CN112133385A - Inquiry information acquisition and analysis system and acquisition and analysis method - Google Patents
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Abstract
The invention provides an inquiry information acquisition and analysis system and an inquiry information acquisition and analysis method. The storage analysis end generates a patient medical record by using the questionnaire information, feeds the patient medical record back to the patient end and the medical care end, and the medical care end receives the patient medical record of the storage analysis end to assist in completing the inquiry of the patient. The structured data of the deposit at the storage analysis end can be used as scientific research materials, and can be analyzed by methods such as artificial intelligence and the like to search new medical knowledge. The invention collects information by questionnaire survey, introduces machine learning and artificial intelligence into the processing of the corresponding database of the patient, constructs a corresponding analysis model and algorithm, comprehensively processes various indexes and conditions of the patient in the database through different dimensions, and rapidly collects materials, stores information, generates medical records, analyzes results, searches new knowledge and prompts various risks.
Description
Technical Field
The invention belongs to the technical field of medical software development, and particularly relates to an inquiry information acquisition and analysis system and an inquiry information acquisition and analysis method.
Background
In clinical practice, medical information of outpatients is acquired by inquiring patients mostly by doctors. However, the inquiry process between the doctor and the patient wastes time and labor, and meanwhile, because the medical resources are limited, the inquiry time allocated to the patient by the doctor is more rare, and most of the doctors can only grasp the key information to complete the inquiry link by key inquiry.
However, many important details are not known to the physician or the patient forgets to answer, resulting in details being hidden or even missing. These hidden details that are not appreciated may be important factors that are recognized by the prior medical knowledge but not appreciated by the patient, or that are not recognized by the prior medical knowledge but are relevant to diagnostic treatment, such as disease or prognosis.
Disclosure of Invention
The invention aims to provide an information acquisition and analysis system and an information acquisition and analysis method; the system and the method are particularly applied to the medical field, acquire medical information of patients based on a network questionnaire, intelligently analyze the physical conditions of the patients and generate medical history for storage, provide high-quality data for scientific research, are convenient for medical workers to inquire, and have high intelligence degree.
In order to solve the technical problems, the invention adopts the technical scheme that:
an inquiry information acquisition and analysis system comprises a patient end, a storage and analysis end and a medical care end,
the patient end is provided with a questionnaire, the patient end is connected with the storage analysis end through the Internet, and after the questionnaire is filled by the patient for consultation, the questionnaire is sent to the storage analysis end;
the storage analysis end receives and stores the questionnaire information of an inquiry patient, generates a patient medical record, feeds the patient medical record back to the patient end and the medical care end, precipitates the questionnaire information into structural data, performs intelligent analysis, provides data information and possible new knowledge for a medical scientific research system, and sends disease risk and treatment scheme recommendation information prompted by the existing knowledge and the new knowledge to the patient end and the medical care end to assist the completion of a medical link and promote the medical understanding of the patient;
the medical care end is connected with the storage analysis end through the Internet network, receives the patient medical history of the storage analysis end, and assists in completing the inquiry of the patient.
Further, the patient side is a smart phone, and the questionnaire is loaded on the smart phone in an application program or webpage form.
Furthermore, the patient side is a computer, and the questionnaire is loaded on the computer in a Web interface mode.
Further, the storage analysis end is in cloud storage, and the storage analysis end comprises a template capturing module or an intelligent analysis module, wherein the template capturing module extracts the questionnaire information to generate the patient medical record, or the intelligent analysis module generates the patient medical record.
Furthermore, the storage analysis end comprises an analysis model, the analysis model deposits data in a structured mode, various indexes and conditions of the patient in the database are comprehensively processed through different dimensions, scientific research materials are deposited, new medical knowledge is found, meanwhile, disease risk and treatment scheme recommendation information prompted by the existing knowledge and the new knowledge are sent to the patient end and the medical care end, completion of medical links is assisted, and medical understanding of the patient is promoted.
