Special arrhythmia disease screening system

Abstract

The invention provides an arrhythmia special disease screening system, and belongs to the technical field of medical information. The screening system comprises a plurality of data sources, a data acquisition module and a screening module, the plurality of data sources are used for providing different types of medical data related to arrhythmia; the data acquisition module is used for acquiring various medical data of a target object from the plurality of data sources by adopting a middle library mode, and sending the various medical data to the screening module; the screening module is used for processing various medical data of the target object by adopting a machine learning model to obtain a screening result of the target object, and the screening result comprises whether the target object suffers from arrhythmia or not and the type of the arrhythmia under the condition that the target object suffers from the arrhythmia. According to the technical scheme, the screening efficiency and accuracy of arrhythmia can be improved.

Description

Technical Field

[0001] This application relates to the field of medical information technology, and particularly to a special disease screening system for arrhythmia. Background Art

[0002] Currently, as one of the common cardiovascular diseases, early screening and effective management of arrhythmia are of great significance for improving the quality of life of patients and reducing medical costs. Traditional arrhythmia screening mainly relies on doctors' manual reading and judgment of electrocardiograms. This method is not only time-consuming and laborious, but also easily affected by personal experience, resulting in limited screening efficiency and accuracy. Summary of the Invention

[0003] An embodiment of this application provides a special disease screening system for arrhythmia, which can improve the screening efficiency and accuracy of arrhythmia. The technical solution is as follows:

[0004] On the one hand, a special disease screening system for arrhythmia is provided. The screening system includes: multiple data sources, a data acquisition module, and a screening module;

[0005] The multiple data sources are used to provide different types of medical data related to arrhythmia;

[0006] The data acquisition module is used to obtain various medical data of a target object from the multiple data sources in the manner of an intermediate library, and send the various medical data to the screening module;

[0007] The screening module is used to process the various medical data of the target object by using a machine learning model to obtain a screening result of the target object. The screening result includes whether the target object has arrhythmia and the type of arrhythmia suffered in the case of having arrhythmia.

[0008] In some embodiments, the multiple data sources are respectively deployed in a hospital information system, a laboratory test system, an imaging examination system, and an electrocardiogram center system. The hospital information system contains the medical record information and doctor's order information of the target object. The laboratory test system contains various medical examination results detected for the target object through the laboratory. The imaging examination system contains medical images collected for the target object through imaging equipment. The electrocardiogram center system includes at least one electrocardiogram data of the electrocardiogram signal and electrocardiogram of the target object.

[0009] In some embodiments, the data acquisition module is used to obtain various medical data of the target object from the multiple data sources in the manner of an intermediate library in response to an arrhythmia detection operation for the target object;

[0010] Alternatively, the data source for providing electrocardiogram data can automatically send the electrocardiogram data of the target object to the data acquisition module after collecting the electrocardiogram data of the target object. The data acquisition module is configured to, upon receiving the electrocardiogram data of the target object, obtain various medical data of the target object from the multiple data sources in the manner of an intermediate library.

[0011] In some embodiments, the screening module is further configured to, when the target object has arrhythmia, determine the lesion location on the target object that causes the arrhythmia based on various medical data of the target object.

[0012] In some embodiments, the screening module is further configured to, when the target object has arrhythmia, determine the grade of the arrhythmia suffered by the target object based on various medical data of the target object, where the grade is used to represent the severity of the arrhythmia.

[0013] In some embodiments, the screening module is configured to, when the target object has arrhythmia, determine the duration of the arrhythmia of the target object based on various medical data of the target object, and determine the grade of the arrhythmia suffered by the target object based on the duration, where the grade is positively correlated with the duration.

[0014] In some embodiments, the screening module is further configured to, when the target object has arrhythmia, determine the treatment method for the target object based on the type of arrhythmia suffered by the target object.

[0015] In some embodiments, the screening module is further configured to train the machine learning model based on the various medical data of multiple sample objects and the sample labels of the multiple sample objects, where the sample label of each sample object is used to indicate whether the sample object has arrhythmia and the type of arrhythmia suffered when having arrhythmia.

[0016] In some embodiments, the screening module is further configured to, when the target object does not have arrhythmia, predict the risk probability of the target object based on various medical data of the target object, where the risk probability is used to represent the probability that the target object will suffer from arrhythmia in a future time period.

