A Smart Cloud Service Method and System for Clinical Laboratory Testing for the Elderly

Abstract

This invention provides a smart cloud service method and system for clinical laboratory testing for the elderly, relating to the field of smart healthcare technology. The method includes: acquiring chronic disease laboratory data of the elderly; performing entity feature set mining on the chronic disease laboratory data to obtain the correlation between clinical aging laboratory indicators of different types of chronic diseases; constructing a chronic disease knowledge graph and a corresponding digital twin knowledge graph based on the correlation; configuring corresponding abnormal values ​​for indicators of each type of chronic disease in the digital twin knowledge graph to obtain a complete digital twin knowledge graph, and deploying it to a cloud server; collecting clinical laboratory data of aging patients and uploading it to the cloud server; and performing personalized interpretation of the clinical laboratory data based on the complete digital twin knowledge graph, outputting the interpretation results of the chronic disease type and corresponding abnormal indicators of the aging patients.

Description

Technical Field

[0001] This invention relates to the field of smart healthcare technology, and in particular to a smart cloud service method and system for clinical testing of the elderly. Background Technology

[0002] Clinical laboratory testing, as a core tool for screening, diagnosing, and monitoring chronic diseases in the elderly, occupies an important position in the proactive health service system. Research into the correlation between laboratory indicators related to chronic diseases in the elderly and disease mechanisms is deepening, and smart healthcare technologies based on knowledge graphs, digital twins, and cloud computing are gradually being implemented, providing a favorable technological environment and application support for the intelligent, standardized, and homogenized development of clinical laboratory testing.

[0003] Existing clinical testing technologies and service models are continuously being optimized to meet the needs of elderly patients with chronic diseases. Various types of testing equipment, detection methods, and quality control systems are developing in parallel. Work such as regional mutual recognition of test results and improvement of laboratory quality is progressing steadily. Related technologies play a positive role in elucidating the patterns of chronic disease changes, standardizing testing procedures, and supporting clinical decision-making. They are of great significance in improving the accessibility of health services for the elderly and promoting the downward flow of high-quality medical resources.

[0004] However, the existing clinical testing system still has room for improvement in the mining of early warning indicators for aging and chronic diseases and the correlation analysis of multi-dimensional indicators. At the same time, there are differences in testing methods, reference ranges and quality control standards among different institutions, and the homogenization of test results needs to be further improved. Summary of the Invention

[0005] To address the existing shortcomings in the clinical testing system, such as the lack of room for improvement in the mining of early warning indicators for aging and chronic diseases and the analysis of multi-dimensional correlations between indicators, as well as the differences in testing methods, reference ranges, and quality control standards among different institutions, and the need to further improve the homogeneity of test results, this invention provides a smart cloud service method and system for clinical testing of the elderly.

[0006] The technical solutions provided by the embodiments of the present invention are as follows: The first aspect of this invention provides a smart cloud service method for clinical laboratory testing for the elderly, comprising: S1: Obtain medical data on chronic diseases in the elderly; S2: Perform entity feature set mining on chronic disease laboratory medical data to obtain the correlation between clinical aging test indicators of different types of chronic diseases; S3: Based on the relationships, construct a knowledge graph of chronic diseases and a corresponding digital twin knowledge graph; S4: After configuring corresponding outlier values ​​for each type of chronic disease in the digital twin knowledge graph, the complete digital twin knowledge graph is obtained and deployed to the cloud server; S5: Collect clinical laboratory data from elderly patients and upload it to the cloud server; S6: Based on a complete digital twin knowledge graph, perform personalized interpretation of clinical test data for chronic diseases, and output the interpretation results of chronic disease types and corresponding abnormal indicators for aging patients.

[0007] A second aspect of the present invention provides a smart cloud service system for clinical laboratory testing for the elderly, comprising: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the smart cloud service method for clinical testing of the elderly as described in the first aspect.

[0008] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the smart cloud service method for clinical testing of the elderly as described in the first aspect.