Further, the analytical model includes, but is not limited to, back propagation, Boltzmann machine, convolutional neural network, Hopfield network, multi-layered perceptron, radial basis function network, constrained Boltzmann machine, recurrent neural network, self-organizing map, spiking neural network, naive Bayes, Gaussian Bayes, polynomial Bayes, mean-dependency estimation, Bayesian belief network, Bayesian network, etc., classification and regression trees, iterative Dichtomosser 3, C4.5 algorithm, C5.0 algorithm, chi-square auto-interaction detection, decision stump, ID3 algorithm, random forest, SLIQ, Fisher linear discrimination, linear regression, logistic regression, multinomial logistic regression, naive Bayes classifier, sensing, support vector machine, generation countermeasure network, feedforward neural network, logistic learning machine, self-organizing map, prior algorithm, Eclat algorithm, FP-Grth algorithm, FP-Growth algorithm, Single interlocking clustering, conceptual clustering, BIRCH algorithm, DBSCAN algorithm, expectation maximization, fuzzy clustering, K-means algorithm, K-means clustering, mean shift algorithm, OPTICS algorithm, nearest neighbor algorithm, local anomaly algorithm, generative model, low density separation, graph-based method, joint training, Q learning, state-action-reward-state-action, DQN, policy gradient algorithm, model-based reinforcement learning, timing difference learning, deep belief network, deep convolutional neural network, deep recursive neural network, hierarchical time memory, deep boltzmann machine, stacked autoencoder, generative confrontation network.
Furthermore, the medical care end is a smart phone or a computer, and the questionnaire is displayed through an application program or a Web interface.
Furthermore, the invention also provides an inquiry information acquisition and analysis method, which utilizes the inquiry information acquisition and analysis system and comprises the following steps,
s1: the patient end acquires the information of the patient to be diagnosed through the questionnaire;
s2: the patient side sends the collected questionnaire information to a storage analysis side for storage and analysis;
s3: the storage analysis end generates a patient medical record, the storage analysis end sends the patient medical record to the patient end and the medical care end, and meanwhile, the storage analysis end stores the structured data of the questionnaire information and sends the structured data to an algorithm modeling module for data analysis;
s4: the medical care end obtains the patient medical record through the storage analysis end.
Further, the S1 includes the following steps,
s11: the patient side logs in an account according to the personal information;
s12: the patient receiving the questionnaire content in the account;
s13: and after the questionnaire is filled in, the questionnaire is confirmed by the patient end and then is sent to the storage analysis end.
Further, the S4 includes the following steps,
s41: the medical care end logs in according to personal information;
s42: the medical care end acquires the patient medical record;
s43: the medical care end completes the inquiry of the patient by means of the medical record of the patient.
The invention has the advantages and positive effects that:
1. according to the medical record generation system, the inquiry survey is carried out before the patient visits the doctor in the questionnaire survey mode, so that the medical record is automatically generated, the medical care flow of the medical care personnel and the patient is simplified, and the medical care efficiency is improved. Meanwhile, data are precipitated in a structured mode, machine learning and artificial intelligence are introduced into the processing of corresponding data of a patient, a corresponding analysis model and an algorithm are constructed, various indexes and conditions of the patient in the database are comprehensively processed through different dimensions, scientific research materials are precipitated, and possible new medical knowledge is discovered. Meanwhile, information such as disease risk, treatment scheme recommendation and the like prompted by existing knowledge and new knowledge is sent to the patient side and the medical care side, so that completion of a medical link is assisted, and medical understanding of the patient is promoted.
2. The invention collects questionnaire information in the form of web pages or small programs of mobile phone clients, systematically collects the traditional medical history contents such as current medical history, past history, surgical trauma history, marriage and childbirth history, family history, allergy history, epidemic area contact history, physical examination and auxiliary examination, and the biological and life experience conditions such as psychological assessment indexes, economic conditions, social relationships, occupational conditions and educational conditions related to patients in the form of tree-shaped deep questionnaires. Systematically and comprehensively collecting the traditional medical history, saving the inquiry time of medical personnel and assisting in completing the inquiry link. Meanwhile, the multi-factor deep learning can be carried out, and the possible relevance among the factors can be searched. Provides prompt for medical personnel, can be used as high-quality scientific research data for automatic analysis and storage, and is beneficial to medical progress.
