CN112435748A - Risk prediction method, device, equipment and computer readable medium - Google Patents
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- 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
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Abstract
The disclosed embodiments of the invention disclose a risk prediction method, apparatus, device and computer readable medium. The method comprises the following steps: acquiring case related data of a target user; inputting the case related data into a preset trained risk prediction model, and outputting a prediction result, wherein the risk prediction model is obtained through joint learning training; transmitting the prediction result to a target display device, and controlling the target display device to display the prediction result. According to the embodiment, the equipment is selected for training, the risk prediction model is generated based on the parameters obtained through training, data transmission of large data volume is avoided, communication cost is reduced, and the utilization rate of data on the edge equipment is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a risk prediction method, a risk prediction device, risk prediction equipment and a computer readable medium.
Background
The traditional processing mode of artificial intelligence realizes the expansion of training sample number by centralizing data of different nodes, however, the requirements of modern society on data privacy and safety are more and more strict, and the emergence of relevant data privacy laws and regulations provides new challenges for the traditional processing mode of artificial intelligence. For example, in the field of medical care, electronic medical records, physical examination data, image data and the like of different hospitals and different people are very sensitive, data cannot be concentrated among the hospitals and the people, and in addition, in consideration of the requirements of user privacy, commercial interests, supervision and the like, small data and individual data islands face, and the artificial intelligence needs the big data.
In the medical field, risk prediction is a research task with prospective and great practical significance, a large amount of long-time continuous data is often needed, and in the real life, most of patients suffering from chronic diseases in medical institutions have the problems of small data volume and poor data quality, and the realization of the risk prediction of the chronic diseases is not enough supported; especially, the extremely sensitive medical information such as the electronic medical record, the traditional Chinese medicine prescription, the traditional Chinese medicine syndrome, the traditional Chinese medicine inquiry and the like of the patient are difficult to be fully and effectively utilized on the premise of compliance.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary of the disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiments of the present disclosure provide a risk prediction method, apparatus, device and computer readable medium to solve the technical problems mentioned in the background above.
In a first aspect, an embodiment of the present disclosure provides a risk prediction method, including: acquiring case related data of a target user; inputting the case related data into a preset trained risk prediction model, and outputting a prediction result, wherein the risk prediction model is obtained by joint learning training, and the training of the risk prediction model comprises the following steps: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; generating the risk prediction model based on parameters of the equipment model; transmitting the prediction result to a target display device, and controlling the target display device to display the prediction result.
In a second aspect, an embodiment of the disclosure provides a risk prediction apparatus, including: an acquisition unit configured to acquire case-related data of a target user; a prediction unit configured to input the case-related data into a pre-trained risk prediction model obtained by joint learning training, and output a prediction result, wherein the training of the risk prediction model comprises: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; generating the risk prediction model based on parameters of the equipment model; a display unit configured to transmit the prediction result to a target display apparatus, and control the target display apparatus to display the prediction result.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method as described in the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in the first aspect.
One of the above embodiments disclosed by the invention has the following beneficial effects: and inputting the acquired case-related data of the target user into a risk prediction model trained in advance to obtain a prediction result. The prediction result can be obtained without mutual data transmission, and the 'sand gathering tower' of sensitive small data is realized. Therefore, key problems of data privacy, data access right, small data and the like are solved. In addition, equipment is selected for training, and a risk prediction model is generated based on parameters obtained through training, so that data transmission of large data volume is avoided, communication cost is reduced, and the utilization rate of data on edge equipment is improved.
Drawings
The above and other features, advantages and aspects of the disclosed embodiments will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of an application scenario of a risk prediction method according to a disclosed embodiment of the invention;
FIG. 2 is a flow chart of an embodiment of a risk prediction method according to the present disclosure;
FIG. 3 is a flow diagram of an embodiment of generating a risk prediction model according to the risk prediction method of the present disclosure;
FIG. 4 is a schematic block diagram of an embodiment of a risk prediction device according to the present disclosure;
FIG. 5 is a schematic block diagram of an electronic device suitable for use in implementing disclosed embodiments of the invention.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments disclosed in the present invention may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules, or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules, or units.