[0009] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: This invention addresses the need for improvements in the existing clinical testing system regarding the mining of aging-chronic disease early warning indicators and the analysis of multi-dimensional indicator correlations. It strengthens the correlation analysis and early warning applications among testing indicators by mining entity feature sets from chronic disease testing data and constructing a chronic disease knowledge graph and a digital twin knowledge graph. To address the issues of differences in testing methods, reference ranges, and quality control standards among different institutions, resulting in low homogeneity of results, this invention configures outlier values ​​for indicators and deploys the complete digital twin knowledge graph to a cloud server, unifying data application standards. Then, based on the cloud-based graphs, it intelligently interprets clinical testing data, outputting standardized chronic disease types and abnormal indicator results, thus resolving existing shortcomings on a one-to-one basis. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a smart cloud service method for clinical laboratory testing for the elderly, provided as an embodiment of the present invention.

[0012] Figure 2 This is a block diagram of a smart cloud service platform for clinical testing of the elderly, provided as an embodiment of the present invention.

[0013] Figure 3This is a system block diagram of a clinical testing system provided in an embodiment of the present invention.

[0014] Figure 4 This is a schematic diagram of the structure of a smart cloud service system for clinical testing of the elderly, provided in an embodiment of the present invention. Detailed Implementation

[0015] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0016] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0017] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0018] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] Reference manual attached Figure 1 The diagram shows a flowchart of a smart cloud service method for clinical testing of the elderly provided by an embodiment of the present invention.

[0021] This invention provides a method for providing smart cloud services for clinical laboratory testing for the elderly. This method can be implemented by a smart cloud service device for clinical laboratory testing for the elderly, which can be a terminal or a server. The processing flow of the smart cloud service method for clinical laboratory testing for the elderly may include the following steps: S1: Obtain medical data on chronic diseases in the elderly.

[0022] Chronic disease laboratory medical data refers to the collection of clinical laboratory data related to chronic diseases in the elderly.

[0023] Optionally, the chronic disease testing data specifically includes: types of aging-related chronic diseases and corresponding clinical aging testing indicators for these chronic disease types.

[0024] Clinical aging test indicators include chronic disease symptoms, chronic disease biomarkers, abnormality indicators, and corresponding abnormality test data and chronic disease medications.

[0025] Among them, clinical aging test indicators refer to test items used to assess the aging status of the elderly and the occurrence and development of chronic diseases, while chronic disease biomarkers refer to molecular, tissue, cellular or physiological state indicators that appear in or outside an organism under the influence of chronic disease characteristics.

[0026] It should be noted that chronic disease biomarkers refer to molecular, tissue, cellular, or physiological state indicators that appear in or outside an organism under the influence of chronic disease characteristics, and play an important role in the prevention and treatment of chronic diseases.

[0027] For example, common biomarkers for chronic diseases include biochemical indicators such as blood glucose, blood lipids, and blood pressure; genetic markers such as gene mutations or variations associated with specific diseases; and cellular markers such as changes in cell surface or internal structure.

[0028] S2: Perform entity feature set mining on chronic disease laboratory medical data to obtain the correlation between clinical aging test indicators of different types of chronic diseases.

[0029] Among them, entity feature set mining refers to the process of extracting clinically significant feature entities from medical data, and association refers to the logical correspondence between different types of chronic diseases and clinical aging test indicators.

[0030] In one possible implementation, S2 specifically includes sub-steps S201 and S202: S201: By using association rule mining algorithms, feature set mining is performed on chronic disease testing medical data to obtain chronic disease-related entity features.

[0031] Among them, association rule mining algorithm is a data mining method used to discover certain relationships or rules between items in a dataset. Chronic disease related entity features refer to feature entities extracted from chronic disease medical data that are related to chronic diseases.

[0032] Specifically, association rule mining algorithms achieve their goals through a series of steps: First, the raw data is preprocessed, including data cleaning, transformation, and standardization. Then, specific data structures and algorithms, such as frequent pattern trees, are used to effectively store and access the data. Next, the association degree or support between different feature dimensions is calculated to identify frequent itemsets. Finally, metrics such as confidence and lift are used to evaluate the strength and effectiveness of the association rules.

[0033] In one possible implementation, S201 specifically includes sub-steps S2011 to S2013: S2011: Construct a training feature set corresponding to the entity feature set.

[0034] The training feature set is the set of feature data used to train the AI ​​model.