3. The medical care end of the invention logs in through the personal account, checks the medical history of the patient, ensures that the privacy of the patient is not revealed, and simultaneously, the doctor can add interested problems by himself, and the questions can be used as specific scientific research and use and follow-up visit of the patient, thereby perfecting the content of the questionnaire.
4. The storage analysis end of the invention establishes subsequent machine learning, training method and training mechanism on the basis of the model so as to continuously optimize the accuracy of model prediction, and meanwhile, has the functions of dynamic analysis of the past data and prediction of the event to be generated, and has high automation degree and strong practicability.
Drawings
Fig. 1 is a schematic diagram of the operation of an embodiment of the present invention.
Fig. 2 is an overall flow chart of another embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Embodiments of the invention are further described below with reference to the accompanying drawings:
as shown in fig. 1, an inquiry information collecting and analyzing system includes a patient end, a storage and analysis end, and a medical care end.
The patient end is provided with a questionnaire, the patient end is connected with the storage analysis end through the Internet, and after the questionnaire is filled in by the patient for consultation, the questionnaire is sent to the storage analysis end.
And the storage analysis end receives and stores questionnaire information of a plurality of patients to be subjected to inquiry, and generates medical records. The information of the sediment questionnaire is structured data, and the data is intelligently analyzed, so that data and possible new knowledge are provided for a medical scientific research system. Meanwhile, information such as disease risk, treatment scheme recommendation and the like prompted by existing knowledge and new knowledge is sent to the patient side and the medical care side, so that completion of a medical link is assisted, and medical understanding of the patient is promoted.
The medical care end is connected with the storage analysis end through the Internet network, receives the patient medical history of the storage analysis end, and assists in completing the inquiry of the patient.
According to the embodiment, the inquiry survey is carried out before the patient visits the doctor in the questionnaire survey mode, so that medical records are automatically generated, the medical treatment process of medical staff and the patient is simplified, and the medical treatment efficiency is improved. Meanwhile, data are precipitated in a structured mode, machine learning and artificial intelligence are introduced into the processing of corresponding data of a patient, a corresponding analysis model and an algorithm are constructed, various indexes and conditions of the patient in the database are comprehensively processed through different dimensions, scientific research materials are precipitated, and possible new medical knowledge is discovered. Meanwhile, information such as disease risk, treatment scheme recommendation and the like prompted by existing knowledge and new knowledge is sent to the patient side and the medical care side, so that completion of a medical link is assisted, and medical understanding of the patient is promoted.
Specifically, the term "questionnaire" in this embodiment is a carrier for systematically recording the content to be investigated in the form of questions, including but not limited to text, audio, video, games, and the like. The questions are transmitted to the asked patients, the asked patients are pleased to answer, and the information needing to be understood is obtained through the obtained answers. Specifically, the questionnaire content provided in this embodiment includes traditional medical history, including but not limited to current medical history, past medical history, surgical trauma history, marriage and childbirth history, family history, allergy history, epidemic focus exposure history, physical examination, auxiliary examination, and the like. Also included are biological and life experience associated with the patient, including but not limited to psychological assessment metrics, economic, social relationships, occupational, educational, and the like.
The patient side is a device for acquiring the questionnaire content from the storage and analysis side after responding to the push message notification of the storage and analysis side, after the questionnaire is filled in by the inquiry patient, the patient side sends the questionnaire answer data and the internet behavior data of the inquiry patient to the storage and analysis side, and the storage and analysis side stores and analyzes the questionnaire answer data and the internet behavior data to obtain the final investigation information.
The patient end can be a smart phone, and the questionnaire is loaded on the smart phone in an application program form. Or the patient end is a computer, and the questionnaire is loaded on the computer in a Web interface mode. Preferably, the patient end is provided with a patient identity verification function for verifying the identity of the patient before the questionnaire information of the patient is collected, so that the patient can fill in the questionnaire by himself and the medical record result of the patient is directly recorded in the information base of the patient.
