CN112100629A - Medical data processing method, device and system - Google Patents
Medical data processing method, device and system Download PDFInfo
- Publication number
- CN112100629A CN112100629A CN201910522671.7A CN201910522671A CN112100629A CN 112100629 A CN112100629 A CN 112100629A CN 201910522671 A CN201910522671 A CN 201910522671A CN 112100629 A CN112100629 A CN 112100629A
- Authority
- CN
- China
- Prior art keywords
- medical data
- data processing
- medical
- processing apparatus
- update
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims description 13
- 238000012545 processing Methods 0.000 claims abstract description 214
- 238000000034 method Methods 0.000 claims abstract description 45
- 238000003745 diagnosis Methods 0.000 claims abstract description 28
- 230000008569 process Effects 0.000 claims description 18
- 230000005540 biological transmission Effects 0.000 claims description 10
- 238000003062 neural network model Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 6
- 238000002955 isolation Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 description 15
- 238000004590 computer program Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 6
- 238000005481 NMR spectroscopy Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000012790 confirmation Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000007170 pathology Effects 0.000 description 1
- 230000000306 recurrent effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Software Systems (AREA)
- Bioethics (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Databases & Information Systems (AREA)
- Computer Security & Cryptography (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Epidemiology (AREA)
- Computing Systems (AREA)
- Primary Health Care (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Mathematical Physics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Pathology (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The method comprises the step of executing processing on received medical data by using a medical data processing model component which is included in the medical data processing device and corresponds to the medical data, and acquiring medical diagnosis information corresponding to the medical data. By the method and the device, the medical diagnosis information aiming at the medical data can be acquired in time under the condition of ensuring the privacy of the medical data.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, and a system for processing medical data.
Background
In the clinical diagnosis and treatment process, many medical data such as electrocardio, electroencephalogram, images and pathology are generated.
In order to further improve the accuracy of diagnosis and treatment, the medical data can be processed by using an artificial intelligence technology, in the prior art, the medical data is generally uploaded to a cloud end and processed by using a cloud end, but various problems, such as confidentiality of hospital data, can be encountered in the implementation process; the data is uploaded to the cloud, and the requirement of real-time performance is difficult to meet. Therefore, there is a need in the art for a solution that satisfies real-time while ensuring privacy.
The above information is presented merely as background information to aid in understanding the present disclosure. No determination has been made, nor has a statement been made, as to whether any of the above information is applicable as prior art against the present disclosure.
Disclosure of Invention
The embodiment of the application provides a medical data processing method, a medical data processing device and a medical data processing system, and aims to solve the problems of data privacy and processing real-time performance.
The embodiment of the application also provides a medical data processing method, which comprises the steps of executing processing on the received medical data by using a medical data processing model component which is included in a medical data processing device and corresponds to the medical data, and acquiring medical diagnosis information corresponding to the medical data.
The embodiment of the application also provides a medical data processing method, which comprises the following steps: transmitting the acquired medical data to a medical data processing apparatus, wherein the medical data processing apparatus includes a medical data processing model corresponding to a type of the medical data and performing processing on the medical data.
An embodiment of the present application further provides a medical data processing apparatus, where the apparatus includes: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the above method.
Embodiments of the present application also provide a computer-readable storage medium having stored thereon computer instructions, which when executed, implement the above method.
An embodiment of the present application further provides a medical data processing system, including: a medical data processing apparatus configured to: processing the received medical data by using a medical data processing model component corresponding to the medical data and included in the medical data processing device, and acquiring medical diagnosis information corresponding to the medical data, wherein the server is configured to: update data is stored that performs an update to the medical data processing model component. The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the medical data processing method of the exemplary embodiment of the application can process data by a device different from the medical data processing device, so that medical diagnosis information for the medical data can be acquired in time under the condition that the privacy of the medical data is guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is an application scenario diagram illustrating medical data processing according to an exemplary embodiment of the present application;
fig. 2 is a flowchart illustrating a medical data processing method according to an exemplary embodiment of the present application;
FIG. 3 is a block diagram illustrating a medical data processing system according to an exemplary embodiment of the present application;
fig. 4 is a block diagram illustrating a medical data processing apparatus according to an exemplary embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is an application scenario diagram illustrating medical data processing according to an exemplary embodiment of the present application. For descriptive purposes, the architecture portrayed is only one example of a suitable environment and is not intended to suggest any limitation as to the scope of use or functionality of the application. Neither should the computing system be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in FIG. 1.
