CN112289437A - Diabetes adjuvant therapy cloud platform system based on edge computing framework - Google Patents
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Abstract
The invention provides a diabetes adjuvant therapy cloud platform system based on an edge computing framework, and relates to the field of public health. The diabetes adjuvant therapy cloud platform system based on the edge computing architecture comprises a hospital body, a community and a patient, wherein the hospital body is a hospital-community-patient, the patient is a terminal, the community, namely a community medical center, is an edge node, the hospital is a cloud center, the edge nodes form an edge computing layer, the edge nodes are mutually independent, and the edge nodes are interconnected with HIS, LIS, EMR and PACS data. Through edge calculation, data synchronization and model sharing between edge nodes and cloud nodes are achieved, data processing time delay, communication overhead and system overload risks are reduced, a clustering algorithm is introduced to identify diabetes patients according to classes, local sensitivity of data is highlighted on the basis of overall data characteristics, and clustered diabetes patients are enabled to be more similar and diagnosis and treatment modes of the clustered diabetes patients are enabled to have more reference values.
Description
Technical Field
The invention relates to the field of public health, in particular to a diabetes adjuvant therapy cloud platform system based on an edge computing architecture.
Background
The diabetes has the characteristics of long course time and complicated complications, the diabetes course monitoring and the complications management are used as keys for diabetes diagnosis, curative effect evaluation and treatment scheme adjustment at home and abroad, and the cloud platform can analyze and apply various diagnosis and treatment and physical examination data of the diabetes in a comprehensive manner on the diabetes course monitoring and the complications management, so that the cloud platform has natural advantages.
With the advent of 5G and the development of the internet of things (IoT), over 500 billions of terminal devices were networked by the year 2020. Considering the challenges of bandwidth consumption, network delay, data privacy protection and the like, under the scenes of huge data quantity, sensitivity to processing delay and sensitivity to data privacy of smart cities, smart medical treatment, smart manufacturing, smart homes and the like, more than half of data generated by terminal equipment needs to be analyzed and processed nearby at the edge side of the terminal equipment or the network, and a centralized cloud only processes computing tasks with high computing resource requirements and low real-time requirements.
The new pattern brought by the edge computing architecture utilizes the edge nodes to not only carry out task scheduling, storage, network management and the like on edge equipment, but also provide a set of complete safety isolation mechanism to prevent tasks dynamically scheduled on the same edge node from being influenced mutually; the central node belongs to a management platform and is responsible for managing a large number of small data centers formed by edge equipment, namely when a cluster formed by terminal equipment, an access gateway and the like is used as a small data center one by one, each edge node does not run a single task any more and becomes a general computing node capable of dynamically executing various tasks of the node to be scheduled.
By introducing edge calculation, the technical scheme of the customized service support environment can be directly constructed according to the abundance and the functional requirements of resources in different scenes, so that the resource occupation is reduced, and the response speed is increased. For example, a large number of clinical departments or hospital units directly access the cloud center, which may cause excessive load of the cloud center, network congestion, and long patient visit time, the system response efficiency can be improved through the edge nodes and the edge management platform, and the waiting time of the patient can be saved.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a diabetes adjuvant therapy cloud platform system based on an edge computing framework under the medical conjuncted background, and solves the problems that the existing diabetes patient has long treatment waiting time, and the existing system has high prediction accuracy and is to be improved in treating complex complications.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: an edge computing architecture-based diabetes adjuvant therapy cloud platform system comprises a hospital body, a community and a patient, wherein the hospital body is a hospital-community-patient, the patient is a terminal, the community, namely a community medical center, is an edge node, the hospital is a cloud center, the edge node forms an edge computing layer, each edge node is independent of each other, the edge nodes are interconnected with HIS, LIS, EMR and PACS data, the cloud center is matched with cloud nodes and the edge nodes, the cloud nodes form a cloud computing layer, the terminal-edge computing layer-cloud computing layer are interconnected through a network and address application is carried out through a webpage and an app, a doctor communication platform is additionally arranged in the webpage and the app, the doctor communication platform autonomously calls diabetes treatment related documents and experimental data and stores the documents and the experimental data in the cloud computing layer as required and can issue and communicate opinions, the communication opinions are autonomously stored by a doctor communication platform and can be called by a cloud computing layer;