[0035] S2012: Based on the training feature set, perform unsupervised model training to generate an unsupervised AI model.

[0036] Unsupervised model training is a method of training a model by discovering the inherent structure and patterns of data without labeled data. Unsupervised AI models refer to artificial intelligence models trained through unsupervised learning algorithms.

[0037] S2013: Using an unsupervised AI model, feature mining is performed on the data features of various dimensions in chronic disease testing medical data to obtain the features of chronic disease-related entities.

[0038] Specifically, the data features across various dimensions include chronic disease symptoms, biomarkers, outlier indicators, examination data, and medication information.

[0039] Furthermore, the unsupervised AI model simultaneously performs mining operations on the above-mentioned multiple types of data features, and can automatically extract effective feature entities without relying on manual annotation.

[0040] For example, when diabetes-related test data is input, the model can automatically extract various chronic disease-related entity features such as blood glucose levels, blood lipid levels, and complication symptoms.

[0041] S202: Based on the Apriori algorithm, explore the correlation between chronic disease-related indicators of different types of chronic diseases.

[0042] Among them, the Apriori algorithm is an association rule mining algorithm based on frequent itemsets.

[0043] Specifically, the basic steps of the Apriori algorithm include: First, defining a minimum support threshold. Then, scanning the entire dataset to find all frequent 1-itemsets. Next, generating candidate (k+1)-itemsets from the frequent k-itemsets, and filtering them through join and pruning steps. Then, scanning the dataset again to calculate the support of the candidate (k+1)-itemsets and finding the frequent (k+1)-itemsets. Finally, repeating the above process until no new frequent itemsets are found, and generating association rules from the frequent itemsets.

[0044] It should be noted that those skilled in the art can set the minimum support threshold according to actual needs, and this invention does not limit it.

[0045] In this embodiment of the invention, entity feature extraction and indicator association analysis are achieved through association rule mining, unsupervised AI models and Apriori algorithm, which can automatically and efficiently mine potential associations, improve the accuracy of identifying logical relationships between indicators and enhance the value of data utilization.

[0046] S3: Based on the relationships, construct a knowledge graph of chronic diseases and a corresponding digital twin knowledge graph.

[0047] Among them, the chronic disease knowledge graph is a knowledge base that organizes the relationship between chronic diseases and test indicators in the form of a graph structure, while the digital twin knowledge graph is a visualized knowledge graph model built in virtual space that reflects the dynamic relationship between chronic diseases and test indicators.

[0048] In one possible implementation, S3 specifically includes sub-steps S301 and S302: S301: Based on the entity and logical relationships in the association, construct the association knowledge graph between the indicator features of different types of chronic diseases to obtain the chronic disease knowledge graph.

[0049] Logical relevance refers to the relational connection characteristics between entities.

[0050] S302: Using digital twin technology, a three-dimensional visualization model of the chronic disease knowledge graph is created to generate a digital twin knowledge graph that reflects the dynamic relationship between chronic diseases and clinical aging test indicators.

[0051] Digital twin technology is a simulation technology that integrates physical models and data to complete entity mapping in virtual space.

[0052] It should be noted that the basic steps for constructing a digital twin knowledge graph are as follows: First, determine the construction goal and domain. Then, collect various types of domain-related data and perform cleaning, integration, and standardization. Next, construct a digital twin model based on the collected data. Then, extract and represent knowledge based on the digital twin model. Next, organize the formally represented knowledge into a knowledge graph. Finally, optimize and evaluate the constructed knowledge graph and apply it to real-world scenarios.

[0053] In this embodiment of the invention, a chronic disease knowledge graph and a digital twin knowledge graph are constructed based on the relationship, so as to realize the visualization and dynamic mapping of indicators, clearly show the relationship between chronic diseases and test indicators, and improve the efficiency of data management and access.

[0054] S4: After configuring corresponding outlier values ​​for each type of chronic disease in the digital twin knowledge graph, the complete digital twin knowledge graph is obtained and deployed to the cloud server.

[0055] Among them, outlier values ​​refer to the standard values ​​used in clinical diagnostic criteria to determine whether a test indicator is abnormal; a complete digital twin knowledge graph refers to a digital twin knowledge graph configured with outlier values; and a cloud server refers to a server platform that provides computing resources and data storage services through a network.