The execution main body of the storage analysis end is a server, and the server can be a remote background server or a server of a cloud platform, namely cloud storage. The cloud storage platform integrates multiple functions of software searching, downloading, using, managing, backing up and the like by adopting an application virtualization technology, builds a software resource, software application and a software service platform for a user, improves the current software obtaining and using mode, and brings a simple, smooth, convenient and quick brand new experience to the user. The background server may be implemented by using an independent server or a server cluster formed by a plurality of servers.
The input end of the storage analysis end receives and stores questionnaire information of each patient transmitted by the patient end, and the output end of the storage analysis end is respectively connected with the patient end and the medical care end. The questionnaire information is transmitted among the patient side, the medical care side and the server through a communication network which can be, but is not limited to, 3G, 4G and 5G, wifi.
The storage analysis end can extract questionnaire information through the template grabbing module to generate a patient medical record, and can also generate the patient medical record through the intelligent analysis module. The storage analysis end also comprises an analysis model, the analysis model deposits data in a structured form, comprehensively processes various indexes and conditions of the patient in the database through different dimensions, deposits scientific research materials, discovers new medical knowledge, and simultaneously sends disease risk and treatment scheme recommendation information prompted by the existing knowledge and the new knowledge to the patient end and the medical care end, so that the completion of medical links is assisted and the medical understanding of the patient is promoted. The analytical models include, but are not limited to, back propagation, Boltzmann machine, convolutional neural network, Hopfield network, multi-layer perceptron, radial basis function network, constrained Boltzmann machine, recurrent neural network, self-organizing map, spiking neural network, naive Bayes, Gauss Bayes, multi-term naive Bayes, mean-dependency assessment, Bayesian belief network, Bayesian network, etc., classification and regression trees, iterative Dichtomaser 3, C4.5 algorithm, C5.0 algorithm, chi-square auto-interaction detection, decision stump, ID3 algorithm, stochastic forest, SLIQ, Fisher linear discrimination, linear regression, logistic regression, multi-term logistic regression, Bayesian classifier, perception, support vector machine, generative confrontation network, feedforward neural network, logistic learning machine, self-organizing map, prior algorithm, Eclat algorithm, FP-Growth algorithm, single-connected cluster, clustering concept, and clustering, BIRCH algorithm, DBSCAN algorithm, expectation maximization, fuzzy clustering, K-means algorithm, K-means clustering, mean shift algorithm, OPTICS algorithm, nearest Neighbor (KNN) algorithm, local anomaly factor algorithm, generative model, low density separation, graph-based method, joint training, Q learning, state-action-reward-state-action, DQN, policy gradient algorithm, model-based reinforcement learning, timing difference learning, deep belief network, deep convolutional neural network, deep recursive neural network, hierarchical time memory, deep boltzmann machine, stacked autoencoder, generative confrontation network.
Through the form of the tree-shaped deep questionnaire, the traditional medical history is systematically and comprehensively collected, the inquiry time of medical personnel is saved, and the inquiry link is assisted to be completed. Meanwhile, the multi-factor deep learning can be carried out, and the possible relevance among the factors can be searched. Provides prompt for medical personnel, can be used as high-quality scientific research data for automatic analysis and storage, and is beneficial to medical progress.
The patient side is provided with an App application program or a Web application program, and the patient side realizes information interaction with the cloud server through the application program. And the application program of the patient side detects whether the communication connection between the patient side and the cloud server is normal or not in real time.
The patient side can log in by using a self-biometric identification mode, such as a fingerprint identification mode, a face identification mode and the like. Or the user can use the account name and the password to request login, and can use the mobile phone number and the password to request login and the like.
After receiving the login request of the patient side, the cloud server of the storage analysis side matches the user information stored in the cloud server, and if the matching is successful, the login request passes the verification; if the matching is unsuccessful, the login request is not verified, and the verification results of the cloud server are all fed back to the patient side.
If the login request is not verified, the patient end prompts the user whether to register, if the user selects to register, a registration page is displayed, the user fills information in the registration page, after the registration is completed, the patient end sends the registration information to the cloud server, and the cloud server updates the stored user information.