[0017] In some embodiments, the screening system further includes a quality control detection module, and the quality control detection module is configured to determine the accuracy of the screening result for the target object based on quality control punctuation, where the quality control punctuation is used to indicate the conditions to be met when determining arrhythmia.

[0018] In some embodiments, the quality control detection module is further configured to determine the accuracy rate of the machine learning model in the screening module based on the accuracy of the screening results of multiple objects.

[0019] In some embodiments, the screening module is further configured to adjust the model parameters in the machine learning model with the goal of improving the accuracy rate of the machine learning model.

[0020] In some embodiments, the screening system further includes an information management platform, and the information management platform is configured to display the output results of each module in the screening system.

[0021] The arrhythmia special disease screening system provided by the embodiments of the present application, in the process of detecting whether a target object has arrhythmia, can integrate various medical data related to arrhythmia of the target object in multiple data sources through the data acquisition module, and transmit the various medical data to the screening module for automatic screening, improving the screening efficiency of arrhythmia. And the screening module uses a machine learning model to process the various medical data to determine whether the target object has arrhythmia and the type of arrhythmia suffered in the case of having arrhythmia, avoiding the subjective influence in the artificial diagnosis process, and being beneficial to improving the accuracy of screening arrhythmia. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0023] Figure 1 is a framework diagram of an arrhythmia special disease screening system provided by an embodiment of the present application;

[0024] Figure 2 is a framework diagram of another arrhythmia special disease screening system provided by an embodiment of the present application;

[0025] Figure 3 is a structural block diagram of a terminal provided by an embodiment of the present application;

[0026] Figure 4 is a structural schematic diagram of a server provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To make the objectives, technical solutions, and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the drawings.

[0028] In this application, terms such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions. It should be understood that there is no logical or chronological dependency between "first", "second", and "nth", nor are the quantity and execution order limited.

[0029] In this application, the term "at least one" means one or more, and the meaning of "multiple" means two or more.

[0030] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.), and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions. For example, the medical data involved in this application are all obtained under full authorization.

[0031] Figure 1 It is a framework diagram of an arrhythmia special disease screening system provided according to an embodiment of this application. Refer to Figure 1 , the screening system includes multiple data sources, a data acquisition module, and a screening module. The multiple data sources, the data acquisition module, and the screening module can be directly or indirectly connected through wired or wireless communication methods, and this application does not limit this here. The multiple data sources, the data acquisition module, and the screening module in the screening system will be introduced in turn below.

[0032] In the embodiment of this application, multiple data sources are used to provide different types of medical data related to arrhythmia. The types of medical data provided by different data sources are different. That is, each piece of medical data collected during the detection of the target object (user or patient) is stored in different data sources. The embodiment of this application does not limit the data stored in the multiple data sources.

[0033] Continue to refer to Figure 1 , in some embodiments, the multiple data sources are respectively deployed in the hospital information system, the laboratory test system, the imaging examination system, and the electrocardiogram center system. That is, the multiple data sources are respectively the medical data stored in the hospital information system, the laboratory test system, the imaging examination system, and the electrocardiogram center system.

[0034] Among them, the Hospital Information System (HIS) generally covers all hospital operations and the entire process of operations. The types of data collected are very rich and can include personal information of the target object (such as name, gender, age, contact information, etc.), medical record information (such as chief complaint (i.e., the main symptoms of the target object), current medical history, past medical history, family history, etc.), doctor's order information (such as drug treatment plan, examination and inspection applications, etc.), cost information (such as registration fee, examination fee, treatment fee, drug fee, etc.), and progress notes. For an object with arrhythmia, the hospital information system usually records the chief complaint symptoms of the object (such as palpitations, dizziness, chest tightness, etc.), past arrhythmia medical history, and family history of arrhythmia diseases in the medical record information. At the same time, the hospital information system also records the application forms for arrhythmia-related examinations prescribed by doctors (such as electrocardiogram, ambulatory electrocardiogram, echocardiogram, etc.) and the doctor's order information of treatment drugs. The embodiments of the present application do not limit this. Correspondingly, when detecting whether a target object has an arrhythmia disorder, the hospital information system can provide the above-mentioned medical record information and doctor's order information related to arrhythmia.