The principles of the present application may be implemented using other general purpose or special purpose computing or communication environments or configurations. Examples of well known computing systems, environments, and configurations that may be suitable for use with the application include, but are not limited to, personal computers, servers, multiprocessor systems, microprocessor-based systems, minicomputers, mainframe computers, and distributed computing environments that include any of the above systems or devices.
In its most basic configuration, medical data processing system 100 in FIG. 1 includes at least: one or more medical data acquisition apparatuses 101 and one or more medical data processing apparatuses 102 that perform processing on medical data transmitted from the one or more medical data acquisition apparatuses 101, and a server 103 that can perform data transmission with the medical data processing apparatuses.
During a diagnostic procedure, a physician may request that a patient perform various related examinations using the medical data acquisition device 101 in order to acquire various medical data, which may include various bioelectric signals, as well as medical image data acquired using various medical imaging techniques. The medical acquisition device 101 may include medical devices such as an electrocardiograph, electroencephalograph, electromyograph, electroretinogram, and nuclear magnetic resonance.
Taking the example where the medical data acquisition device 101 is an electrocardiograph, as shown in fig. 1, after the patient lies on the bed, the doctor picks up the body surface bioelectric signals through the electrodes placed on the standard points of the body, and after the bioelectric signals are appropriately amplified by the bioelectric amplifier, forms an electrocardiogram and displays the electrocardiogram on the electrocardiograph 101, so that the doctor can diagnose the condition of the patient by combining his or her own experience after observing the electrocardiogram on the electrocardiograph 101.
In the present application, the medical data acquiring device may transmit the acquired medical data to the medical data processing device 102, and then the medical data processing device 102 may perform processing on the medical data, and in an implementation, the medical data processing device 102 may be a device dedicated to model operation, for example, the medical data processing device may be a customized single chip microcomputer, for example, a Tensor Processing Unit (TPU) that directly performs addition and multiplication operations in model operation without performing memory access, or an embedded neural Network Processor (NPU) adopting an architecture of "data-driven parallel computation", or a Graphics Processing Unit (GPU) that may perform a large number of parallelized processes, or the like.
In order to ensure privacy of the medical data, the medical data processing apparatus blocks data transmission in the medical data processing apparatus to the server 103 by using a physical isolation method.
Although fig. 1 only shows that the medical data processing device 102 performs processing on the medical data transmitted by a single medical data acquisition device 101, in practice, the medical data processing device 102 may serve a plurality of medical data acquisition devices 101 at the same time, and the categories of the medical data acquisition devices 101 may be the same or different, that is, the medical data acquisition devices 101 may belong to the same department, for example, an electrocardiogram room, or may belong to different departments, for example, an electrocardiogram room, an electroencephalogram room, and a nuclear magnetic resonance room, respectively.
In this case, the medical data processing apparatus 102 may embed a plurality of kinds of medical data processing model components so that processing can be performed with different medical data processing model components for medical data of different departments, for example, the medical data processing apparatus 102 may have an electroencephalogram data processing model component for electroencephalogram, an electrocardiograph data processing model component for electrocardiogram, and a nuclear magnetic resonance image processing model component for nuclear magnetic resonance image.