And the cloud server side can receive the questionnaire sent by the patient side only after the login request passes the verification.
The medical care end is a smart phone or a computer, and the questionnaire is displayed through an application program or a Web interface. The medical care end receives and stores the analysis result sent to the medical care end by the analysis end, and assists a doctor to complete the inquiry of the patient.
Specifically, an App application program or a Web application program is installed on the medical care end, and the medical care end realizes information interaction with the cloud server through the application program. The application program of the medical care end detects whether the communication connection between the medical care end and the cloud server is normal or not in real time.
The medical care end can log in by using a self biological identification mode, such as a fingerprint identification mode, a face identification mode and the like. Login can also be requested using an account name and password, login can be requested using a mobile phone number and password, and the like.
After receiving the login request of the medical care end, the cloud server of the storage analysis end is matched with the medical care information stored in the cloud server, and if the matching is successful, the login request passes the verification; if the matching is unsuccessful, the login request is not verified, and the verification results of the cloud server are all fed back to the medical care end.
If the login request is not verified, the medical care end prompts whether medical care is registered or not, if the medical care selects to register, a registration page is displayed, the medical care fills information in the registration page, after the registration is completed, the medical care end sends the registration information to the cloud server, and the cloud server updates the stored user information.
Only after the login request is verified, the cloud server side can send an analysis result to the medical care side, and the medical care side can send the perfection information to the cloud server so that the cloud server can store the perfection information and then send the perfection information to the scientific research system.
Preferably, as shown in fig. 2, the present invention further provides an inquiry information collecting and analyzing method, which utilizes the inquiry information collecting and analyzing system described above, and includes the following steps.
S1: the patient end collects the inquiry information of the inquiry patient through the questionnaire. Specifically, the server obtains the latest questionnaire task and the latest distribution strategy, and pushes the questionnaire task to the patient end meeting the task strategy conditions, wherein the patient end can be a computer, a mobile phone and the like of the patient. The questionnaire message is pushed to the patient device in an HTML page by methods such as notification, message push, and the like. Because the messages pushed by the router are based on the HTTP standard protocol, the access across operating systems and devices is supported.
S11: the patient side logs in the account according to the personal information, and can log in by using a self biological identification mode, such as a fingerprint identification mode, a face identification mode and the like. Or the user can use the account name and the password to request login, and can use the mobile phone number and the password to request login and the like.
S12: the patient end receives the questionnaire content in the account, namely after the patient responds to the questionnaire task pushed by the server, the patient end obtains the questionnaire content of the questionnaire from the server and can answer the questionnaire content. The questionnaire content includes, but is not limited to, traditional medical history including, but not limited to, current medical history, past medical history, surgical trauma history, marriage and childbirth history, family history, allergy history, epidemic area exposure history, physical examination, auxiliary examination, and biological and life experience conditions related to the patient. Biological and life experience associated with the patient including, but not limited to, psychological assessment metrics, economic, social, occupational, educational, etc.
S13: after the questionnaire is filled in, the questionnaire is confirmed by the patient end and then is sent to the storage analysis end.
S2: and the patient side sends the acquired questionnaire information to a storage analysis side for storage and analysis. The storage analysis end can establish a database by taking a hospital as a whole; a database can also be established by sharing the resources of a plurality of hospitals in a certain area; or a database with larger data volume and more comprehensive is established by taking the whole medical resources of the society as a whole.
S21: and the storage analysis end acquires questionnaire information of the patient end.
S22: and the storage analysis end performs model operation on the questionnaire information.
S23: a plurality of model parameters included in the questionnaire information are acquired.
S3: the storage analysis end generates a patient medical record, the storage analysis end sends the patient medical record to the patient end and the medical care end, and meanwhile, the storage analysis end sends questionnaire information to the scientific research system, so that high-quality scientific research data are provided for the scientific research system, and medical progress is facilitated.
S31: and the storage analysis end extracts questionnaire information through a template grabbing module of the storage analysis end to generate the patient medical record.
