's medical consultation records; Follow-up consultation module, which is used to conduct online follow-up consultations with patients when they return for follow-up consultations; The patient evaluation and feedback module is used to evaluate the doctor's service attitude, professional level, and treatment effect after the consultation. At the same time, it collects patients' opinions and suggestions to provide reference for system optimization. Hospital managers and doctors can view patients' evaluations and feedback to identify problems and make improvements in a timely manner. The disease risk prediction module is used to build a disease risk prediction model based on the patient's basic information and their previous medical records using machine learning algorithms. This allows the patient to assess their risk of developing a specific disease in the future and provide personalized prevention recommendations. The intelligent medication management module is used to automatically link patients' diagnosis results with prescription information, provide basic drug information query, and use time reminders to ensure that patients take their medications on time and in the correct dosage. It also uses drug interaction detection to avoid safety issues caused by improper drug combinations. At the same time, it collects patient feedback after medication use to provide reference for doctors to adjust treatment plans. The consultation time calculation module includes: Symptom description module, used to enable patients to describe their own symptoms in words; The disease condition storage module is used to store the data entered by the disease condition description module; A storage module is used to store various medical conditions and the consultation time required; A comparison module, for comparing the data stored in the condition storage module with the data stored in the storage module; The judgment module is used to judge whether the data stored in the condition storage module is similar to the data in the storage module. If so, the patient's consultation time will be obtained and uploaded to the client.

2. A medical pre-diagnosis system based on artificial intelligence and big data according to claim 1, characterized in that: The consultation time calculation module includes: The first analysis module is used to analyze the patient's number according to the registration information obtained by the registration information acquisition module to obtain the patient's registration number.

3. The medical pre-diagnosis system based on artificial intelligence and big data according to claim 2, characterized in that: The consultation time calculation module also includes: A calculation module, configured to calculate the consultation time before the registration number analyzed by the first analysis module; The time statistics module is used to count the patient's consultation time based on the data calculated by the calculation module.

4. The medical pre-diagnosis system based on artificial intelligence and big data according to claim 1, characterized in that: The medical status module includes: The reporting module is used to automatically report the patient on the client when the patient is consulting, so that other patients can understand the progress of the consultation; The delay module is used to postpone the patient's consultation when the patient is late, and upload the postponed data to the client.

5. The medical pre-diagnosis system based on artificial intelligence and big data according to claim 4, characterized in that: The medical status module also includes: The end module is used to automatically end the patient's consultation on the client when the patient finishes the consultation, so that other patients can understand the progress of the consultation; The consultation record storage module is used to store the patient's consultation records for the convenience of doctors' later inquiries.

6. The medical pre-diagnosis system based on artificial intelligence and big data according to claim 1, characterized in that: The follow-up consultation module includes: Follow-up data entry module, used to enter follow-up data into the client; The second analysis module is used to analyze the doctor information based on the registration information obtained by the registration information acquisition module to obtain the doctor information.

7. The medical pre-diagnosis system based on artificial intelligence and big data according to claim 6, characterized in that: The follow-up consultation module also includes: A transmission module, configured to upload the data in the medical consultation record storage module and the follow-up consultation data entry module to the doctor platform based on the data analyzed by the second analysis module; Doctor login module, used for doctors to log in to the doctor platform.

8. The medical pre-diagnosis system based on artificial intelligence and big data according to claim 7, characterized in that: The follow-up consultation module also includes: Integration module, used to organize and analyze the data transmitted by the transmission module; The upload module is used to upload the data integrated by the integration module to the message list of the corresponding doctor; The answer module is used to enable the doctor to answer the data entered by the follow-up data entry module in combination with the data stored in the consultation record storage module.

Images