These model components may be various model components available, such as a medical data model component implemented with a neural network model trained with training data. The neural network model is a model in machine learning, and is an arithmetic mathematical model which simulates animal neural network behavior characteristics and performs distributed parallel information processing. The network achieves the aim of processing information by adjusting the mutual connection relationship among a large number of nodes in the network depending on the complexity of the system. Existing neural network models may include a feedforward neural network model, a recurrent network model, and a symmetric connection network model. In implementation, the model implemented by the neural network architecture or other models can be applied to the present application, and will not be expanded herein.
In this case, after the medical data is transmitted to the medical data processing apparatus 102, the medical data processing apparatus 102 may determine a medical data processing model component corresponding to the medical data. In implementation, the extracted data characteristic information of the medical data may be used to determine a medical data processing model component corresponding to the medical data, where the data characteristic information includes one or more combinations of the type of the medical data, the number of leads, the waveform information, and the dimension information, and in an embodiment, only one of the data characteristic information or two or more combinations of the data characteristic information may be selected as needed.
Taking the type of medical data as an example, the medical data processing apparatus 102 may determine the type of the medical data after acquiring the medical data, and in practice, the type of the medical data may be set in advance, for example, data transmitted from an electrocardiograph may be set as electrocardiograph data, and then, a medical data processing model component corresponding to the medical data, for example, an electrocardiograph model component for processing electrocardiograph data may be determined by using the type of the medical data, and electrocardiograph data may be transmitted to the electrocardiograph model component.
Taking the number of leads as an example, after the medical data is acquired, the medical data processing apparatus 102 may determine the number of leads of the medical data, for example, the number of leads of an electrocardiogram is 12 and the number of leads of an electroencephalogram is 20, and determine a medical data processing model component corresponding to the medical data using the lead data of the medical data.
Taking the waveform information as an example, the medical data processing apparatus 102 determines the waveform information of the medical data after acquiring the medical data, for example, the waveform of the electrocardiogram, which has a distinct R-shaped wave, is greatly different from the waveform of the electroencephalogram, and determines the medical data processing model component corresponding to the medical data by using the waveform information of the medical data.
Taking the dimension information as an example, after acquiring the medical data, the medical data processing apparatus 102 determines the dimension information of the medical data, for example, the magnetic resonance data is image data, and the magnetic brain data is multi-dimensional time series data, and determines the medical data processing model component corresponding to the medical data by using the dimension information of the medical data.
In addition, the medical data processing device may be the above-mentioned device dedicated to model operation, or may be an electronic terminal including such a device, for example, a mobile terminal, in which case, after the patient may receive the medical data by using his/her own mobile terminal (for example, a smartphone), the patient performs processing on the medical data and acquires medical diagnosis information.
Since the medical data processing device 102 has embedded one or more available models, for example: the trained neural network model component can directly process the medical data. And the medical data processing apparatus 102 may transmit the medical diagnosis information to various terminals as necessary after acquiring the medical diagnosis information, for example, to the medical data acquisition apparatus 101 as shown in fig. 1, or may transmit to a specified terminal, for example, an electronic terminal used by a treating doctor, an electronic terminal used by a patient, or the like.
In implementation, the server 103 stores update data that performs an update on the medical data processing model component. When it is necessary to perform an update on the medical data processing apparatus 102, the server 103 may transmit the update data to the medical data processing apparatus 102 according to a preset policy. Specifically, the server 103 may transmit update data to the medical data processing apparatus 102 upon receiving an update request transmitted from the medical data processing apparatus 102, and the medical data processing apparatus 102 may then perform update processing of the medical data processing model component using the update data.
Further, the server 103 may also actively transmit an update request to the medical data processing apparatus 102, for example, the update request may be actively transmitted to the medical data processing apparatus 102 at a preset time interval (for example, one month), or the update request may be actively transmitted to the medical data processing apparatus 102 when the server 103 needs to perform a version update on the medical data processing apparatus 102. When the medical data processing apparatus 102 determines that the update processing can be performed, it transmits confirmation information to the server 103. The server 103 may transmit the update data to the medical data processing apparatus 102 after receiving the confirmation information, and the medical data processing apparatus 102 may then perform the update processing of the medical data processing model component using the update data.