S32: and the storage analysis end generates the patient medical record after intelligent analysis is carried out through the intelligent analysis module.
S33: and the storage analysis end sends the patient medical history to the patient end and the medical care end.
S4: the medical care end obtains the medical history of the patient from the storage and analysis end.
S41: the medical care end logs in according to the personal information, and the medical care end can log in by using a self biological identification mode, such as a fingerprint identification mode, a face identification mode and the like. Login can also be requested using an account name and password, login can be requested using a mobile phone number and password, and the like.
S42: the medical care end obtains the patient case history, and the doctor who uses can add interesting problem by oneself, uses as specific scientific research and patient follow-up visit, improves the questionnaire content.
S43: the doctor perfects the inquiry of the patient by means of the patient medical record.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The invention has the advantages and positive effects that:
1. according to the medical record generation system, the inquiry survey is carried out before the patient visits the doctor in the questionnaire survey mode, so that the medical record is automatically generated, the medical care flow of the medical care personnel and the patient is simplified, and the medical care efficiency is improved. Meanwhile, data are precipitated in a structured mode, machine learning and artificial intelligence are introduced into the processing of corresponding data of a patient, a corresponding analysis model and an algorithm are constructed, various indexes and conditions of the patient in the database are comprehensively processed through different dimensions, scientific research materials are precipitated, and possible new medical knowledge is discovered. Meanwhile, information such as disease risk, treatment scheme recommendation and the like prompted by existing knowledge and new knowledge is sent to the patient side and the medical care side, so that completion of a medical link is assisted, and medical understanding of the patient is promoted.
2. The invention collects questionnaire information in the form of web pages or small programs of mobile phone clients, systematically collects the traditional medical history contents such as current medical history, past history, surgical trauma history, marriage and childbirth history, family history, allergy history, epidemic area contact history, physical examination and auxiliary examination, and the biological and life experience conditions such as psychological assessment indexes, economic conditions, social relationships, occupational conditions and educational conditions related to patients in the form of tree-shaped deep questionnaires. Systematically and comprehensively collecting the traditional medical history, saving the inquiry time of medical personnel and assisting in completing the inquiry link. Meanwhile, the multi-factor deep learning can be carried out, and the possible relevance among the factors can be searched. Provides prompt for medical personnel, can be used as high-quality scientific research data for automatic analysis and storage, and is beneficial to medical progress.
3. The medical care end of the invention logs in through the personal account, checks the medical history of the patient, ensures that the privacy of the patient is not revealed, and simultaneously, the doctor can add interested problems by himself, and the questions can be used as specific scientific research and use and follow-up visit of the patient, thereby perfecting the content of the questionnaire.
4. The storage analysis end of the invention establishes subsequent machine learning, training method and training mechanism on the basis of the model so as to continuously optimize the accuracy of model prediction, and meanwhile, has the functions of dynamic analysis of the past data and prediction of the event to be generated, and has high automation degree and strong practicability.
While one embodiment of the present invention has been described in detail, the description is only a preferred embodiment of the present invention and should not be taken as limiting the scope of the invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (10)
1. An inquiry information acquisition and analysis system is characterized in that: comprises a patient end, a storage and analysis end and a medical care end,
the patient end is provided with a questionnaire, the patient end is connected with the storage analysis end through the Internet, and after the questionnaire is filled by the patient for consultation, the questionnaire is sent to the storage analysis end;
the storage analysis end receives and stores the questionnaire information of an inquiry patient, generates a patient medical record, feeds the patient medical record back to the patient end and the medical care end, precipitates the questionnaire information into structural data, performs intelligent analysis, provides data information and possible new knowledge for a medical scientific research system, and sends disease risk and treatment scheme recommendation information prompted by the existing knowledge and the new knowledge to the patient end and the medical care end to assist the completion of a medical link and promote the medical understanding of the patient;
the medical care end is connected with the storage analysis end through the Internet network, receives the patient medical history of the storage analysis end, and assists in completing the inquiry of the patient.