the edge node acquires medical information of a patient, establishes diagnosis and treatment records for the diagnosis and treatment records of the patient, preprocesses the diagnosis and treatment records and the medical information after a data trigger platform in the edge node is triggered by filtering, uploads the diagnosis and treatment records and the medical information to a hospital cloud node, shares the diagnosis and treatment records in a cloud computing layer, transmits the information in the edge node by using QoS (quality of service) constraint, uses data gathered by the edge computing layer as a basic data set by the cloud computing layer, selects a proper calculation standard of the similarity of diabetes patients according to the data characteristics of the data set, reselects according to a main evaluation index meeting the standard or not as a contour coefficient, if not, then performs cluster analysis on the data set according to the calculation standard of the similarity, and then performs coupling and multi-parameter optimization by fully considering disease course change and internal relation through time sequence, machine learning or collaborative filtering aiming at the continuity of high-concurrence diabetes courses in a conjuncted body, the diabetes course trend and the auxiliary diagnosis and treatment prediction model are designed and shared, diagnosis and rehabilitation schemes are formulated by the edge nodes according to the diabetes course trend, the auxiliary diagnosis and treatment prediction model and the patient medical information designed by the cloud computing layer for the patient, relevant data for diagnosis and treatment of the patient are stored in the cloud nodes and participate in next model training, and the patient can acquire the diagnosis and treatment and rehabilitation schemes from the terminal layer and perform information interaction with medical staff of the edge nodes.
Preferably, the cloud computing layer is used for storing the diabetes big data processed by the edge nodes and training the data model.
Preferably, the edge computing layer is formed by interconnection of edge computing network devices with certain computing and storing capabilities, and is mainly used for receiving and processing medical data from the terminal layer, uploading medical information and diagnosis and treatment records to the cloud computing layer, and realizing global information sharing.
Preferably, the diabetes patient similarity calculation standard may be replaced with a standard corresponding to cosine similarity and euclidean distance.
Preferably, before the diagnosis and rehabilitation scheme is generated by the edge node, model verification needs to be performed on a diabetes course trend and an auxiliary diagnosis and treatment prediction model designed for a patient by using a public data set and an experimental environment.
Preferably, the edge node and the cloud center have unique static addresses, and the terminal patient registers for application in a webpage and an app through terminal equipment.
Preferably, the medical information is data information about diabetes acquired by the patient through one of a community medical center, a hospital and a self-detection mode.
Preferably, the diabetes course trend and auxiliary diagnosis and treatment prediction model is analyzed and checked by the staff of hospitals and scientific research institutions under the guidance of doctors with the authority of a doctor communication platform, and the analysis and check result is stored as a secondary item of the diabetes course trend and auxiliary diagnosis and treatment prediction model.
(III) advantageous effects
The invention provides a diabetes adjuvant therapy cloud platform system based on an edge computing architecture. The method has the following beneficial effects:
1. according to the invention, data synchronization and model sharing between the edge nodes and the cloud nodes are realized based on edge computing, so that data heterogeneity and system hardware difference of each data source are shielded, and data processing time delay, communication overhead and system overload risk are reduced.
2. According to the invention, a clustering algorithm is introduced to identify the diabetics according to classes, and the local sensitivity of data is highlighted on the basis of the overall characteristics of the data, so that the clustered diabetics are more similar, and the diagnosis and treatment modes of the clustered diabetics have more reference values.
3. According to the method, based on the clustering result and the data sensitivity, the coupling and multi-parameter optimization of the basic algorithm are realized, the algorithm parameter selection is ensured to meet the requirement of a data set, and the accuracy of model prediction is improved.
4. According to the invention, the doctor communication platform is used as the intermediate diabetes diagnosis communication platform, and the communication result is used as the basis of the system, so that the reliability of the auxiliary treatment of the special diabetes is improved.