[0035] The Laboratory Information System (LIS) mainly collects data related to laboratory tests, including test application information (such as basic information of the target object, test items, ordering doctor, etc.), test sample information (such as sample type, collection time, sample status, etc.), test result data (such as specific values of various test indicators such as blood routine, biochemical test, coagulation function, etc.), and test report information (such as report time, reporting doctor, reviewing doctor, etc.). The embodiments of the present application do not limit this. For an object with arrhythmia, the laboratory information system can collect data on test items related to arrhythmia, such as blood electrolyte levels (potassium, sodium, calcium, magnesium, etc.) because electrolyte disorders may induce or exacerbate arrhythmia; and such as myocardial enzyme spectrum, troponin and other indicators to determine myocardial damage and indirectly reflect the severity of arrhythmia; and such as thyroid function indicators because abnormal thyroid function may also cause arrhythmia, etc. Correspondingly, when detecting whether a target object has an arrhythmia disorder, the laboratory information system can provide various medical examination results detected by the laboratory for the target object.

[0036] A Picture Archiving and Communication System (PACS) mainly collects data related to imaging examinations, including imaging examination application information (such as basic information of the target object, examination site, examination item, applying doctor, etc.), original image data collected by imaging devices (such as medical images of X-ray, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), ultrasound, etc.), imaging diagnosis report information (such as diagnosis opinion, report time, reporting doctor, reviewing doctor, etc.), and imaging post-processing data (such as results after processing like image reconstruction, enhancement, measurement, etc.). The embodiments of this application do not limit this. Correspondingly, when detecting whether a target object has an arrhythmia disorder, the imaging examination system can provide the medical images collected for the target object by the imaging device as described above.

[0037] The electrocardiogram center system includes at least one of the electrocardiogram signal and electrocardiogram of the target object. That is, when detecting whether a target object has an arrhythmia disorder, the electrocardiogram center system can directly provide the original electrocardiogram signal of the target object, or provide an electrocardiogram obtained based on the original electrocardiogram signal, etc. The embodiments of this application do not limit this.

[0038] Continue to refer to Figure 1 , in the embodiments of this application, the data acquisition module is used to obtain various medical data of the target object from multiple data sources in the form of an intermediate library and send the various medical data to the screening module. The intermediate library refers to a database located between the data source and the target data warehouse, which is used for the intermediate link of storing and processing data. It is mainly used for the processes of data extraction, transformation, and loading to ensure the seamless flow and consistency of data between different systems. That is, the data acquisition module can be regarded as a database between multiple data sources and the screening module. After the data acquisition module obtains all the required data from multiple data sources, it sends them to the screening module at one time for processing to screen whether the target object has arrhythmia.

[0039] In some embodiments, before transmitting the medical data to the screening module, the data acquisition module can also integrate and transform the medical data from different data sources so that various medical data can be stored and used in a unified format and structure. This may include operations such as data type conversion, data format conversion, data cleaning, etc. The embodiments of this application do not limit this.

[0040] In some other embodiments, before transmitting medical data to the screening module, the data acquisition module may also clean and verify various medical data to ensure the accuracy and integrity of the medical data required in the subsequent screening process. For example, operations such as removing duplicate data, formatting data, and processing missing values are not limited in the embodiments of the present application. Alternatively, the data acquisition module may also verify and filter various medical data by defining data rules and constraints to exclude errors, invalid data, and data unrelated to arrhythmia, etc., which are not limited in the embodiments of the present application.

[0041] The embodiments of the present application do not limit the screening timing of arrhythmia.

[0042] In some embodiments, the arrhythmia specialized disease screening system may screen for arrhythmia according to user operations. Accordingly, the data acquisition module is used to respond to an arrhythmia detection operation for a target object and obtain various medical data of the target object from multiple data sources in the manner of an intermediate library. That is, whenever an arrhythmia operation is triggered, the data acquisition module in the above screening system can obtain various medical data related to arrhythmia and transmit the various medical data to the screening module for processing to determine whether the target object has arrhythmia and the type of arrhythmia suffered in the case of having arrhythmia.

[0043] In some other embodiments, since the screening process of arrhythmia usually requires obtaining the electrocardiogram data of the target object, in the case of detecting that the electrocardiogram data has been collected, it indicates that the possibility of the target object undergoing arrhythmia screening is relatively high. In this case, the screening system may obtain various medical data related to arrhythmia to screen for arrhythmia. Accordingly, the data source for providing electrocardiogram data can automatically send it to the data acquisition module after collecting the electrocardiogram data of the target object, and the data acquisition module is used to obtain various medical data of the target object from multiple data sources in the manner of an intermediate library in the case of receiving the electrocardiogram data of the target object.