The server 103 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, a storage device for storing data, and a transmission device performing communication with the medical data processing device. In this description and in the claims, a "system" may also be defined as any hardware component or combination of hardware components capable of executing software, firmware, or microcode to perform a function, and the relationship extraction system 100 may even be distributed to perform distributed functions.
As used herein, the terms "module," "component," or "unit" may refer to a software object or routine that executes on the medical data processing system 100. The different components, modules, units, engines, and services described herein may be implemented as objects or processes that execute (e.g., as separate threads) on medical data processing system 100. Although the systems and methods described herein are preferably implemented in software, implementations in hardware or a combination of software and hardware are also possible and contemplated.
To better explain the present application, a flowchart of a medical data processing method performed by the medical data processing apparatus 102 will be described in detail below with reference to fig. 2.
Fig. 2 is a flowchart illustrating a medical data processing method according to an exemplary embodiment of the present application.
In practice, the medical data may be received from the outside first. Specifically, the medical data processing apparatus 102 is an apparatus that processes only medical data independently of each apparatus, and therefore, the medical data may be acquired directly from the medical data acquisition apparatus 101 or may be acquired from another apparatus that can provide medical data to be processed.
In step S210, the received medical data is processed by using a medical data processing model component corresponding to the medical data included in the medical data processing apparatus, and medical diagnosis information corresponding to the medical data is acquired.
In an implementation, the medical data processing apparatus includes a plurality of medical data processing model components together with the medical data processing model component, wherein the plurality of medical data processing model components respectively correspond to a plurality of kinds of medical data. In this case, the extracted data characteristic information of the medical data can be used to determine a medical data processing model component corresponding to the medical data; and inputting the medical data into the medical data processing model assembly to acquire medical diagnosis information corresponding to the medical data. The data characteristic information may include a combination of one or more of the types of medical data, lead numbers, waveform information, and dimensional information mentioned above.
Furthermore, the method further comprises: receiving update data that performs an update to the medical data processing model component; performing an update process on the medical data processing model component using the update data. And may inhibit outward transmission of medical data using physical isolation.
In summary, the medical data processing method according to the exemplary embodiment of the present application may perform processing on medical data by a medical data processing model component different from the medical data acquisition device, and perform processing on the medical data by using different entities, thereby reducing the risk of tampering the medical data and acquiring medical diagnosis information for the medical data in time. Furthermore, the medical data processing model is an available embedded model, so that the medical data processing model can be directly operated on the model, and the speed of medical data processing is increased. Furthermore, the updating data can be acquired from the outside to update the model on the model, so that the model can continuously adapt to new requirements under the condition of ensuring the existing requirements. Furthermore, the outward transmission of the medical data can be prohibited by utilizing a physical isolation mode, so that the privacy is further ensured. Further, the medical data processing device can be embedded with various model components, so that services can be provided for more medical data acquisition devices.
Further, according to an exemplary embodiment of the present application, there may be also provided a medical data processing method performed by a medical data acquisition apparatus, the method including transmitting acquired medical data to a medical data processing apparatus, wherein the medical data processing apparatus includes a medical data processing model corresponding to a type of the medical data and performing processing on the medical data. As can be seen, the medical data processing apparatus according to the exemplary embodiment of the present application can perform processing on medical data using an instantiated medical data processing apparatus that is different and separate from the medical data acquisition apparatus, thereby enabling faster acquisition of medical diagnostic information.
Further, according to an exemplary embodiment of the present application, there may be also provided a medical data processing system including: a medical data acquisition device; the acquired medical data is transmitted to the medical data processing apparatus. A medical data processing apparatus configured to: receiving medical data from a medical data acquisition device; and processing the acquired medical data by using a medical data processing model component corresponding to the medical data and included in the medical data processing device, and acquiring medical diagnosis information corresponding to the medical data. It can be seen that the medical data processing system according to the exemplary embodiment of the present application can perform the medical data acquisition process and the medical data processing process by different devices, respectively, so that the speed of data processing can be increased. In addition, the medical data processing apparatus can process the medical data by using a single materialized medical data processing apparatus, thereby acquiring the medical diagnosis information more quickly.