2. The system for collecting and analyzing inquiry information of claim 1, wherein: the patient side is a smart phone, and the questionnaire is loaded on the smart phone in an application program or webpage mode.
3. The system for collecting and analyzing inquiry information of claim 1, wherein: the patient side is a computer, and the questionnaire is loaded on the computer in a Web interface mode.
4. The system for collecting and analyzing inquiry information according to any one of claims 1 to 3, wherein: the storage analysis end is in cloud storage and comprises a template grabbing module or an intelligent analysis module, and the template grabbing module extracts the questionnaire information to generate the patient medical record or generates the patient medical record through the intelligent analysis module.
5. The system for collecting and analyzing inquiry information according to any one of claims 1 to 3, wherein: the storage analysis end comprises an analysis model, the analysis model deposits data in a structured mode, various indexes and conditions of the patient in the database are comprehensively processed through different dimensions, scientific research materials are deposited, new medical knowledge is found, meanwhile, disease risk and treatment scheme recommendation information prompted by the existing knowledge and the new knowledge are sent to the patient end and the medical care end, completion of medical links is assisted, and medical understanding of the patient is promoted.
6. The system of claim 5, wherein the interrogation information collection and analysis system comprises: the analytical models include, but are not limited to, back propagation, Boltzmann machine, convolutional neural network, Hopfield network, multi-layer perceptron, radial basis function network, constrained Boltzmann machine, recurrent neural network, self-organizing map, spiking neural network, naive Bayes, Gauss Bayes, multi-term naive Bayes, mean-dependency assessment, Bayesian belief network, Bayesian network, etc., classification and regression trees, iterative Dichtomaser 3, C4.5 algorithm, C5.0 algorithm, chi-square auto-interaction detection, decision stump, ID3 algorithm, linear discrimination for random forests, SLIQ, Fisher, linear regression, logistic regression, multi-term logistic regression, naive classifier, sensing, support vector machine, generative confrontation network, neural network, logistic learning machine, self-organizing map, prior algorithm, Eclat algorithm, FP-Growth algorithm, single-connected cluster lock algorithm, etc, Concept clustering, BIRCH algorithm, DBSCAN algorithm, expectation maximization, fuzzy clustering, K-means algorithm, K-means clustering, mean shift algorithm, OPTICS algorithm, nearest neighbor algorithm, local anomaly algorithm, generative model, low density separation, graph-based method, joint training, Q learning, state-action-reward-state-action, DQN, policy gradient algorithm, model-based reinforcement learning, sequential difference learning, deep belief network, deep convolutional neural network, deep recursive neural network, hierarchical time memory, deep boltzmann machine, stacked autoencoder, generative confrontation network.
7. The system for collecting and analyzing inquiry information according to any one of claims 1 to 3, wherein: the medical care end is a smart phone or a computer, and the questionnaire is displayed through an application program or a Web interface.
8. An inquiry information collecting and analyzing method using the inquiry information collecting and analyzing system according to any one of claims 1 to 7, characterized in that: comprises the following steps of (a) carrying out,
s1: the patient end acquires the information of the patient to be diagnosed through the questionnaire;
s2: the patient side sends the collected questionnaire information to a storage analysis side for storage and analysis;
s3: the storage analysis end generates a patient medical record, the storage analysis end sends the patient medical record to the patient end and the medical care end, and meanwhile, the storage analysis end stores the structured data of the questionnaire information and sends the structured data to an algorithm modeling module for data analysis;
s4: the medical care end obtains the patient medical record through the storage analysis end.
9. The method for collecting and analyzing inquiry information of claim 8, wherein: the S1 includes the steps of,
s11: the patient side logs in an account according to the personal information;
s12: the patient receiving the questionnaire content in the account;
s13: and after the questionnaire is filled in, the questionnaire is confirmed by the patient end and then is sent to the storage analysis end.
10. The method for collecting and analyzing inquiry information according to claim 8 or 9, wherein: the S4 includes the steps of,
s41: the medical care end logs in according to personal information;
s42: the medical care end acquires the patient medical record;
s43: the medical care end completes the inquiry of the patient by means of the medical record of the patient.
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