Drawings
FIG. 1 is a diagram of a data architecture of the present invention;
fig. 2 is a model diagram of diagnosis and treatment prediction according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Example (b):
as shown in fig. 1-2, an embodiment of the present invention provides a diabetes adjuvant therapy cloud platform system based on an edge computing architecture, which includes a hospital body, a hospital-community-patient, a patient terminal, a community, namely, a community medical center, an edge node, a hospital cloud center, an edge computing layer composed of edge nodes, and each edge node being independent of each other, the edge nodes being interconnected with HIS, LIS, EMR, and PACS data, the cloud center matching cloud nodes and edge nodes, the cloud nodes constituting the cloud computing layer, the terminal-edge computing layer-cloud computing layer being interconnected using a network, and performing addressing application through a web page and an app, a physician communication platform being additionally provided in the web page and the app, the physician communication platform autonomously calling relevant documents and experimental data for diabetes therapy and storing, after obtaining rights, calling data in the cloud computing layer as needed and issuing communication opinions, the communication opinions are autonomously stored by the doctor communication platform and can be called by the cloud computing layer;
the edge nodes collect medical information of patients, establish diagnosis and treatment records for the diagnosis and treatment records of the patients, preprocess the diagnosis and treatment records and the medical information after the filtration triggering of a data triggering platform in the edge nodes, and upload the diagnosis and treatment records and the medical information to a hospital cloud node, the filtration triggering of the data triggering platform can prevent the data aggregation of the central node which is influenced by the data aggregation of the central node even possibly resulting in dirty data of the central node when the service in the edge computing node is failed or the data triggering is abnormal, a cloud node early warning mechanism is established in the cloud node to ensure the data triggering and the edge cloud synchronization, then the sharing is carried out in a cloud computing layer, the information transmission in the edge nodes uses QoS constraint, so as to shield the data heterogeneity and the system hardware difference of each data source, realize the processing and the fusion of the data of each edge node, reduce the risks of data processing delay, communication overhead, system overload and the like, and provide, the cloud computing layer takes data gathered by the edge computing layer as a basic data set, then selects a proper diabetes patient similarity calculation standard (cosine similarity and Euclidean distance) according to the data characteristics of the data set, takes main evaluation indexes whether the data set meets the standard as a contour coefficient, reselects if the data set does not meet the standard, then carries out cluster analysis on the data set according to the similarity calculation standard if the data set meets the standard, then carries out coupling and multi-parameter optimization by fully considering the course change and the internal relation according to time sequence, machine learning or collaborative filtering aiming at the continuity of high-complication diabetes course in a medical body, designs a diabetes course trend and an auxiliary diagnosis and treatment prediction model, shares the diabetes course trend and the auxiliary diagnosis and treatment prediction model, improves the clinical prediction and accuracy of diabetes and the effectiveness of diagnosis and treatment rehabilitation, assists clinical diagnosis and treatment and early warning of potential blood sugar risk crowds, and achieves the purpose of reducing blood sugar risk, the edge node makes diagnosis and rehabilitation schemes according to the diabetes course trend designed by the cloud computing layer for the patient, the auxiliary diagnosis and treatment prediction model and the patient medical information, stores relevant data for diagnosis and treatment of the patient in the cloud node, participates in next model training, the patient can acquire the diagnosis and treatment and rehabilitation schemes from the terminal layer and perform information interaction with medical staff of the edge node, and judges whether treatment corresponding to the diagnosis and treatment schemes improves the patient symptoms or not.
The cloud computing layer has the function of storing the diabetes big data processed by the edge nodes and training the data model.
The edge computing layer is formed by interconnection of edge computing network devices (such as switches, routers and common servers based on home-made processors) with certain computing and storage capacities, and is mainly used for receiving and processing medical data from the terminal layer and uploading medical information and diagnosis and treatment records to the cloud computing layer to achieve global information sharing.
Before the diagnosis and rehabilitation scheme is generated by the edge node, model verification needs to be carried out on the diabetes course trend and the auxiliary diagnosis and treatment prediction model designed for the patient by utilizing a public data set and an experimental environment, so that the correctness of the data integration and aggregation framework and the diabetes auxiliary diagnosis and treatment prediction model is proved.
The edge node and the cloud center are provided with unique static addresses, and the terminal patient registers and applies in a webpage and an app through the terminal device.
The medical information is data information about diabetes acquired by a patient through one of community medical centers, hospitals and self-detection modes.