[0044] Continue to refer to Figure 1 In the embodiments of the present application, a machine learning model is deployed on the screening module. The screening module is used to process various medical data of the target object by using the machine learning model to obtain the screening result of the target object. The screening result includes whether the target object has arrhythmia and the type of arrhythmia suffered in the case of having arrhythmia. The machine learning model may be any deep learning model, which is not limited in the embodiments of the present application.

[0045] Among them, the types of arrhythmia can be divided into atrial fibrillation, atrial flutter, pre-excitation syndrome, ventricular tachycardia, atrioventricular block, escape beats, sinus bradycardia, heart failure, etc. Atrial fibrillation refers to the loss of regular and orderly electrical activity in the atrium, replaced by rapid and disorderly fibrillation waves, resulting in the loss of normal rhythm in the atrium. Atrial flutter refers to the contraction of myocardial fibers in the atrium at a very high frequency. However, due to abnormal conduction between the atrium and the ventricle, the ventricular rate may not reach the same high frequency. Pre-excitation syndrome refers to an arrhythmia in which the ventricle is pre-excited due to the existence of an abnormal conduction pathway (bypass) between the atrium and the ventricle. Ventricular tachycardia refers to a ventricular rate of ≥120 beats per minute and ≥3 consecutive ventricular beats. Atrioventricular block refers to the blockage that occurs during the transmission of electrical impulses from the atrium to the ventricle. Escape beats refer to the situation where when the excitation of the sinoatrial node or atrioventricular node cannot be normally conducted, the pacing cells of the ventricle spontaneously issue excitations to maintain the basic rhythm of the heart. Sinus bradycardia refers to the excitation frequency issued by the sinoatrial node being lower than 60 beats per minute. Heart failure refers to the impairment of the heart's pumping function, unable to meet the body's needs.

[0046] In some embodiments, the lesions causing arrhythmia can occur in different parts of the heart, including: the sinoatrial node, the atrium (including the left atrium and the right atrium), the atrioventricular node, the ventricle (including the left ventricle and the right ventricle), and the cardiac conduction system (such as the His bundle, Purkinje fibers), etc. The embodiments of the present application do not limit this. The types of arrhythmia caused by different lesion locations may be different. The screening module is also used to determine the lesion location on the target object that causes arrhythmia based on various medical data of the target object when the target object has arrhythmia. The process of determining the lesion location can also be determined by a machine learning model. The embodiments of the present application do not limit this. This solution is beneficial for subsequent faster treatment or more accurate determination of the type of arrhythmia by determining the lesion location.

[0047] For example, for atrial fibrillation, the lesion location is mainly in the atrium, especially in the area where the left atrium intersects with the pulmonary veins; for atrial flutter, the lesion location is mainly in the atrium, and the lesion may be strip-shaped, located in the left atrium or the right atrium; for pre-excitation syndrome, the lesion location is mainly in the abnormal conduction pathway (bypass) between the atrium and the ventricle; for ventricular tachycardia, the lesion location is mainly in the ventricle, and it may be punctate or small patchy; for atrioventricular block, the lesion location is mainly in the conduction systems such as the atrioventricular node or the His bundle; for escape beats, the lesion location is mainly in the ventricle or the atrioventricular junction area; for sinus bradycardia, the lesion location is mainly in the sinoatrial node; for heart failure, the lesion location may involve the atrium and the ventricle.

[0048] In some embodiments, the screening module is further configured to, when the target object has arrhythmia, determine the grade of the arrhythmia suffered by the target object based on various medical data of the target object. The grade is used to represent the severity of the arrhythmia. By determining the grade of the arrhythmia, timely treatment can be provided to the target object according to the severity reflected by the grade in the subsequent process.

[0049] Taking atrioventricular block as an example, according to the degree of block, the severity of this type of arrhythmia can be divided into three grades, namely first-degree atrioventricular block, second-degree atrioventricular block, and third-degree atrioventricular block. Among them, first-degree atrioventricular block: the atrioventricular conduction time is prolonged, but each atrial impulse can still be conducted into the ventricle; second-degree atrioventricular block: some atrial impulses cannot be conducted to the ventricle, which is divided into Mobitz type I and Mobitz type II; third-degree atrioventricular block: the electrical signal between the atrium and the ventricle is completely interrupted, and the atrium and the ventricle work independently, often requiring emergency treatment.