Further, according to an exemplary embodiment of the present application, there may be also provided a medical data processing system including: a medical data processing apparatus configured to: and processing the received medical data by using a medical data processing model component corresponding to the medical data and included in the medical data processing device, and acquiring medical diagnosis information corresponding to the medical data. A server configured to: update data is stored that performs an update to the medical data processing model component.
Optionally, the system further comprises: a server configured to: transmitting the update data to a medical data processing apparatus.
To better illustrate the present application, a medical data processing system according to an exemplary embodiment of the present application will be further described below in conjunction with fig. 3.
Fig. 3 shows a block diagram of a medical data processing system according to an exemplary embodiment of the present application.
As shown in fig. 3, the system may include two sides, namely a hospital side and a cloud side, and communication transmission may be performed between the hospital side and the cloud side through wires or wirelessly. The hospital side can comprise a medical data acquisition device and a medical data processing device, and the cloud side can comprise a server for updating the model embedded in the medical data processing device, so that when the medical data processing device needs to perform updating, the update data can be received from the server at the cloud side, the storage pressure of the medical data processing device can be reduced, the processing speed is further improved, the latest version can be continuously provided for a user, and different requirements of the user can be met. Since the medical data processing device may also not be updated at all times, the system may comprise only the hospital side in a specific application scenario.
The medical data acquisition device can transmit the medical data to a medical data processing model component in the medical data processing device to execute processing. Although it is shown in fig. 3 that the acquired medical diagnosis information may be transmitted to the medical data acquisition apparatus, in implementation, the acquired medical diagnosis information may be transmitted to other terminals according to settings, for example, the medical diagnosis information may be transmitted to a mobile terminal of a patient, or the medical diagnosis data may be directly transmitted to an electronic terminal used by a doctor, assisting the doctor in making a diagnosis. Furthermore, the medical data processing device may comprise a storage component and a communication module, which will not be described in detail herein.
In order to more clearly understand the inventive concept of the exemplary embodiment of the present application, a block diagram of a medical data processing apparatus of the exemplary embodiment of the present application will be described below with reference to fig. 4. Those of ordinary skill in the art will understand that: the apparatus in fig. 4 shows only components related to the present exemplary embodiment, and common components other than those shown in fig. 4 are also included in the apparatus.
Fig. 4 shows a block diagram of a medical data processing device of an exemplary embodiment of the present application. Referring to fig. 4, the apparatus includes, at a hardware level, a processor, an internal bus, and a computer-readable storage medium, wherein the computer-readable storage medium includes volatile memory and non-volatile memory. The processor reads the corresponding computer program from the non-volatile memory and then runs it. Of course, besides the software implementation, the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Specifically, the processor performs the following operations: and processing the received medical data by using a medical data processing model component corresponding to the medical data and included in the medical data processing device, and acquiring medical diagnosis information corresponding to the medical data.
The processor may also process the following operations: receiving update data that performs an update to the medical data processing model component; performing an update process on the medical data processing model component using the update data.
Optionally, the plurality of medical data processing model components comprises a neural network model component.
Optionally, the medical data processing apparatus includes a plurality of medical data processing model components together with the medical data processing model component, wherein the plurality of medical data processing model components correspond to a plurality of kinds of medical data, respectively.
Optionally, the processor, in the implementing step, performing processing on the acquired medical data by using a medical data processing model component corresponding to the medical data and included in the medical data processing apparatus to acquire medical diagnosis information corresponding to the medical data includes: determining a medical data processing model component corresponding to the medical data by using the extracted data characteristic information of the medical data; and inputting the medical data into the medical data processing model assembly to acquire medical diagnosis information corresponding to the medical data.
Optionally, the data characteristic information comprises a combination of one or more of a type of medical data, a number of leads, waveform information, and dimensional information.