It is noted that references to "a", "an", and "the" modifications in the disclosure are exemplary rather than limiting, and that those skilled in the art will understand that "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the disclosed embodiments are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of a risk prediction method according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may acquire case-related data 102 of a target user. The computing device 101 may then input the case-related data 102 to a pre-set trained risk prediction model 103, outputting a prediction result 104. Finally, the computing device 101 may transmit the prediction result 104 to the target display device 105, and control the target display device 105 to display the prediction result 104.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to FIG. 2, a flow 200 of an embodiment of a risk prediction method according to the present disclosure is shown. The method may be performed by the computing device 101 of fig. 1. The risk prediction method comprises the following steps:
In an embodiment, the executing entity (e.g., the computing device 101 shown in fig. 1) of the risk prediction method may obtain the case-related data of the target user through a wired connection or a wireless connection. Here, the case related data may be case record information within a past preset time period of the above-mentioned target user.
It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
In an embodiment, the execution subject may input the case-related data to a risk prediction model trained in advance, and output a prediction result. Here, the risk prediction model may be a deep neural network that has been trained to generate a prediction result. Here, the prediction result may be used to characterize the probability that the target user is ill (e.g., chronic disease) within a future preset time period.
In an alternative implementation of the embodiment, the risk prediction model is updated by a gradient selection (e.g., Top-k gradient) during the training process.
In an optional implementation manner of the embodiment, the training of the risk prediction model includes: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; generating the risk prediction model based on parameters of the equipment model.
In an alternative implementation manner of the embodiment, the execution subject may transmit the prediction result to a target display device, and control the target display device to display the prediction result.
One of the above embodiments disclosed by the invention has the following beneficial effects: and inputting the acquired case-related data of the target user into a risk prediction model trained in advance to obtain a prediction result. The prediction result can be obtained without mutual data transmission, and the 'sand gathering tower' of sensitive small data is realized. Therefore, key problems of data privacy, data access right, small data and the like are solved. In addition, equipment is selected for training, and a risk prediction model is generated based on parameters obtained through training, so that data transmission of large data volume is avoided, communication cost is reduced, and the utilization rate of data on edge equipment is improved.
With continued reference to FIG. 3, a flow 300 of an embodiment of generating a risk prediction model according to the risk prediction method of the present disclosure is shown. The method may be performed by the computing device 101 of fig. 1. The training method comprises the following steps:
in response to receiving a training request from a target user, at least one device is selected as a target device, step 301.
In an embodiment, in response to receiving a training request of a target user, an executing agent of the scene text recognition method (e.g., computing device 101 shown in fig. 1) may select (e.g., select all) at least one device from a library of devices as the target device. The equipment library stores at least one equipment for participating in training.
In an embodiment, the executing entity may control the target device to start training by: firstly, the execution main body can obtain an initial model and model parameters of the initial model; secondly, the executing body can transmit the model parameters to the target device; and thirdly, the executing body can control the target device to start training based on the local data of the target device. Here, the initial model may be a model that is not trained or does not reach a preset condition after training. The initial model may be a model having a deep neural network structure. The storage location of the initial model is likewise not limiting in this disclosure.
As an example, the execution subject may obtain a local model of the target device, and obtain a local model set. Then, the execution subject may select a local model from the local model set as the initial model based on the data quality and the model effect.
Optionally, in response to determining that the end training condition is reached, the executive may complete training. Here, the training termination condition may be that a preset number of training tasks are executed, or that the model accuracy meets a preset requirement.
In an embodiment, in response to determining that the training is complete, the performing agent may obtain parameters of the trained device model.
In an embodiment, the executing entity may generate the risk prediction model based on parameters of the equipment model by:
first, the execution subject transmits the parameters of the equipment model to a central server.
In an alternative implementation manner of the embodiment, the execution body may encrypt the parameter of the device model based on a preset encryption method (e.g., a symmetric encryption method or an asymmetric encryption method). Then, in response to determining that the encryption is complete, the executing agent may transmit parameters of the encrypted device model to the central server.
And secondly, the execution main body can control the central server to aggregate the parameters of the equipment model to obtain aggregated parameters.