[0050] The embodiments of the present application do not limit the method for determining the grade of arrhythmia. In the process of determining the grade of arrhythmia, the screening module is configured to, when the target object has arrhythmia, determine the duration of the arrhythmia suffered by the target object based on various medical data of the target object, and determine the grade of the arrhythmia suffered by the target object based on the duration. The grade is positively correlated with the duration. That is, the longer the duration, the higher the severity of the arrhythmia, that is, the higher the grade; the shorter the duration, the lower the severity of the arrhythmia, that is, the lower the grade. By determining the grade of arrhythmia according to the duration of arrhythmia, the accuracy of the grade can be improved, which is beneficial to the subsequent treatment of the target object.

[0051] In some embodiments, the screening module is further configured to, when the target object has arrhythmia, determine the treatment method for the target object based on the type of arrhythmia suffered by the target object. Different types of arrhythmias have different treatment methods. The process of determining the treatment method can also be implemented by a machine learning model, and the embodiments of the present application do not limit this.

[0052] For the machine learning model deployed in the screening module, before screening, the screening module can also train the machine learning model. Correspondingly, the screening module is further configured to train the machine learning model based on various medical data of multiple sample objects and the sample labels of multiple sample objects. The sample label of each sample object is used to indicate whether the sample object has arrhythmia and the type of arrhythmia suffered when having arrhythmia. The trained machine learning model can detect whether the target object has arrhythmia and the type of arrhythmia suffered by the target object when having arrhythmia.

[0053] In some other embodiments, for a target object without arrhythmia, the screening module can also predict the risk that the target object will develop arrhythmia. Accordingly, the screening module is further configured to, when the target object does not have arrhythmia, predict the risk probability of the target object based on various medical data of the target object. The risk probability is used to represent the probability that the target object will develop arrhythmia within a future time period. The process of predicting the risk probability can also be implemented by a machine learning model, which is not limited in the embodiments of the present application. By predicting the risk probability that the target object will develop arrhythmia in the future, this solution facilitates subsequent timely prevention of the target object based on the risk probability.

[0054] For various data such as the type, lesion location, duration, grade, treatment method, etc. of arrhythmia determined by the above screening module, the screening module can summarize the various data and generate a screening report so that the target object can obtain various information at one time, which is beneficial to improving the information transmission degree and information transmission efficiency.

[0055] The arrhythmia specialized disease screening system provided by the embodiments of the present application can, in the process of detecting whether a target object has arrhythmia, integrate various medical data related to arrhythmia of the target object in multiple data sources through the data acquisition module, and transmit the various medical data to the screening module for automatic screening, improving the screening efficiency of arrhythmia. Moreover, a machine learning model is adopted in the screening module to process the various medical data to determine whether the target object has arrhythmia and the type of arrhythmia suffered by the target object when having arrhythmia, avoiding the subjective influence in the artificial diagnosis process and being beneficial to improving the accuracy of screening arrhythmia.

[0056] In some embodiments, the screening system further includes a quality control detection module. For example, referring to Figure 2 , Figure 2 is a framework diagram of another arrhythmia specialized disease screening system provided by the embodiments of the present application. The quality control detection module is used to determine the accuracy of the screening result for the target object based on quality control punctuation. The quality control punctuation is used to indicate the conditions to be met when determining arrhythmia. Among them, the quality control detection module can formulate quality control punctuation according to the screening result output by the screening module, the doctor's advice information and examination information in the various medical data, and the embodiments of the present application do not limit the specific determination method of the quality control punctuation. Then, the quality control detection module can perform quality control and effect evaluation on the screening process based on the quality control label to ensure the accuracy and reliability of the screening result. That is to say, the quality control detection module can determine the accuracy of the screening result for the target object based on the quality control punctuation, or the quality control detection module can also determine the accuracy rate of the machine learning model in the screening module based on the accuracy of the screening results of multiple objects.

[0057] In some embodiments, the screening module is further configured to adjust the model parameters in the machine learning model with the goal of improving the accuracy of the machine learning model.