Optionally, the processor may further perform the following operations: the outward transmission of the medical data is prohibited by using a physical isolation mode.
In summary, the medical data processing apparatus according to the exemplary embodiment of the present application may perform processing on medical data by a medical data processing model component different from the medical data acquisition apparatus, perform processing on the medical data by using different entities, thereby reducing the risk of tampering of the medical data, and acquire medical diagnosis information for the medical data in time. Furthermore, the medical data processing model is embedded with an available neural network model, so that the medical data processing model can be directly operated on the neural network model, and the speed of medical data processing is increased. Furthermore, the updating data can be acquired from the outside to update the model on the model, so that the model can continuously adapt to new requirements under the condition of ensuring the existing requirements. Furthermore, the outward transmission of the medical data can be prohibited by utilizing a physical isolation mode, so that the privacy is further ensured. Further, the medical data processing device can be embedded with various model components, so that services can be provided for more medical data acquisition devices.
It should be noted that the execution subjects of the steps of the method provided in embodiment 1 may be the same device, or different devices may be used as the execution subjects of the method. For example, the execution subject of steps 21 and 22 may be device 1, and the execution subject of step 23 may be device 2; for another example, the execution subject of step 21 may be device 1, and the execution subjects of steps 22 and 23 may be device 2; and so on.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (15)
1. A medical data processing method, comprising:
and processing the received medical data by using a medical data processing model component corresponding to the medical data and included in the medical data processing device, and acquiring medical diagnosis information corresponding to the medical data.
2. The method of claim 1, further comprising:
receiving update data that performs an update to the medical data processing model component;
performing an update process on the medical data processing model component using the update data.
3. The method of claim 1, wherein the medical data processing apparatus includes a plurality of medical data processing model components along with the medical data processing model component, wherein the plurality of medical data processing model components correspond to a plurality of types of medical data, respectively.
4. The method of claim 3, wherein the plurality of medical data processing model components includes a neural network model component.
5. The method of claim 4, wherein performing processing on the acquired medical data using a medical data processing model component corresponding to the medical data included in a medical data processing apparatus to acquire medical diagnostic information corresponding to the medical data comprises:
determining a medical data processing model component corresponding to the medical data from the plurality of medical data processing model components by using the extracted data characteristic information of the medical data;
and inputting the medical data into the medical data processing model assembly to acquire medical diagnosis information corresponding to the medical data.
6. The method of claim 5, wherein the data characteristic information comprises a combination of one or more of a type of medical data, a number of leads, waveform information, and dimensional information.
7. The method of claim 1, further comprising:
the outward transmission of the medical data is prohibited by using a physical isolation mode.
8. A medical data processing method, comprising:
transmitting the acquired medical data to a medical data processing apparatus, wherein the medical data processing apparatus includes a medical data processing model corresponding to a type of the medical data and performing processing on the medical data.
9. The method of claim 8, further comprising:
medical diagnostic information is received from the medical data processing device.
10. A medical data processing system, comprising:
a medical data acquisition device establishing a communication link with the medical data processing device configured to:
transmitting the acquired medical data to the medical data processing apparatus,
a medical data processing apparatus configured to:
receiving medical data from a medical data acquisition device;
and processing the acquired medical data by using a medical data processing model component corresponding to the medical data and included in the medical data processing device, and acquiring medical diagnosis information corresponding to the medical data.
11. The system of claim 10, further comprising:
a server, the medical server establishing a communication link with a medical data processing apparatus, configured to:
the update data is transmitted to the medical data processing apparatus.
12. A medical data processing apparatus, characterized by comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method of any of claims 1 to 9.
13. A computer readable storage medium having computer instructions stored thereon that, when executed, implement the method of any of claims 1 to 9.
14. A medical data processing system, comprising:
a medical data processing apparatus configured to:
processing the received medical data by using a medical data processing model component corresponding to the medical data and included in a medical data processing device, and acquiring medical diagnosis information corresponding to the medical data;
a server configured to:
update data is stored that performs an update to the medical data processing model component.