In an optional implementation manner of the embodiment, the execution subject may control the central server to average the parameters of the device model based on a preset aggregation algorithm (e.g., an average aggregation algorithm), and obtain an average result as the aggregation parameter.
In an optional implementation manner of the embodiment, the execution main body may control the central server to select a median value for the parameter of the equipment model based on a preset aggregation algorithm (e.g., a median value aggregation algorithm), and obtain the median value as the aggregation parameter.
Third, the execution subject may generate the risk prediction model based on the aggregation parameter.
In an optional implementation manner of the embodiment, the executing entity may determine the aggregation parameter as a parameter of the initial model, thereby obtaining the risk prediction model.
In an alternative implementation manner of the embodiment, the execution subject may control the central server to transmit the model parameters of the risk prediction model to the target device. Then, the executing agent may control the target device to perform parameter update on the local model.
As can be seen from fig. 3, the flow 300 of risk prediction in some embodiments corresponding to fig. 3 embodies steps of extending how to generate a risk prediction model, compared to the description of the embodiment corresponding to fig. 2. Thus, the approaches described in these embodiments may generate a risk prediction model by selecting a target device, controlling the target device to train, and then obtaining the trained model parameters. The updating and iteration of the model is done by transmitting the parameters to the target device. In addition, the parameters of the equipment model are encrypted, so that information leakage can be prevented, and the safety is improved.
With further reference to fig. 4, as an implementation of the above method for the above figures, the present disclosure provides some embodiments of a risk prediction apparatus, which correspond to the above method embodiments of fig. 2, and which can be applied in various electronic devices.
As shown in fig. 4, the risk prediction apparatus 400 of the embodiment includes: an acquisition unit 401, a prediction unit 402, and a display unit 403. Wherein the acquiring unit 401 is configured to acquire case related data of a target user; a prediction unit 402 configured to input the case-related data into a pre-trained risk prediction model obtained by joint learning training, and output a prediction result, wherein the training of the risk prediction model comprises: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; generating the risk prediction model based on parameters of the equipment model; a display unit 403 configured to transmit the prediction result to a target display apparatus and control the target display apparatus to display the prediction result.
In an optional implementation manner of the embodiment, a gradient selection method is adopted for updating in the training process of the risk prediction model.
In an optional implementation manner of the embodiment, the controlling the target device to start training includes: obtaining an initial model and model parameters of the initial model; transmitting the model parameters to the target device; controlling the target device to start training based on the local data of the target device.
In an optional implementation manner of the embodiment, the generating the risk prediction model based on the parameters of the device model includes: transmitting parameters of the equipment model to a central server; controlling the central server to aggregate the parameters of the equipment model to obtain aggregate parameters; generating the risk prediction model based on the aggregation parameters.
In an optional implementation manner of the embodiment, the transmitting the parameters of the device model to the central server includes: encrypting the parameters of the equipment model based on a preset encryption method; in response to determining that the encryption is complete, transmitting parameters of the encrypted device model to the central server.
In an optional implementation manner of the embodiment, the controlling the central server to aggregate the parameters of the device model to obtain an aggregated parameter includes: and controlling the central server to aggregate the parameters of the equipment model based on a preset aggregation algorithm to obtain aggregation parameters.