[0058] In some other embodiments, the screening system further includes an information management platform. Continuing to refer to Figure 2 , the information management platform is configured to display the output results of each module in the screening system. That is, the information management platform provides a visual interface to display the screening results, quality control results, and the medical data of the object. Doctors can efficiently manage the specialized disease of arrhythmia through the information management platform, such as viewing the screening history of patients, formulating personalized treatment plans, etc.

[0059] Among them, the input data and output data of the screening module and the quality control detection module can also be archived and saved. The result data of the information management platform can also be transmitted back as needed according to the requirements of each system within the hospital.

[0060] The solution provided by the embodiments of the present application can perform quality control and effect evaluation on the screening process of arrhythmia based on quality control punctuation marks to ensure the accuracy and reliability of the screening results; and assist doctors to efficiently and accurately manage the screening and management work of the specialized disease of arrhythmia within the hospital or in the medical consortium through the information platform, reducing the doctor's workload and improving the diagnosis and treatment efficiency and quality.

[0061] The multiple data sources and each module in the above-mentioned specialized disease screening system for arrhythmia can be respectively deployed on different computer devices. For example, the multiple data sources are respectively deployed on the servers of their corresponding data systems, the data collection module and the information management platform can be deployed on the terminal (such as the front-end machine within the hospital), and the screening module and the quality control detection module can be deployed on the server corresponding to the screening system. The embodiments of the present application do not limit this.

[0062] In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a smart voice interaction device, a vehicle-mounted terminal, etc., but is not limited thereto. An application program supporting the screening of arrhythmia can run on the terminal. The application program can include a data collection module and an information management platform to control the screening process and display the screening results.

[0063] Those skilled in the art can know that the number of the above-mentioned terminals can be more or less. For example, the above-mentioned terminal can be only one, or the above-mentioned terminal can be dozens or hundreds, or a larger number. The embodiments of the present application do not limit the number and device type of the terminals.

[0064] In some embodiments, the server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), big data, and artificial intelligence platforms. The server is used to provide background services for an application program that supports arrhythmia screening. In some embodiments, the server undertakes the main computing work and the terminal undertakes the secondary computing work; or, the server undertakes the secondary computing work and the terminal undertakes the main computing work; or, a distributed computing architecture is adopted between the server and the terminal for collaborative computing.

[0065] In the embodiments of the present application, the computer device can be configured as a terminal or a server. When the computer device is configured as a terminal, the technical solutions provided in the embodiments of the present application can be implemented with the terminal as the execution subject. When the computer device is configured as a server, the technical solutions provided in the embodiments of the present application can be implemented with the server as the execution subject, or the technical solutions provided in the present application can be implemented through the interaction between the terminal and the server. The embodiments of the present application do not limit this.

[0066] Figure 3 It is a structural block diagram of a terminal 300 provided according to the embodiments of the present application. The terminal 300 can be a portable mobile terminal, such as: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a notebook computer, or a desktop computer. The terminal 300 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, etc.

[0067] Generally, the terminal 300 includes: a processor 301 and a memory 302.

[0068] The processor 301 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 301 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.

[0069] The memory 302 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 302 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 302 is used to store at least one computer program, and the at least one computer program is used to be executed by the processor 301 to implement the arrhythmia screening method provided in the method embodiments of the present application.

[0070] In some embodiments, the terminal 300 may further optionally include: a peripheral device interface 303 and at least one peripheral device. The processor 301, the memory 302, and the peripheral device interface 303 may be connected through a bus or signal lines. Each peripheral device may be connected to the peripheral device interface 303 through a bus, signal lines, or a circuit board. Specifically, the peripheral devices include at least one of a radio frequency circuit 304, a display screen 305, a camera assembly 306, an audio circuit 307, and a power supply 308.

[0071] The peripheral device interface 303 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the peripheral device interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the peripheral device interface 303 can be implemented on a separate chip or circuit board, and this embodiment does not limit this.

[0072] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 304 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. In some embodiments, the radio frequency circuit 304 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and so on. The radio frequency circuit 304 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, each generation of mobile communication networks (2G, 3G, 4G, and 5G), a wireless local area network, and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 304 may further include a circuit related to NFC (Near Field Communication), and this application does not limit this.