[0056] In this embodiment of the invention, by configuring outlier values ​​of indicators and deploying them in the cloud, and combining them with a multi-model database to achieve hierarchical storage, a unified standard and reliable support for test data is provided, which facilitates cloud access and quality verification.

[0057] The following are included after S4 and before S5: In the cloud server, a multi-modal database is built that is linked to the complete digital twin knowledge graph.

[0058] A multi-model database consists of multiple database nodes constructed in a tree structure.

[0059] A database node includes a parent node database and multiple child node databases associated with the parent node database.

[0060] Among them, multi-model database refers to a database system that supports the storage of multiple data types, tree structure is a data organization method with hierarchical relationships, and database node refers to the logical unit in the database used to store data.

[0061] It should be noted that if this solution is used for clinical verification of laboratory quality in chronic disease testing, it can employ a multi-modal database for equilateral grouping. Through data storage, big data analysis, data mining, and other technologies, statistical methods can be used to complete multi-indicator evaluation and establish a new model for laboratory quality control management and evaluation. Anomaly detection algorithms can be used to identify laboratories with abnormal quality status. Through the integration of hardware and software in an intelligent system, a quality control comparison platform and a unified and standardized evaluation standard system can be established to achieve continuous monitoring and improvement of laboratory quality.

[0062] Anomaly detection algorithms are an important tool in the field of machine learning. They are mainly used to find points in a dataset that are significantly different from most data points. These points are usually called outliers or anomalies. Outliers may be caused by measurement errors, data input errors, fraudulent behavior, or special events. They have wide applications in data analysis, data mining, and machine learning.

[0063] Specifically, commonly used anomaly detection algorithms include statistical methods, distance-based methods, density-based methods, clustering-based methods, and machine learning-based methods. The selection of an algorithm is determined based on the data dimension, distribution, noise level, and anomaly type, and it needs to be optimized and verified to ensure the recognition effect.

[0064] Furthermore, identifying outliers in chronic disease data through anomaly detection algorithms requires sequentially completing the steps of data understanding, algorithm selection, data preprocessing, algorithm application, result interpretation and verification, and subsequent processing and application to achieve automated and intelligent anomaly detection.

[0065] Optionally, the multi-model database construction process specifically includes: Clinical testing data on aging-related diseases obtained from clinical laboratories are uploaded to the cloud server via clinical terminals.

[0066] Clinical terminals refer to the data acquisition and transmission equipment used in clinical laboratories, while clinical test data refers to the data obtained by clinical laboratories after testing patients.

[0067] Clinical test data is received and stored in a multi-model database via a cloud server.

[0068] The clinical test data were modally grouped to obtain multiple sets of clinical test datasets.

[0069] Modal grouping refers to the operation of dividing data into different categories based on the content attributes and storage requirements of the data.

[0070] Each group of clinical test datasets is stored in the corresponding database node of the multi-model database.

[0071] For example, data related to chronic disease types is stored in the parent node database, data related to chronic disease biomarkers is stored in the first child node database, and data related to anomaly indicators, corresponding examination data, and medication is stored in the second child node database.

[0072] Specifically, to perform clinical test quality verification on clinical test data using a digital twin knowledge graph, it is necessary to traverse the clinical test datasets of each parent and child node in the multi-model database according to the chronic disease type, input the data into the digital twin knowledge graph and match the associated features, use the associated features to compare and verify the clinical test content, and determine whether they are consistent. If they are consistent, output the test item qualified information; otherwise, output the quality verification unqualified information.

[0073] It should be noted that the clinical testing content includes data on various clinical testing indicators for chronic diseases. The indicator data can be standardized and verified through digital twin knowledge graphs to solve problems such as inconsistent testing methodologies, inconsistent equipment and reagents, differences in testing scope, differences in reference range, and lack of homogeneity in test results, thereby achieving inter-laboratory quality control within the laboratory.