In an optional implementation manner of the embodiment, the generating the risk prediction model based on the aggregation parameter includes: and determining the aggregation parameters as the parameters of the initial model to obtain the risk prediction model.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to FIG. 5, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1)500 suitable for use in implementing some embodiments of the present disclosure is shown. The server shown in fig. 5 is only an example, and should not bring any limitation to the function and the scope of use of the disclosed embodiments of the present invention.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. Which when executed by the processing means 501 performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium mentioned above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring case related data of a target user; inputting the case related data into a preset trained risk prediction model, and outputting a prediction result, wherein the risk prediction model is obtained by joint learning training, and the training of the risk prediction model comprises the following steps: in response to receiving a training request of a target user, selecting at least one device as a target device; controlling the target device to start training; in response to determining that the training is complete, obtaining parameters of the trained device model; generating the risk prediction model based on parameters of the equipment model; transmitting the prediction result to a target display device, and controlling the target display device to display the prediction result.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a prediction unit, and a display unit. Where the names of these units do not in some cases constitute a limitation of the unit itself, for example, the acquisition unit may also be described as a "unit that acquires case-related data of the target user".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the present disclosure and is provided for the purpose of illustrating the general principles of the technology. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments disclosed in the present application is not limited to the embodiments with specific combinations of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A method of risk prediction, comprising:
acquiring case related data of a target user;
inputting the case related data into a preset trained risk prediction model, and outputting a prediction result, wherein the risk prediction model is obtained by joint learning training, and the training of the risk prediction model comprises the following steps:
in response to receiving a training request of a target user, selecting at least one device as a target device;
controlling the target device to start training;
in response to determining that the training is complete, obtaining parameters of the trained device model;
generating the risk prediction model based on parameters of the equipment model;
transmitting the prediction result to a target display device, and controlling the target display device to display the prediction result.
2. The method of claim 1, wherein the risk prediction model is updated by a gradient selection method during the training process.
3. The method of claim 1, wherein controlling the target device to begin training comprises:
obtaining an initial model and model parameters of the initial model;
transmitting the model parameters to the target device;
controlling the target device to start training based on the local data of the target device.
4. A risk prediction method according to claim 3, wherein the generating the risk prediction model based on the parameters of the equipment model comprises:
transmitting parameters of the equipment model to a central server;
controlling the central server to aggregate the parameters of the equipment model to obtain aggregate parameters;
generating the risk prediction model based on the aggregation parameters.
5. The method of claim 4, wherein transmitting the parameters of the equipment model to a central server comprises:
encrypting the parameters of the equipment model based on a preset encryption method;
in response to determining that the encryption is complete, transmitting parameters of the encrypted device model to the central server.
6. The method of claim 4, wherein the controlling the central server to aggregate the parameters of the equipment model to obtain aggregated parameters comprises:
and controlling the central server to aggregate the parameters of the equipment model based on a preset aggregation algorithm to obtain aggregation parameters.
7. The risk prediction method of claim 4, wherein the generating the risk prediction model based on the aggregation parameter comprises:
and determining the aggregation parameters as the parameters of the initial model to obtain the risk prediction model.
8. A risk prediction device, comprising:
an acquisition unit configured to acquire case-related data of a target user;
a prediction unit configured to input the case-related data into a pre-trained risk prediction model obtained by joint learning training, and output a prediction result, wherein the training of the risk prediction model comprises:
in response to receiving a training request of a target user, selecting at least one device as a target device;
controlling the target device to start training;
in response to determining that the training is complete, obtaining parameters of the trained device model;
generating the risk prediction model based on parameters of the equipment model;
a display unit configured to transmit the prediction result to a target display apparatus, and control the target display apparatus to display the prediction result.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
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| CN202011357573.1A CN112435748A (en) | 2020-11-26 | 2020-11-26 | Risk prediction method, device, equipment and computer readable medium |
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| CN202011357573.1A CN112435748A (en) | 2020-11-26 | 2020-11-26 | Risk prediction method, device, equipment and computer readable medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2018223194A1 (en) * | 2017-06-09 | 2018-12-13 | Alerte Digital Sport Pty Ltd | Systems and methods of prediction of injury risk with a training regime |
| CN110444263A (en) * | 2019-08-21 | 2019-11-12 | 深圳前海微众银行股份有限公司 | Disease data processing method, device, equipment and medium based on federation's study |
| CN111507481A (en) * | 2020-04-17 | 2020-08-07 | 腾讯科技（深圳）有限公司 | Federated learning system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018223194A1 (en) * | 2017-06-09 | 2018-12-13 | Alerte Digital Sport Pty Ltd | Systems and methods of prediction of injury risk with a training regime |
| CN110444263A (en) * | 2019-08-21 | 2019-11-12 | 深圳前海微众银行股份有限公司 | Disease data processing method, device, equipment and medium based on federation's study |
| CN111507481A (en) * | 2020-04-17 | 2020-08-07 | 腾讯科技（深圳）有限公司 | Federated learning system |
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