[0073] The display screen 305 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 305 is a touch display screen, the display screen 305 also has the ability to collect touch signals on or above the surface of the display screen 305. The touch signals can be input as control signals to the processor 301 for processing. At this time, the display screen 305 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, there may be one display screen 305, which is disposed on the front panel of the terminal 300; in other embodiments, there may be at least two display screens 305, which are respectively disposed on different surfaces of the terminal 300 or in a foldable design; in other embodiments, the display screen 305 may be a flexible display screen, which is disposed on the curved surface or the folding surface of the terminal 300. Even, the display screen 305 can also be set to an irregular non-rectangular shape, that is, an irregular-shaped screen. The display screen 305 can be prepared using materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0074] The camera module 306 is used to capture images or videos. In some embodiments, the camera module 306 includes a front camera and a rear camera. Generally, the front camera is disposed on the front panel of the terminal, and the rear camera is disposed on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera, so as to realize the function of background blurring by fusing the main camera and the depth-of-field camera, the function of panoramic shooting by fusing the main camera and the wide-angle camera, and the VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera module 306 may further include a flash. The flash can be a single-color-temperature flash or a dual-color-temperature flash. The dual-color-temperature flash refers to the combination of a warm-light flash and a cold-light flash, which can be used for light compensation under different color temperatures.

[0075] The audio circuit 307 may include a microphone and a speaker. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into electrical signals for input to the processor 301 for processing, or input to the radio frequency circuit 304 to achieve voice communication. For the purpose of stereo collection or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 300. The microphone may also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signal from the processor 301 or the radio frequency circuit 304 into sound waves. The speaker may be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into audible sound waves for humans, but also convert the electrical signal into inaudible sound waves for humans for uses such as ranging. In some embodiments, the audio circuit 307 may further include a headphone jack.

[0076] The power supply 308 is used to supply power to each component in the terminal 300. The power supply 308 may be alternating current, direct current, a primary battery or a rechargeable battery. When the power supply 308 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0077] In some embodiments, the terminal 300 further includes one or more sensors 309. The one or more sensors 309 include but are not limited to: an acceleration sensor 310, a gyroscope sensor 311, a pressure sensor 312, an optical sensor 313, and a proximity sensor 314.

[0078] The acceleration sensor 310 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established with the terminal 300. For example, the acceleration sensor 310 can be used to detect the components of the gravitational acceleration on the three coordinate axes. The processor 301 can control the display screen 305 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 310. The acceleration sensor 310 can also be used for collecting game or user's motion data.

[0079] The gyroscope sensor 311 can detect the body direction and rotation angle of the terminal 300. The gyroscope sensor 311 can cooperate with the acceleration sensor 310 to collect the 3D actions of the user on the terminal 300. According to the data collected by the gyroscope sensor 311, the processor 301 can achieve the following functions: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0080] The pressure sensor 312 can be disposed on the side frame of the terminal 300 and / or the lower layer of the display screen 305. When the pressure sensor 312 is disposed on the side frame of the terminal 300, it can detect the holding signal of the user on the terminal 300, and the processor 301 can perform left / right hand recognition or quick operation according to the holding signal collected by the pressure sensor 312. When the pressure sensor 312 is disposed on the lower layer of the display screen 305, the processor 301 can control the operable controls on the UI interface according to the pressure operation of the user on the display screen 305. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0081] The optical sensor 313 is used to collect the ambient light intensity. In one embodiment, the processor 301 can control the display brightness of the display screen 305 according to the ambient light intensity collected by the optical sensor 313. Specifically, when the ambient light intensity is high, the display brightness of the display screen 305 is increased; when the ambient light intensity is low, the display brightness of the display screen 305 is decreased. In another embodiment, the processor 301 can also dynamically adjust the shooting parameters of the camera module 306 according to the ambient light intensity collected by the optical sensor 313.

[0082] The proximity sensor 314, also known as the distance sensor, is usually disposed on the front panel of the terminal 300. The proximity sensor 314 is used to collect the distance between the user and the front of the terminal 300. In one embodiment, when the proximity sensor 314 detects that the distance between the user and the front of the terminal 300 is gradually decreasing, the processor 301 controls the display screen 305 to switch from the lit state to the off state; when the proximity sensor 314 detects that the distance between the user and the front of the terminal 300 is gradually increasing, the processor 301 controls the display screen 305 to switch from the off state to the lit state.