[0074] For example, for chronic diseases such as diabetes, chronic kidney disease, cardiovascular disease, and hypertension, the corresponding test data can be input into a digital twin knowledge graph to obtain clinical characteristics and related characteristics, thereby determining whether laboratory test items are qualified, providing technical support and platform support for the mutual recognition of test results among medical institutions.

[0075] S5: Collect clinical test data from elderly patients and upload them to the cloud server.

[0076] Clinical laboratory data refers to the personal data obtained after clinical testing of elderly patients.

[0077] S6: Based on a complete digital twin knowledge graph, perform personalized interpretation of clinical test data for chronic diseases, and output the interpretation results of chronic disease types and corresponding abnormal indicators for aging patients.

[0078] Among them, "personal interpretation of chronic diseases" refers to the process of analyzing and judging chronic diseases by combining clinical test data of individual patients with knowledge graphs, and "abnormal indicators" refers to test indicators that exceed the normal reference range.

[0079] In one possible implementation, S6 specifically includes sub-steps S601 to S605: S601: Input clinical test data of aging patients into a complete digital twin knowledge graph.

[0080] S602: Based on the correlation between chronic disease types and clinical aging test indicators stored in the complete digital twin knowledge graph, match and identify clinical test data.

[0081] Among them, matching recognition refers to the process of comparing and matching input data with knowledge in the graph.

[0082] Specifically, the system compares the numerical values ​​and types of patient test data with the standard data in the atlas one by one to determine the degree of similarity.

[0083] It should be noted that the matching and identification results are directly used as the basis for subsequent determination of chronic disease type and identification of abnormal indicators.

[0084] For example, if a patient's blood glucose level exceeds the normal range in their test data, the system can match and identify it with diabetes-related indicators in the atlas.

[0085] S603: Based on the matching and identification results, determine the type of chronic disease corresponding to the aging patient.

[0086] S604: Identify abnormal indicators in clinical laboratory data based on outlier values ​​configured in the complete digital twin knowledge graph.

[0087] Specifically, the numerical values ​​of indicators in the patient's test data are compared with the corresponding abnormal values ​​to determine whether they exceed the limit range.

[0088] Furthermore, items that exceed the outlier range of the indicator will be automatically marked as abnormal indicators and included in the interpretation results.

[0089] For example, abnormal blood glucose levels are set as the upper limit of the standard. If a patient's blood glucose level is higher than this value in their test data, it will be identified as an abnormal indicator.

[0090] S605: Output the interpretation results of chronic disease types and abnormal indicators.

[0091] Reference manual attached Figure 2 The diagram shows a block diagram of a smart cloud service platform for clinical testing of the elderly provided by the present invention.

[0092] Specifically, Figure 2 The Smart Cloud Service Platform for Clinical Laboratory Testing for Middle-Aged and Elderly Individuals comprises a data acquisition terminal 310, a digital twin application platform 320, and a cloud server 330. The data acquisition terminal 310 is used to acquire chronic disease laboratory medical data from the elderly. The digital twin application platform 320 is used to perform entity feature mining, correlation analysis, construction of a chronic disease knowledge graph and a digital twin knowledge graph, and configuration of outlier values. The cloud server 330 is used to deploy the complete digital twin knowledge graph, providing data storage, computation, and result distribution services.

[0093] Furthermore, Figure 2 In terms of connectivity, the data acquisition terminal 310 communicates with the digital twin application platform 320, transmitting the collected chronic disease testing and medical data to the digital twin application platform 320. The digital twin application platform 320 communicates with the cloud server 330, deploying the completed digital twin knowledge graph to the cloud server 330. The cloud server 330 maintains communication with both the data acquisition terminal 310 and the digital twin application platform 320, enabling data interaction and service invocation.

[0094] It should be noted that, Figure 2 By employing a layered and modular design, the system decouples data collection, map construction, and cloud services, thereby improving its scalability and maintainability while ensuring efficient processing and intelligent service output of laboratory data for chronic diseases in the elderly.

[0095] In this embodiment of the invention, matching and anomaly detection are performed based on a complete digital twin knowledge graph, which can automatically output chronic disease types and abnormal indicators, improve interpretation efficiency and accuracy, and realize personalized intelligent analysis.

[0096] Optionally, after S6, it also includes: S7: Import the interpretation results into the visualization component of the digital twin application platform.

[0097] Among them, the digital twin application platform is a software platform that supports the operation and application of digital twin models, and the visualization component is a software module used for data visualization.

[0098] S8: Call the chart model in the visualization component, process the interpretation results, and generate visualized data.

[0099] Among them, chart models are algorithmic models used to convert data into chart forms, and visualized data refers to data formats suitable for visualization.

[0100] Specifically, the chart model performs a structured transformation of information such as chronic disease types, abnormal indicators, indicator values, and normal ranges.

[0101] Furthermore, visualized data offers an intuitive, clear, and easy-to-understand presentation, making it convenient for patients and medical staff to view.

[0102] For example, abnormal blood glucose indicators will be presented in the form of a comparative bar chart, clearly showing the difference between the measured value and the normal range.

[0103] S9: The visualized data is sent from the cloud server to the user's terminal for display.

[0104] Among them, the user terminal refers to the receiving and display terminal equipment used by elderly patients.

[0105] Reference manual attached Figure 3 The diagram shows a system block diagram of a clinical testing system provided by the present invention.

[0106] Specifically, Figure 3 It includes a digital twin application platform, a cloud server, a digital twin knowledge graph, clinical terminals, and user terminals. The digital twin application platform is used to build and configure the digital twin knowledge graph. The cloud server is used to deploy the digital twin knowledge graph and provide data interaction and computing services. The digital twin knowledge graph, an internal component of the cloud server, is used to store the correlation between chronic diseases and laboratory indicators, as well as outlier values. The clinical terminal is used to collect and upload clinical test data and receive test quality verification results. The user terminal is used to collect and upload patient clinical test data and receive chronic disease interpretation results.

[0107] Furthermore, in terms of inclusion, the cloud server contains a digital twin knowledge graph, which is a core functional component of the cloud server. In terms of connectivity, the digital twin application platform has a bidirectional communication connection with the cloud server for deploying the digital twin knowledge graph. Clinical terminals have a unidirectional connection with the cloud server for uploading clinical test data and receiving verification results. User terminals also have a unidirectional connection with the cloud server for uploading patient test data and receiving interpretation results. The digital twin knowledge graph is internally connected to the cloud server, providing core data support and computational logic for the cloud server.

[0108] It should be noted that, Figure 3 Through a hierarchical and modular connection design, efficient collection of clinical laboratory data, intelligent cloud processing, and multi-terminal result display have been achieved, improving the standardization and accessibility of laboratory services for elderly patients with chronic diseases and providing reliable support for clinical quality control and personal health management.

[0109] Reference manual attached Figure 4 The diagram shows a schematic of the structure of a smart cloud service system for clinical testing of the elderly provided by the present invention.

[0110] The present invention also provides a smart cloud service system 20 for clinical laboratory testing for the elderly, applied to the above-mentioned smart cloud service method for clinical laboratory testing for the elderly, comprising: Processor 201.

[0111] The memory 202 stores computer-readable instructions, which, when executed by the processor 201, implement the smart cloud service method for clinical testing of the elderly as described in the method embodiment.

[0112] The smart cloud service system 20 for clinical laboratory testing for the elderly provided by the present invention can execute the above-mentioned smart cloud service method for clinical laboratory testing for the elderly and achieve the same or similar technical effects. To avoid duplication, the present invention will not elaborate further.

[0113] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0114] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0115] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0116] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0117] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0118] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0119] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0121] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0124] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0125] This invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the smart cloud service method for clinical testing of the elderly as described in the method embodiment.

[0126] The present invention provides a computer-readable storage medium that can implement the steps and effects of the smart cloud service method for clinical testing of the elderly in the above-described method embodiments. To avoid repetition, the present invention will not repeat the details.

[0127] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0128] The following points need to be explained: (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0129] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0130] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0131] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart cloud service method for clinical laboratory testing for the elderly, characterized in that, include: S1: Obtain medical data on chronic diseases in the elderly; S2: Perform entity feature set mining on the chronic disease testing medical data to obtain the correlation between clinical aging test indicators of different types of chronic diseases; S3: Based on the aforementioned relationships, construct a chronic disease knowledge graph and a corresponding digital twin knowledge graph; S4: After configuring corresponding outlier values ​​for each type of chronic disease in the digital twin knowledge graph, a complete digital twin knowledge graph is obtained and deployed to a cloud server; S5: Collect clinical test data of elderly patients and upload them to the cloud server; S6: Based on the complete digital twin knowledge graph, perform personal interpretation of the clinical test data for chronic diseases, and output the interpretation results of the chronic disease type and corresponding abnormal indicators of the aging patients.

2. The intelligent cloud service method for clinical laboratory testing for the elderly according to claim 1, characterized in that, The specific medical data for chronic disease testing includes: types of aging-related chronic diseases and corresponding clinical aging testing indicators for these types of chronic diseases. The clinical aging test indicators include chronic disease symptoms, chronic disease biomarkers, abnormality indicators, and corresponding abnormality test data and chronic disease medications.

3. The intelligent cloud service method for clinical laboratory testing for the elderly according to claim 1, characterized in that, S2 specifically includes: S201: Using an association rule mining algorithm, feature set mining is performed on the chronic disease test medical data to obtain chronic disease-related entity features; S202: Based on the Apriori algorithm, explore the correlation between chronic disease-related indicators of different types of chronic diseases.

4. The intelligent cloud service method for clinical laboratory testing for the elderly according to claim 3, characterized in that, S201 specifically includes: S2011: Construct a training feature set corresponding to the entity feature set; S2012: Based on the training feature set, perform unsupervised model training to generate an unsupervised AI model; S2013: Using the unsupervised AI model, feature mining is performed on the data features of each dimension in the chronic disease examination medical data to obtain the features of the chronic disease-related entities.

5. The intelligent cloud service method for clinical laboratory testing for the elderly according to claim 1, characterized in that, S3 specifically includes: S301: Based on the entity and logical relationships in the aforementioned relationships, construct a knowledge graph of the relationships between the indicator features of different types of chronic diseases to obtain the chronic disease knowledge graph; S302: Using digital twin technology, perform three-dimensional visualization modeling on the chronic disease knowledge graph to generate the digital twin knowledge graph that reflects the dynamic relationship between chronic diseases and clinical aging test indicators.

6. The intelligent cloud service method for clinical laboratory testing for the elderly according to claim 1, characterized in that, The following is included after S4 and before S5: In the cloud server, a multi-modal database linked to the complete digital twin knowledge graph is constructed; The multi-model database includes multiple database nodes constructed according to a tree structure; The database node includes a parent node database and multiple child node databases associated with the parent node database.

7. The intelligent cloud service method for clinical laboratory testing for the elderly according to claim 6, characterized in that, The multi-model database construction process specifically includes: Clinical testing data related to aging diseases obtained from clinical laboratories are uploaded to the cloud server through clinical terminals; The cloud server receives and saves the clinical test data to the multi-model database. The clinical test data are modally grouped to obtain multiple sets of clinical test datasets; The clinical test datasets of each group are stored in the corresponding database nodes of the multi-model database.

8. The intelligent cloud service method for clinical laboratory testing for the elderly according to claim 1, characterized in that, S6 specifically includes: S601: Input the clinical test data of the aging patient into the complete digital twin knowledge graph; S602: Based on the correlation between chronic disease types and clinical aging test indicators stored in the complete digital twin knowledge graph, the clinical test data is matched and identified. S603: Based on the matching and identification results, determine the type of chronic disease corresponding to the elderly patient; S604: Based on the outlier values ​​of the indicators configured in the complete digital twin knowledge graph, identify the abnormal indicators in the clinical test data; S605: Output the interpretation results of the chronic disease type and the abnormal indicators.

9. The intelligent cloud service method for clinical laboratory testing for the elderly according to claim 1, characterized in that, Following S6, the following is also included: S7: Import the interpretation results into the visualization component of the digital twin application platform; S8: Call the chart model in the visualization component to process the interpretation results and generate visualization data; S9: The visualization data is sent from the cloud server to the user terminal for display.

10. A smart cloud service system for clinical laboratory testing for the elderly, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the smart cloud service method for clinical testing of the elderly as described in any one of claims 1 to 9.

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