The diabetes course trend and auxiliary diagnosis and treatment prediction model is analyzed and checked by the staff of hospitals and scientific research institutions under the guidance of doctors with the authority of a doctor communication platform, and the analysis and check result is stored as a secondary item of the diabetes course trend and auxiliary diagnosis and treatment prediction model.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A diabetes adjuvant therapy cloud platform system based on an edge computing framework comprises a hospital body and is characterized in that: the hospital body is a hospital-community-patient, the patient is a terminal, the community, namely a community medical center, is an edge node, the hospital is a cloud center, the edge node forms an edge computing layer, and each edge node is independent with each other, the edge nodes are interconnected with HIS, LIS, EMR and PACS data, the cloud center is matched with cloud nodes and edge nodes, the cloud nodes form a cloud computing layer, the terminal-edge computing layer-cloud computing layer are interconnected by using a network, and address application is carried out through a webpage and an app, a doctor communication platform is additionally arranged in the webpage and the app, the doctor communication platform automatically calls and stores relevant documents and experimental data for treating diabetes, after the authority is obtained, data in the cloud computing layer can be called as required and communication opinions can be issued, wherein the communication opinions are independently stored by the doctor communication platform and can be called by the cloud computing layer;
the edge node acquires medical information of a patient, establishes diagnosis and treatment records for the diagnosis and treatment records of the patient, preprocesses the diagnosis and treatment records and the medical information after a data trigger platform in the edge node is triggered by filtering, uploads the diagnosis and treatment records and the medical information to a hospital cloud node, shares the diagnosis and treatment records in a cloud computing layer, transmits the information in the edge node by using QoS (quality of service) constraint, uses data gathered by the edge computing layer as a basic data set by the cloud computing layer, selects a proper calculation standard of the similarity of diabetes patients according to the data characteristics of the data set, reselects according to a main evaluation index meeting the standard or not as a contour coefficient, if not, then performs cluster analysis on the data set according to the calculation standard of the similarity, and then performs coupling and multi-parameter optimization by fully considering disease course change and internal relation through time sequence, machine learning or collaborative filtering aiming at the continuity of high-concurrence diabetes courses in a conjuncted body, the diabetes course trend and the auxiliary diagnosis and treatment prediction model are designed and shared, diagnosis and rehabilitation schemes are formulated by the edge nodes according to the diabetes course trend, the auxiliary diagnosis and treatment prediction model and the patient medical information designed by the cloud computing layer for the patient, relevant data for diagnosis and treatment of the patient are stored in the cloud nodes and participate in next model training, and the patient can acquire the diagnosis and treatment and rehabilitation schemes from the terminal layer and perform information interaction with medical staff of the edge nodes.
2. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the cloud computing layer has the function of storing the diabetes big data processed by the edge nodes and training the data model.
3. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the edge computing layer is formed by interconnection of edge computing network equipment with certain computing and storing capabilities, and is mainly used for receiving and processing medical data from the terminal layer, uploading medical information and diagnosis and treatment records to the cloud computing layer, and realizing global information sharing.
4. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the similarity calculation standard of the diabetic patient can be replaced with the corresponding standard of cosine similarity and Euclidean distance.
5. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: before the edge node generates the diagnosis and rehabilitation scheme, model verification is carried out on the diabetes course trend and the auxiliary diagnosis and treatment prediction model designed for the patient by utilizing a public data set and an experimental environment.
6. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the edge nodes and the cloud center are provided with unique static addresses, and the terminal patient registers and applies in a webpage and an app through terminal equipment.
7. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the medical information is data information about diabetes acquired by a patient through one of community medical centers, hospitals and self-detection modes.
8. The cloud platform system for diabetes adjuvant therapy based on edge computing architecture of claim 1, wherein: the diabetes course trend and auxiliary diagnosis and treatment prediction model is analyzed and checked by the staff of hospitals and scientific research institutions under the guidance of doctors with the authority of a doctor communication platform, and the analysis and check result is stored as a secondary item of the diabetes course trend and auxiliary diagnosis and treatment prediction model.
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| CN113094497A (en) * | 2021-06-07 | 2021-07-09 | 华中科技大学 | Electronic health record recommendation method and shared edge computing platform |
| CN113113142A (en) * | 2021-04-09 | 2021-07-13 | 长沙理工大学 | Method for predicting diabetes risk by using intelligent analysis technology |
| CN114093505A (en) * | 2021-11-17 | 2022-02-25 | 山东省计算中心（国家超级计算济南中心） | Cloud-edge-end-architecture-based pathological detection system and method |
| CN115083601A (en) * | 2022-07-25 | 2022-09-20 | 四川省医学科学院·四川省人民医院 | Type 2diabetes auxiliary decision making system based on machine learning |
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