[0083] Those skilled in the art can understand that Figure 3 the structure shown in does not constitute a limitation on the terminal 300, and it may include more or fewer components than shown in the figure, or combine some components, or adopt different component arrangements.

[0084] Figure 4It is a schematic structural diagram of a server provided by an embodiment of the present application. The server 400 may vary greatly due to different configurations or performances, and may include one or more processors (Central Processing Units, CPUs) 401 and one or more memories 402. Among them, at least one computer program is stored in the memory 402, and the at least one computer program is loaded and executed by the processor 401 to implement the arrhythmia screening method provided by each of the above method embodiments. Of course, the server may also have components such as wired or wireless network interfaces, keyboards, and input / output interfaces for input / output. The server may also include other components for implementing device functions, which will not be elaborated here.

[0085] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above embodiments can be completed by hardware, or can be completed by instructing relevant hardware through a program. The said program can be stored in a computer-readable storage medium, and the above-mentioned storage medium can be a read-only memory, a disk, or an optical disc, etc.

[0086] The above are only alternative embodiments of the present application, and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A cardiac arrhythmia screening system, characterized in that: The screening system comprises: multiple data sources, a data acquisition module and a screening module; The multiple data sources are used to provide different types of medical data related to arrhythmia; The data acquisition module is used to obtain a variety of medical data of the target object from the multiple data sources in an intermediate library manner, and send the multiple medical data to the screening module; The screening module is used to use a machine learning model to process multiple medical data of the target object to obtain a screening result of the target object, and the screening result includes whether the target object suffers from arrhythmia and the type of arrhythmia if suffering from arrhythmia.

2. The arrhythmia screening system according to claim 1, characterized in that: The multiple data sources are respectively deployed in a hospital information system, a laboratory testing system, an imaging examination system and an electrocardiography center system. The hospital information system contains the medical record information and doctor's order information of the target object, the laboratory testing system contains various medical examination results obtained by the laboratory for the target object, the imaging examination system contains medical images collected by imaging equipment for the target object, and the electrocardiography center system includes the electrocardiography signal of the target object and at least one electrocardiography data in the electrocardiogram.

3. The arrhythmia screening system according to claim 1, characterized in that: The data acquisition module is used for acquiring a variety of medical data of the target object from the multiple data sources in response to the arrhythmia detection operation for the target object by adopting an intermediate library method; Alternatively, the data source for providing ECG data can automatically send it to the data acquisition module after collecting the ECG data of the target object. The data acquisition module is used to obtain a variety of medical data of the target object from the multiple data sources by using an intermediate library when receiving the ECG data of the target object.

4. The arrhythmia screening system according to claim 1, characterized in that: The screening module is also used to determine the location of a lesion causing arrhythmia on the target object based on a variety of medical data of the target object when the target object suffers from arrhythmia.

5. The arrhythmia screening system according to claim 1, characterized in that: The screening module is also used to determine the grade of arrhythmia suffered by the target object based on a variety of medical data of the target object when the target object suffers from arrhythmia, where the grade is used to indicate the severity of the arrhythmia.

6. The arrhythmia screening system according to claim 5, characterized in that: The screening module is used to determine the duration of the arrhythmia of the target object based on multiple medical data of the target object when the target object suffers from arrhythmia, and determine the grade of the arrhythmia suffered by the target object based on the duration, and the grade is positively correlated with the duration.

7. The arrhythmia screening system according to claim 1, characterized in that: The screening module is also used to determine a treatment method for the target object based on the type of arrhythmia suffered by the target object when the target object suffers from arrhythmia.

8. The arrhythmia screening system according to claim 1, characterized in that: The screening module is also used to train the machine learning model based on the multiple medical data of multiple sample objects and the sample labels of the multiple sample objects, and the sample label of each sample object is used to indicate whether the sample object suffers from arrhythmia and the type of arrhythmia if suffering from arrhythmia.

9. The arrhythmia screening system according to claim 1, characterized in that: The screening module is also used to predict the risk probability of the target object based on multiple medical data of the target object when the target object does not suffer from arrhythmia, and the risk probability is used to represent the probability of the target object suffering from arrhythmia in a future time period.

10. The arrhythmia screening system according to claim 1, characterized in that: The screening system also includes a quality control detection module, which is used to determine the accuracy of the screening results of the target object based on quality control punctuation points, and the quality control punctuation points are used to indicate conditions that must be met when determining arrhythmia.

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