15. The system of claim 14, further comprising:
a server configured to:
transmitting the update data to a medical data processing apparatus.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910522671.7A CN112100629A (en) | 2019-06-17 | 2019-06-17 | Medical data processing method, device and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910522671.7A CN112100629A (en) | 2019-06-17 | 2019-06-17 | Medical data processing method, device and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112100629A true CN112100629A (en) | 2020-12-18 |
Family
ID=73749188
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910522671.7A Pending CN112100629A (en) | 2019-06-17 | 2019-06-17 | Medical data processing method, device and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112100629A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116509419A (en) * | 2023-07-05 | 2023-08-01 | 四川新源生物电子科技有限公司 | Electroencephalogram information processing method and system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108305671A (en) * | 2018-01-23 | 2018-07-20 | 深圳科亚医疗科技有限公司 | By computer implemented medical image dispatching method, scheduling system and storage medium |
-
2019
- 2019-06-17 CN CN201910522671.7A patent/CN112100629A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108305671A (en) * | 2018-01-23 | 2018-07-20 | 深圳科亚医疗科技有限公司 | By computer implemented medical image dispatching method, scheduling system and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116509419A (en) * | 2023-07-05 | 2023-08-01 | 四川新源生物电子科技有限公司 | Electroencephalogram information processing method and system |
| CN116509419B (en) * | 2023-07-05 | 2023-09-29 | 四川新源生物电子科技有限公司 | Electroencephalogram information processing method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102285878B1 (en) | Fundus Image Processing Using Machine Learning Models | |
| US11834052B2 (en) | Estimator generation apparatus, monitoring apparatus, estimator generation method, and computer-readable storage medium storing estimator generation program | |
| KR20210064029A (en) | Method, system and non-transitory computer-readable recording medium for estimating arrhythmia by using artificial neural network | |
| EP3850638B1 (en) | Processing fundus camera images using machine learning models trained using other modalities | |
| Overgaard et al. | An integrative view on consciousness and introspection | |
| KR102415806B1 (en) | Machine learning method of neural network to predict medical events from electronic medical record | |
| JP7002168B1 (en) | ECG analyzer, ECG analysis method and program | |
| Hinss et al. | Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications | |
| Morrison et al. | Dissociation of category-learning systems via brain potentials | |
| US20240290497A1 (en) | Method, and device for localizing and lateralizing brain functional region, apparatus, and storage medium | |
| CN112100629A (en) | Medical data processing method, device and system | |
| CN113870973A (en) | Information output method, device, computer equipment and medium based on artificial intelligence | |
| CN116999054A (en) | Physiological information acquisition device and method based on motion state | |
| Anzalone et al. | QEEG coherence patterns related to mathematics ability in children | |
| EP3850639A1 (en) | System and methods for consciousness evaluation in non-communcating subjects | |
| CN113223713A (en) | Dynamic monitoring method, system and storage medium based on risk assessment | |
| Chen et al. | Research on Working Memory States Based on Weighted K‐Order Propagation Number Algorithm: An EEG Perspective | |
| KR20180133091A (en) | Health diagnostic system and method using tongue | |
| CN110097966B (en) | Information reminding method and device and terminal equipment | |
| Pidnebesna et al. | Tackling the challenges of group network inference from intracranial EEG data | |
| Magliacano et al. | Spontaneous eye blinking during an auditory, an interoceptive and a visual task: The role of the sensory modality and the attentional focus | |
| US20180060489A1 (en) | Method and system for outputting an item of medical information | |
| Gupta et al. | Advanced telemedicine system for remote healthcare monitoring | |
| De Rooij et al. | Enabling large-scale probabilistic seizure detection with a tensor-network kalman filter for ls-svm | |
| Pipke et al. | Feasibility of personalized nonparametric analytics for predictive monitoring of heart failure patients using continuous mobile telemetry |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |