WO2022110719A1 - 基于联邦迁移学习的神经退行性疾病建模装置及相关设备 - Google Patents
基于联邦迁移学习的神经退行性疾病建模装置及相关设备 Download PDFInfo
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Definitions
- the present application relates to the field of artificial intelligence technology and the field of digital medicine, specifically to the field of AI + medical care, and in particular to a neurodegenerative disease modeling device and related equipment based on federated transfer learning.
- Neurodegenerative diseases are caused by the loss of function of neurons and their myelin sheaths and can worsen over time with various dysfunctions. With the increasing number of aging population in my country, more and more elderly people in our country suffer from neurodegenerative diseases. With the continuous development of the level of medical research in various countries in the world, the research methods of neurodegenerative diseases in various medical institutions are becoming more and more perfect. The research on neurodegenerative diseases in the industry now focuses on the research on the causes of different diseases, and there are also many new ideas for treatment. Current research results suggest that neurodegenerative diseases are often caused by oxidative stress, mitochondrial dysfunction, excitotoxins, and immune inflammation. The condition is diverse and includes aspects such as cognitive and behavioral disorders.
- the purpose of this application is to provide a neurodegenerative disease modeling device and related equipment based on federated transfer learning, aiming to solve the problem that the existing neurodegenerative disease disease degree prediction device cannot effectively model under the premise of ensuring privacy.
- the embodiments of the present application provide a neurodegenerative disease modeling device based on federated transfer learning, including:
- a data set acquisition unit configured to acquire a medical record data set A and a medical record data set B of the first type of neurodegenerative disease patient, the medical record data set A includes a plurality of pieces of data described by the first feature item set, the medical record data Set B includes multiple pieces of data described by the second feature item set; the first feature item set includes the feature items used to describe the patient's identity and the feature item used to describe the patient's disease state; the second feature item set includes the feature items used to describe Characteristics of the patient's identity and characteristics used to describe the patient's clinical presentation data;
- An intersection selection unit for selecting intersection data from between the case data set A and the case data set B, using the intersection data as a training set sample;
- a model training unit for training the original neural network model based on the training set samples
- the federated transfer unit is used to perform federated transfer learning on the original neural network model based on the training set samples of multiple types of neurodegenerative diseases including the first type of neurodegenerative diseases, and obtain a result for predicting the incidence of neurodegenerative diseases.
- the final neural network model of disease severity is used to perform federated transfer learning on the original neural network model based on the training set samples of multiple types of neurodegenerative diseases including the first type of neurodegenerative diseases, and obtain a result for predicting the incidence of neurodegenerative diseases.
- the final neural network model of disease severity is used to perform federated transfer learning on the original neural network model based on the training set samples of multiple types of neurodegenerative diseases including the first type of neurodegenerative diseases, and obtain a result for predicting the incidence of neurodegenerative diseases.
- an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program
- the following steps are implemented at the time of obtaining the first type of neurodegenerative disease patient medical record data set A and medical record data set B, the medical record data set A includes a plurality of pieces of data described by the first feature item set, and the medical record data set B It includes multiple pieces of data described by the second feature item set;
- the first feature item set includes the feature items used to describe the patient's identity and the feature item used to describe the patient's disease state;
- the second feature item set includes the feature items used to describe the patient's identity
- the characteristic item of and the characteristic item used to describe the clinical performance data of the patient Select the intersection data from the case data set A and the case data set B, and use the intersection data as the training set sample; Based on the training set sample, train the original neural network model; based on the training set samples of multiple types of neurodegenerative diseases including
- an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when executed by a processor, the computer program causes the processor to perform the following steps: Obtain a medical record data set A and a medical record data set B of a patient with a first type of neurodegenerative disease, the medical record data set A includes a plurality of pieces of data described by the first feature item set, and the medical record data set B includes a second feature set B Multiple pieces of data described by the item set; the first feature item set includes the feature item used to describe the patient's identity and the feature item used to describe the patient's disease state; the second feature item set includes the feature item used to describe the patient's identity and the The feature items used to describe the clinical performance data of the patient; select the intersection data from the case data set A and the case data set B, and use the intersection data as a training set sample; based on the training set sample, the original neural network model Carry out training; based on the training set samples of multiple types
- the embodiments of the present application provide a neurodegenerative disease modeling device and related equipment based on federated transfer learning.
- the device includes: a data set acquisition unit configured to acquire a medical record data set A and a medical record data set of the first type of neurodegenerative disease patients B; an intersection selection unit for selecting intersection data from the case data set A and the case data set B, and using the intersection data as a training set sample; a model training unit for selecting the intersection data based on the training set sample
- the original neural network model is trained; the federated transfer unit is used to perform federated transfer learning on the original neural network model based on the training set samples of multiple types of neurodegenerative diseases including the first type of neurodegenerative diseases, and obtain the The ultimate neural network model for predicting the prevalence of neurodegenerative diseases.
- the embodiments of the present application solve the problem of isolated data islands in the modeling process of the predictive model, and can effectively predict the degree of neurodegenerative diseases of patients.
- FIG. 1 is a schematic block diagram of a neurodegenerative disease modeling apparatus based on federated transfer learning provided by an embodiment of the present application;
- FIG. 2 is a schematic block diagram of subunits of the apparatus for modeling neurodegenerative diseases based on federated transfer learning according to an embodiment of the present application;
- FIG. 3 is a schematic block diagram of another subunit of the apparatus for modeling neurodegenerative diseases based on federated transfer learning according to an embodiment of the present application;
- FIG. 4 is a schematic block diagram of another subunit of the apparatus for modeling neurodegenerative diseases based on federated transfer learning provided in an embodiment of the present application;
- FIG. 5 is a schematic block diagram of another subunit of the apparatus for modeling neurodegenerative diseases based on federated transfer learning according to an embodiment of the present application
- FIG. 6 is a schematic block diagram of another subunit of the apparatus for modeling neurodegenerative diseases based on federated transfer learning according to an embodiment of the present application
- FIG. 7 is a schematic block diagram of a computer device provided by an embodiment of the present application.
- FIG. 1 is a schematic block diagram of a neurodegenerative disease modeling apparatus 100 based on federated transfer learning provided by an embodiment of the present application.
- the apparatus includes a data set acquisition unit 101 , an intersection selection unit 102 , and a model training unit 103 and Federal Migration Unit 104:
- a data set obtaining unit 101 is configured to obtain a medical record data set A and a medical record data set B of a first-type neurodegenerative disease patient, where the medical record data set A includes a plurality of pieces of data described by a first feature item set, and the medical record The data set B includes a plurality of pieces of data described by the second feature item set; the first feature item set includes the feature items used to describe the patient's identity and the feature items used to describe the patient's disease state; the second feature item set includes Features that describe the patient's identity and features that describe the patient's clinical presentation data;
- intersection selection unit 102 is used to select intersection data from between the case data set A and the case data set B, and the intersection data is used as a training set sample;
- a model training unit 103 configured to train the original neural network model based on the training set samples
- the federated transfer unit 104 is configured to perform federated transfer learning on the original neural network model based on the training set samples of multiple types of neurodegenerative diseases including the first type of neurodegenerative diseases, to obtain a model for predicting neurodegenerative diseases The final neural network model of disease severity.
- both the medical record data set A and the medical record data set B include multiple pieces of data, and the data types of the medical record data set A and the medical record data set B overlap. There are also parts that do not intersect.
- the medical record data set A includes multiple pieces of data described by a first feature item set
- the first feature item set includes feature items used to describe the patient's identity (for example, Zhang San, male, 65 years old, etc.) Items that describe the patient's disease state (eg, 20% Parkinson's disease).
- the medical record data set B includes multiple pieces of data described by the second feature item set, wherein the second feature item set includes the feature items used to describe the patient's identity (which are the same as the feature items corresponding to the first feature item set, such as Zhang. Three, male, 65 years old, etc.) and characteristic terms used to describe patient clinical presentation data (eg, less sleep, decreased mobility, intermittent mania, etc.).
- the first group of neurodegenerative diseases may be any one of neurodegenerative diseases, such as Parkinson's disease, obsessive lateral sclerosis or Huntington's syndrome.
- Parkinson's syndrome is preferably used, because the first type of neurodegenerative disease needs to be used for modeling and training of the original model, so some basic data volume is required, so the use of Parkinson's syndrome is beneficial to the original model. training.
- intersection data is selected from the case data set A and the case data set B. Due to different data sources, there may be missing or incomplete data.
- the intersection data is the data of a patient recorded in the case that records both the clinical manifestations and the disease state.
- the intersection data of the medical record data set A and the medical record data set B can be taken as the training set sample, so that the model can maximize the learning of the common features of the medical record data set A and the medical record data set B, and also avoid sample differentiation. impact on the model.
- the original neural network model is trained by using the training set samples obtained above. This unit trains the original neural network model first, and then generalizes the trained original neural network model to predict the prevalence of other neurodegenerative diseases in subsequent units.
- the model training unit 103 includes:
- the data input unit 201 is used to use the data of the characteristic item for describing the patient's diseased state in the training set sample as the label of the original neural network model; and the data used to describe the characteristic item of the patient's clinical performance data data, as the input data of the original neural network model;
- the model training subunit 202 is configured to train the original neural network model based on the input data to obtain the trained original neural network model.
- the data used to describe the characteristic item of the patient's disease state is used as the label of the original neural network model, and the data used to describe the characteristic item of the patient's clinical performance data is used as the original neural network model.
- the input data of the network model can make a corresponding relationship between the patient's performance and the disease state.
- the original neural network model predicts the patient's disease degree based on the patient's behavior and its characteristics.
- the model training subunit 202 the original neural network model can be trained by means of federated learning, thereby obtaining the original neural network model for predicting the degree of the first type of neurodegenerative disease.
- the data input unit 201 includes:
- the encryption unit is used for encrypting the data in the training set samples by using the homomorphic encryption technology.
- homomorphic encryption technology can be used to encrypt sample data to protect data privacy. Then, the encrypted data of the feature items used to describe the patient's disease state is used as the label of the original neural network model; and the encrypted data of the feature items used to describe the patient's clinical performance data is used as the input of the original neural network model data.
- the model training unit 103 further includes:
- the test set selection unit 301 is used to use the non-intersection data between the case data set A and the case data set B as a test set sample;
- the testing unit 302 is configured to test the original neural network model based on the test set samples.
- the non-intersection data between the case data set A and the case data set B is also obtained in the embodiment of the present application, and the The non-intersection data is used as a test set sample, and the trained original neural network model is tested by using the test set sample, so that the prediction effect of the original neural network model is more accurate.
- the federated migration unit 104 includes:
- a model obtaining unit 401 configured to obtain the trained original neural network model
- the federated transfer learning unit 402 is configured to perform federated transfer learning on the original neural network model using the training set samples of various types of neurodegenerative diseases stored in each node;
- the gradient updating unit 403 is configured to update the original neural network model by using the gradient values obtained by training of each node to obtain a final neural network model.
- the trained original neural network model can be obtained first, and then the original neural network model can be generalized to the training of various neurodegenerative diseases.
- the training set samples of various neurodegenerative diseases stored in each node are used for federated transfer learning.
- the three diseases share commonalities in etiology, clinical manifestations, and patient population.
- patients with the three diseases all have oxidative damage to the nerve tissue and the phenomenon of increased mitochondria in the brain, and some patients have the clinical manifestations of the three diseases at the same time.
- Federated transfer learning can complete the training of disease detection models under the premise of ensuring patient privacy, and at the same time generalize the trained models to the detection of other neurodegenerative diseases, and finally train a model that can detect the prevalence of neurodegenerative diseases. Model.
- the node may be the local end of the hospital, and the local end of each hospital has different training set samples.
- node A has the training set samples of Parkinson's syndrome
- node B has the training set samples of Huntington's syndrome
- node A has the training set samples of Parkinson's syndrome.
- C has the training set samples of mandatory lateral sclerosis, so the original neural network model can be trained locally on each node, so that the data of each node will not be leaked, and the privacy of the data is protected. Then, the original neural network model is updated by using the gradient values obtained by training each node to obtain the final neural network model.
- the federated transfer learning unit 402 includes:
- the weight judgment unit 501 is used to obtain the model weight of each node in the training process of each round of federated transfer learning, and judge whether the model weight of each node is equal;
- the weight updating unit 502 is configured to update the model weight of each node if the model weight of each node is not equal, so as to be used in the training process of this round of federated transfer learning.
- model weights of different nodes are not equal, the model weights of each node need to be updated, so that each node can still be used in the case of differences in samples. have a consistent learning effect.
- the weight update unit 502 includes:
- the weight update subunit is used to update the model weight of each node according to the following formula:
- ⁇ k represents the model weight of the current node in the current training round
- ⁇ k+1 represents the updated model weight of the current node
- ⁇ is the learning rate
- F k represents the training data of the current node accounts for the total training data of all nodes. proportion.
- the gradient update unit 403 includes:
- a weighted averaging unit 601, configured to perform a weighted average of the gradient values obtained by training each node according to the model weight of each node to obtain an average gradient;
- the gradient update subunit 602 is configured to update the original neural network model by using the average gradient to obtain a final neural network model.
- the model weights of the corresponding nodes are used for weighted average to obtain the average gradient, which represents the contribution provided by the sample training of each node.
- the original neural network model is updated to obtain the final neural network model.
- the data to be measured in each medical record data set can be sequentially input into the final neural network model for calculation, and the obtained results are the respective corresponding disease states.
- the device provided by the embodiment of the present application solves the problem of data island in the process of modeling the prediction model, and can effectively predict the degree of the patient's neurodegenerative disease.
- the above-mentioned neurodegenerative disease modeling apparatus 100 based on federated transfer learning can be implemented in the form of a computer program, and the computer program can be executed on a computer device as shown in FIG. 7 .
- FIG. 7 is a schematic block diagram of a computer device provided by an embodiment of the present application.
- the computer device 700 is a server, and the server may be an independent server or a server cluster composed of multiple servers.
- the computer device 700 includes a processor 702 , a memory and a network interface 705 connected by a system bus 701 , wherein the memory may include a non-volatile storage medium 703 and an internal memory 704 .
- the nonvolatile storage medium 703 can store an operating system 7031 and a computer program 7032 .
- the processor 702 is used to provide computing and control capabilities to support the operation of the entire computer device 700 .
- the internal memory 704 provides an environment for the execution of the computer program 7032 in the non-volatile storage medium 703 .
- the network interface 705 is used for network communication, such as providing transmission of data information.
- the network interface 705 is used for network communication, such as providing transmission of data information.
- FIG. 7 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device 700 to which the solution of the present application is applied.
- the specific computer device 700 may include more or fewer components than shown, or combine certain components, or have a different arrangement of components.
- the processor 702 is configured to run the computer program 7032 stored in the memory, so as to realize the following functions: acquiring the medical record data set A and the medical record data set B of the first type of neurodegenerative disease patients, the medical record data set A It includes multiple pieces of data described by the first feature item set, and the medical record data set B includes multiple pieces of data described by the second feature item set; the first feature item set includes the feature items used to describe the patient's identity and the The characteristic item of the patient's disease state; the second characteristic item set includes the characteristic item used to describe the patient's identity and the characteristic item used to describe the clinical manifestation data of the patient; select the intersection between the case data set A and the case data set B data, the intersection data is used as a training set sample; based on the training set sample, the original neural network model is trained; based on the training set samples of multiple types of neurodegenerative diseases including the first type of neurodegenerative diseases, Federated transfer learning is performed on the original neural network model to obtain a final neural network model for predicting the prevalence of neurode
- the processor 702 when performing the step of training the original neural network model based on the training set samples, performs the following operation: using the training set samples for describing the patient's disease state
- the data of the characteristic item is used as the label of the original neural network model; and the data of the characteristic item used to describe the clinical performance data of the patient is used as the input data of the original neural network model;
- the neural network model is trained to obtain the trained original neural network model.
- the processor 702 when the processor 702 performs the step of training the original neural network model based on the training set samples, the processor 702 further performs the following operation: converting the data between the case data set A and the case data set B The non-intersecting set of data is used as a test set sample; the original neural network model is tested based on the test set sample.
- the processor 702 performs federated transfer learning on the original neural network model when executing the training set samples based on multiple types of neurodegenerative diseases including the first type of neurodegenerative diseases, to obtain
- the following operations are also performed: obtaining the trained original neural network model; using the training sets of various types of neurodegenerative diseases stored in each node
- the sample performs federated transfer learning on the original neural network model; the original neural network model is updated by using the gradient values obtained from the training of each node to obtain the final neural network model.
- the processor 702 when the processor 702 performs the step of performing federated transfer learning on the original neural network model using the training set samples of various types of neurodegenerative diseases stored in each node, the processor 702 performs the following operations: During the training process of the federated transfer learning round, the model weights of each node are obtained to determine whether the model weights of each node are equal; if the model weights of each node are not equal, the model weights of each node are updated for the current round of federation. The training process of transfer learning.
- the processor 702 performs the following operations when performing the step of updating the model weights of each node for use in the training process of the current round of federated transfer learning if the model weights of the nodes are not equal : Update the model weight of each node according to the following formula: ⁇ k+1 ⁇ k + ⁇ F k ( ⁇ k ), ⁇ k represents the model weight of the current node in the current training round, ⁇ k+1 represents the current node The updated model weight, ⁇ is the learning rate, and F k represents the proportion of the training data of the current node to the total training data of all nodes.
- the processor 702 performs the following operations when performing the step of updating the original neural network model by using the gradient values obtained from the training of each node to obtain the final neural network model: according to the model weight of each node A weighted average is performed on the gradient values obtained from the training of each node to obtain an average gradient; the original neural network model is updated by using the average gradient to obtain a final neural network model.
- the processor 702 uses the data of the characteristic items in the training set samples to describe the patient's disease state as the label of the original neural network model; and will be used to describe the clinical symptoms of the patient.
- the data representing the characteristic item of the data is used as the input data of the original neural network model, the following operations are performed: using homomorphic encryption technology to encrypt the data in the training set samples.
- the embodiment of the computer device shown in FIG. 7 does not constitute a limitation on the specific structure of the computer device.
- the computer device may include more or less components than those shown in the drawings. Either some components are combined, or different component arrangements.
- the computer device may only include a memory and a processor. In such an embodiment, the structures and functions of the memory and the processor are the same as those of the embodiment shown in FIG. 7 , and details are not repeated here.
- the processor 702 may be a central processing unit (Central Processing Unit, CPU), and the processor 702 may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
- the general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like.
- a computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium.
- the computer-readable storage medium stores a computer program, wherein when the computer program is executed by the processor, the following steps are implemented: obtaining a medical record data set A and a medical record data set B of a patient with a first type of neurodegenerative disease, wherein the medical record data set A includes Multiple pieces of data described by the first feature item set, the medical record data set B includes multiple pieces of data described by the second feature item set; the first feature item set includes the feature items used to describe the patient's identity and The characteristic item of the diseased state; the second characteristic item set includes the characteristic item used to describe the patient's identity and the characteristic item used to describe the clinical manifestation data of the patient; select the intersection data from the case data set A and the case data set B , using the intersection data as a training set sample; based on the training set sample, the original neural network model is trained;
- the disclosed devices and apparatuses may be implemented in other manners.
- the apparatus embodiments described above are only illustrative.
- the division of the units is only logical function division.
- there may be other division methods, or units with the same function may be grouped into one Units, such as multiple units or components, may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
- the shown or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may also be electrical, mechanical or other forms of connection.
- the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solutions of the embodiments of the present application.
- each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
- the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
- the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a storage medium.
- the technical solutions of the present application are essentially or part of contributions to the prior art, or all or part of the technical solutions can be embodied in the form of software products, and the computer software products are stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the various embodiments of the present application.
- the aforementioned storage medium includes: a U disk, a removable hard disk, a read-only memory (ROM, Read-Only Memory), a magnetic disk or an optical disk and other media that can store program codes.
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Abstract
基于联邦迁移学习的神经退行性疾病建模装置及相关设备,涉及人工智能技术领域,可应用于智慧医院***中,装置包括:数据集获取单元(101),用于获取第一类神经退行性疾病患者的病历数据集A和病历数据集B;交集选取单元(102),用于从所述病历数据集A和病历数据集B之间选取交集数据,将所述交集数据作为训练集样本;模型训练单元(103),用于基于所述训练集样本,对原始神经网络模型进行训练;联邦迁移单元(104),用于基于多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。解决了建模过程的数据孤岛问题,可有效预测患者患病程度。
Description
本申请要求于2020年11月24日提交中国专利局、申请号为202011329754.3,发明名称为“基于联邦迁移学习的神经退行性疾病建模装置及相关设备”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
本申请涉及人工智能技术领域和数字医疗领域,具体涉及AI+医疗领域,特别涉及基于联邦迁移学习的神经退行性疾病建模装置及相关设备。
神经退行性疾病是由神经元和其髓鞘的功能丧失所致的,且会随着时间的推移而恶化,出现各种功能障碍。国内老龄化人口数量的日益增长,我国患神经退行性疾病的老年人越来越多。随着世界各国医疗科研水平的不断发展,各医疗机构对神经退行性疾病的研究方法日趋完善。业内现对于神经退行性疾病的研究聚焦在不同病种的成因研究,同时也有很多治疗的新思路提出。目前的研究成果表明,神经退行性疾病的成因通常有氧化应激、线粒体功能障碍、兴奋性毒素和免疫炎症。该病症种类繁多,包括认知和行为障碍等方面。同时,病理变化不可逆,往往在患者出现认知障碍的时候病程已到中晚期,此时治疗只能延缓病情的发展,不能从根本上逆转神经网络的损伤。因此,对神经性疾病应该尽早诊断与治疗。
在病理研究方面,业内更多的聚焦在单一疾病的病理研究方面,因为患者的检测信息是隐私信息,不方便直接获取与使用;同时,神经退行性疾病的患病人的区域分布不均导致数据采集不便利。另外,在疾病治疗方面,大部分医生从医多年只能遇到几例患者导致了学者无法通过大量的病情数据去详细且完整地对神经退行性疾病的具体病因进行研究,也不利于对症诊治。各医疗机构在智慧医疗场景下提出了诸多设想方案,但发明人意识到由于各机构可供研究的数据量不足,医疗数据预处理及标注所需投入巨大,当前医疗场景下的智能诊断***尚且不完善,在神经退行性疾病患病程度预测方面的诸多设想暂未落地。
申请内容
本申请的目的是提供基于联邦迁移学习的神经退行性疾病建模装置及相关设备,旨在解决现有神经退行性疾病患病程度预测装置无法在保证隐私前提下有效建模的问题。
第一方面,本申请实施例提供一种基于联邦迁移学习的神经退行性疾病建模装置,其中,包括:
数据集获取单元,用于获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;
交集选取单元,用于从所述病例数据集A和病例数据集B之间选取交集数据,将所述 交集数据作为训练集样本;
模型训练单元,用于基于所述训练集样本,对原始神经网络模型进行训练;
联邦迁移单元,用于基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
第二方面,本申请实施例提供一种计算机设备,包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序,其中,所述处理器执行所述计算机程序时实现以下步骤:获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;从所述病例数据集A和病例数据集B之间选取交集数据,将所述交集数据作为训练集样本;基于所述训练集样本,对原始神经网络模型进行训练;基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
第三方面,本申请实施例提供一种计算机可读存储介质,其中,所述计算机可读存储介质存储有计算机程序,所述计算机程序当被处理器执行时使所述处理器执行如下步骤:获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;从所述病例数据集A和病例数据集B之间选取交集数据,将所述交集数据作为训练集样本;基于所述训练集样本,对原始神经网络模型进行训练;基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
本申请实施例提供了基于联邦迁移学习的神经退行性疾病建模装置及相关设备,装置包括:数据集获取单元,用于获取第一类神经退行性疾病患者的病历数据集A和病历数据集B;交集选取单元,用于从所述病例数据集A和病例数据集B之间选取交集数据,将所述交集数据作为训练集样本;模型训练单元,用于基于所述训练集样本,对原始神经网络模型进行训练;联邦迁移单元,用于基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。本申请实施例解决了预测模型建模过程中的数据孤岛问题,可有效预测患者神经退行性疾病的患病程度。
为了更清楚地说明本申请实施例技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图是本申请的一些实施例,对于本领域普通技术 人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的基于联邦迁移学习的神经退行性疾病建模装置的示意性框图;
图2为本申请实施例提供的基于联邦迁移学习的神经退行性疾病建模装置的子单元示意性框图;
图3为本申请实施例提供的基于联邦迁移学习的神经退行性疾病建模装置的另一子单元示意性框图;
图4为本申请实施例提供的基于联邦迁移学习的神经退行性疾病建模装置的另一子单元示意性框图;
图5为本申请实施例提供的基于联邦迁移学习的神经退行性疾病建模装置的另一子单元示意性框图;
图6为本申请实施例提供的基于联邦迁移学习的神经退行性疾病建模装置的另一子单元示意性框图;
图7为本申请实施例提供的计算机设备的示意性框图。
下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。
应当理解,当在本说明书和所附权利要求书中使用时,术语“包括”和“包含”指示所描述特征、整体、步骤、操作、元素和/或组件的存在,但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。
还应当理解,在此本申请说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本申请。如在本申请说明书和所附权利要求书中所使用的那样,除非上下文清楚地指明其它情况,否则单数形式的“一”、“一个”及“该”意在包括复数形式。
还应当进一步理解,在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合,并且包括这些组合。
请参阅图1,图1为本申请实施例提供的基于联邦迁移学习的神经退行性疾病建模装置100的示意性框图,该装置包括数据集获取单元101、交集选取单元102、模型训练单元103和联邦迁移单元104:
数据集获取单元101,用于获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;
交集选取单元102,用于从所述病例数据集A和病例数据集B之间选取交集数据,将所 述交集数据作为训练集样本;
模型训练单元103,用于基于所述训练集样本,对原始神经网络模型进行训练;
联邦迁移单元104,用于基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
本实施例中,所述数据集获取单元101中,所述病历数据集A和所述病历数据集B均包括多条数据,并且病历数据集A和所述病历数据集B的数据类型有交叉的部分也有不交叉的部分。具体的,所述病历数据集A中包含由第一特征项集描述的多条数据,第一特征项集包括用于描述患者身份的特征项(例如张三,男性,65岁等)以及用于描述患者患病状态的特征项(例如患帕金森综合症程度20%)。所述病历数据集B包括由第二特征项集描述的多条数据,其中第二特征项集包括用于描述患者身份的特征项(其与第一特征项集对应的特征项相同,例如张三,男性,65岁等)以及用于描述患者临床表现数据的特征项(例如睡眠少,行动能力减弱,间歇性狂躁等)。
其中的第一类神经退行性疾病可以是任意一种神经退行性疾病,例如帕金森综合症、强制性脊柱侧索硬化症或亨廷顿综合症。本申请实施例优选的是采用帕金森综合症,因为需要利用第一类神经退行性疾病进行原始模型的建模和训练,所以需要一些基础的数据量,故采用帕金森综合症有利于原始模型的训练。
在所述交集选取单元102中,从所述病例数据集A和病例数据集B之间选取交集数据。因数据来源不同,故会存在数据缺失、不完整等情况,交集数据为病例中记录的某位患者既记录了临床表现,又记录了患病状态的数据。
本申请实施例可以取病历数据集A和病历数据集B的交集数据作为训练集样本,这样可以使得模型能够最大化学习病历数据集A与病历数据集B的共同特征,也避免了样本差异化对模型产生的影响。
在所述模型训练单元103中,利用前面得到的训练集样本,对原始神经网络模型进行训练。本单元是先对原始神经网络模型进行训练,后续单元中再将此训练的原始神经网络模型泛化到其他神经退行性疾病的患病程度预测当中。
在一实施例中,如图2所示,所述模型训练单元103包括:
数据输入单元201,用于将所述训练集样本中用于描述患者患病状态的特征项的数据,作为所述原始神经网络模型的标签;以及将用于描述患者临床表现数据的特征项的数据,作为所述原始神经网络模型的输入数据;
模型训练子单元202,用于基于所述输入数据对所述原始神经网络模型进行训练,得到训练好的所述原始神经网络模型。
在本实施例中,利用用于描述患者患病状态的特征项的数据,作为所述原始神经网络模型的标签,以及将用于描述患者临床表现数据的特征项的数据,作为所述原始神经网络模型的输入数据,这样可以使得患者表现与患病状态呈现对应关系,该原始神经网络模型是基于患者行为及其特征对患者的患病程度进行预测的。在模型训练子单元202中,可利用联邦学 习的方式对原始神经网络模型进行训练,从而得到用于预测第一类神经退行性疾病患病程度的原始神经网络模型。
在一实施例中,所述数据输入单元201包括:
加密单元,用于采用同态加密技术对所述训练集样本中的数据进行加密。
即在训练时,可采用同态加密技术对样本数据进行加密,以保护数据隐私。然后再将加密后用于描述患者患病状态的特征项的数据,作为原始神经网络模型的标签;以及将加密后用于描述患者临床表现数据的特征项的数据,作为原始神经网络模型的输入数据。
在一实施例中,如图3所示,所述模型训练单元103还包括:
测试集选取单元301,用于将所述病例数据集A和病例数据集B之间的非交集数据作为测试集样本;
测试单元302,用于基于所述测试集样本对所述原始神经网络模型进行测试。
本实施例中,由于神经退行性疾病的病情数据较少,本申请实施例为了充分利用现有的数据,还获取所述病例数据集A和病例数据集B之间的非交集数据,并将非交集数据作为测试集样本,利用所述测试集样本对训练后的原始神经网络模型进行测试,从而原始神经网络模型预测效果更准确。
在一实施例中,如图4所示,所述联邦迁移单元104包括:
模型获取单元401,用于获取所述训练好的所述原始神经网络模型;
联邦迁移学习单元402,用于利用各节点存储的各类神经退行性疾病的训练集样本对所述原始神经网络模型进行联邦迁移学习;
梯度更新单元403,用于利用各节点训练得到的梯度值对所述原始神经网络模型进行更新,得到最终神经网络模型。
本申请实施例中,可以先获取训练好的该原始神经网络模型,然后将该原始神经网络模型泛化到各类神经退行性疾病的训练中。
即在联邦迁移学习单元中,利用各节点存储的各类神经退行性疾病的训练集样本进行联邦迁移学习,在神经退行性疾病中,帕金森综合症、亨廷顿综合症和强制性脊柱侧索硬化症三种疾病在病因,临床表现以及病患人群三个方面均有共同点。例如,三种疾病患者病理上均有神经组织的氧化损伤,脑内线粒体增加的现象,部分患者同时具有三种疾病的临床表现等。联邦迁移学习能够在保证患者隐私的前提下完成疾病检测模型的训练,同时将训练好的模型泛化到其他神经退行性疾病的检测中,最终训练出一个可检测神经退行性疾病患病程度的模型。
具体地,所述的节点可以是医院本地端,各医院本地端所拥有的训练集样本不同,可能节点A拥有帕金森综合症的训练集样本,节点B拥有亨廷顿综合症的训练集样本,节点C拥有强制性脊柱侧索硬化症的训练集样本,所以可以在各个节点本地对所述原始神经网络模型进行训练,这样各个节点的数据不会外泄,保护了数据的隐私。然后利用各个节点训练得到的梯度值对原始神经网络模型进行更新,从而得到最终神经网络模型。
在一实施例中,如图5所示,所述联邦迁移学习单元402包括:
权重判断单元501,用于在每一轮联邦迁移学习的训练过程中,获取各节点的模型权重,判断各节点的模型权重是否相等;
权重更新单元502,用于若各节点的模型权重不相等,则对各节点的模型权重进行更新,以用于本轮联邦迁移学习的训练过程。
在本实施例中,每一轮联邦迁移学习训练过程中,若不同节点的模型权重不等,则需要对各节点的模型权重进行更新,由此使得各节点能够在样本存在差异的情况下依然具有一致的学习效果。
在一实施例中,所述权重更新单元502包括:
权重更新子单元,用于按下述公式对各节点的模型权重进行更新:
ω
k+1←ω
k+μF
k(ω
k)
ω
k表示当前节点在当前训练轮次的模型权重,ω
k+1表示当前节点更新后的模型权重,μ为学习率,F
k表示当前节点的训练数据量占所有节点的总训练数据量的比重。
按照上述更新方式进行权重更新之后进行模型训练,然后在下一轮联邦迁移学习的训练过程中,继续判断模型权重是否相等,直到各模型权值相等,训练停止,此过程中的模型权重均使用同态加密技术进行加密以保证数据安全。
在一实施例中,如图6所示,所述梯度更新单元403包括:
加权平均单元601,用于按各节点的模型权重对各节点训练得到的梯度值进行加权平均,得到平均梯度;
梯度更新子单元602,用于利用所述平均梯度对所述原始神经网络模型进行更新,得到最终神经网络模型。
本实施例中,对于各节点训练后得到的梯度值,采用对应节点的模型权重进行加权平均,从而得到平均梯度,该平均梯度代表了各个节点的样本训练提供的贡献,然后利用利用平均梯度对原始神经网络模型进行更新,从而得到最终神经网络模型。
在得到最终神经网络模型后,可以将各个病历数据集中待测数据依次输入最终神经网络模型中进行计算,得到的结果即为各自对应的患病状态。
本申请实施例提供的装置解决了预测模型建模过程中的数据孤岛问题,可有效预测患者神经退行性疾病的患病程度。
上述基于联邦迁移学习的神经退行性疾病建模装置100可以实现为计算机程序的形式,该计算机程序可以在如图7所示的计算机设备上运行。
请参阅图7,图7是本申请实施例提供的计算机设备的示意性框图。该计算机设备700是服务器,服务器可以是独立的服务器,也可以是多个服务器组成的服务器集群。
参阅图7,该计算机设备700包括通过***总线701连接的处理器702、存储器和网络接口705,其中,存储器可以包括非易失性存储介质703和内存储器704。
该非易失性存储介质703可存储操作***7031和计算机程序7032。
该处理器702用于提供计算和控制能力,支撑整个计算机设备700的运行。
该内存储器704为非易失性存储介质703中的计算机程序7032的运行提供环境。
该网络接口705用于进行网络通信,如提供数据信息的传输等。本领域技术人员可以理解,图7中示出的结构,仅仅是与本申请方案相关的部分结构的框图,并不构成对本申请方案所应用于其上的计算机设备700的限定,具体的计算机设备700可以包括比图中所示更多或更少的部件,或者组合某些部件,或者具有不同的部件布置。
其中,所述处理器702用于运行存储在存储器中的计算机程序7032,以实现如下功能:获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;从所述病例数据集A和病例数据集B之间选取交集数据,将所述交集数据作为训练集样本;基于所述训练集样本,对原始神经网络模型进行训练;基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
在一实施例中,处理器702在执行所述基于所述训练集样本,对原始神经网络模型进行训练的步骤时,执行如下操作:将所述训练集样本中用于描述患者患病状态的特征项的数据,作为所述原始神经网络模型的标签;以及将用于描述患者临床表现数据的特征项的数据,作为所述原始神经网络模型的输入数据;基于所述输入数据对所述原始神经网络模型进行训练,得到训练好的所述原始神经网络模型。
在一实施例中,处理器702在执行所述基于所述训练集样本,对原始神经网络模型进行训练的步骤时,还执行如下操作:将所述病例数据集A和病例数据集B之间的非交集数据作为测试集样本;基于所述测试集样本对所述原始神经网络模型进行测试。
在一实施例中,处理器702在执行所述基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型的步骤时,还执行如下操作:获取所述训练好的所述原始神经网络模型;利用各节点存储的各类神经退行性疾病的训练集样本对所述原始神经网络模型进行联邦迁移学习;利用各节点训练得到的梯度值对所述原始神经网络模型进行更新,得到最终神经网络模型。
在一实施例中,处理器702在执行所述利用各节点存储的各类神经退行性疾病的训练集样本对所述原始神经网络模型进行联邦迁移学习的步骤时,执行如下操作:在每一轮联邦迁移学习的训练过程中,获取各节点的模型权重,判断各节点的模型权重是否相等;若各节点的模型权重不相等,则对各节点的模型权重进行更新,以用于本轮联邦迁移学习的训练过程。
在一实施例中,处理器702在执行所述若各节点的模型权重不相等,则对各节点的模型权重进行更新,以用于本轮联邦迁移学习的训练过程的步骤时,执行如下操作:按下述公式对各节点的模型权重进行更新:ω
k+1←ω
k+μF
k(ω
k),ω
k表示当前节点在当前训练轮次的模型权重,ω
k+1表示当前节点更新后的模型权重,μ为学习率,F
k表示当前节点的训练数据量占所有节点的总训练数据量的比重。
在一实施例中,处理器702在执行所述利用各节点训练得到的梯度值对所述原始神经网络模型进行更新,得到最终神经网络模型的步骤时,执行如下操作:按各节点的模型权重对各节点训练得到的梯度值进行加权平均,得到平均梯度;利用所述平均梯度对所述原始神经网络模型进行更新,得到最终神经网络模型。
在一实施例中,处理器702在执行所述将所述训练集样本中用于描述患者患病状态的特征项的数据,作为所述原始神经网络模型的标签;以及将用于描述患者临床表现数据的特征项的数据,作为所述原始神经网络模型的输入数据的步骤时,执行如下操作:采用同态加密技术对所述训练集样本中的数据进行加密。
本领域技术人员可以理解,图7中示出的计算机设备的实施例并不构成对计算机设备具体构成的限定,在其他实施例中,计算机设备可以包括比图示更多或更少的部件,或者组合某些部件,或者不同的部件布置。例如,在一些实施例中,计算机设备可以仅包括存储器及处理器,在这样的实施例中,存储器及处理器的结构及功能与图7所示实施例一致,在此不再赘述。
应当理解,在本申请实施例中,处理器702可以是中央处理单元(Central Processing Unit,CPU),该处理器702还可以是其他通用处理器、数字信号处理器(Digital Signal Processor,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现成可编程门阵列(Field-Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。其中,通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
在本申请的另一实施例中提供计算机可读存储介质。该计算机可读存储介质可以为非易失性的计算机可读存储介质,也可以为易失性的计算机可读存储介质。该计算机可读存储介质存储有计算机程序,其中计算机程序被处理器执行时实现以下步骤:获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;从所述病例数据集A和病例数据集B之间选取交集数据,将所述交集数据作为训练集样本;基于所述训练集样本,对原始神经网络模型进行训练;基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
本领域普通技术人员可以意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、计算机软件或者二者的结合来实现,为了清楚地说明硬件和软件的可互换性,在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本申请的范围。
在本申请所提供的几个实施例中,应该理解到,所揭露的设备和装置,可以通过其它的方式实现。例如,以上所描述的装置实施例仅仅是示意性的,例如,所述单元的划分,仅仅为逻辑功能划分,实际实现时可以有另外的划分方式,也可以将具有相同功能的单元集合成一个单元,例如多个单元或组件可以结合或者可以集成到另一个***,或一些特征可以忽略,或不执行。另外,所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口、装置或单元的间接耦合或通信连接,也可以是电的,机械的或其它的形式连接。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本申请实施例方案的目的。
另外,在本申请各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个存储介质中。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分,或者该技术方案的全部或部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行本申请各个实施例的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述,仅为本申请的具体实施方式,但本申请的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本申请揭露的技术范围内,可轻易想到各种等效的修改或替换,这些修改或替换都应涵盖在本申请的保护范围之内。因此,本申请的保护范围应以权利要求的保护范围为准。
Claims (20)
- 一种基于联邦迁移学习的神经退行性疾病建模装置,其中,包括:数据集获取单元,用于获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;交集选取单元,用于从所述病例数据集A和病例数据集B之间选取交集数据,将所述交集数据作为训练集样本;模型训练单元,用于基于所述训练集样本,对原始神经网络模型进行训练;联邦迁移单元,用于基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
- 根据权利要求1所述的基于联邦迁移学习的神经退行性疾病建模装置,其中,所述模型训练单元包括:数据输入单元,用于将所述训练集样本中用于描述患者患病状态的特征项的数据,作为所述原始神经网络模型的标签;以及将用于描述患者临床表现数据的特征项的数据,作为所述原始神经网络模型的输入数据;模型训练子单元,用于基于所述输入数据对所述原始神经网络模型进行训练,得到训练好的所述原始神经网络模型。
- 根据权利要求1所述的基于联邦迁移学习的神经退行性疾病建模装置,其中,所述模型训练单元还包括:测试集选取单元,用于将所述病例数据集A和病例数据集B之间的非交集数据作为测试集样本;测试单元,用于基于所述测试集样本对所述原始神经网络模型进行测试。
- 根据权利要求1所述的基于联邦迁移学习的神经退行性疾病建模装置,其中,所述联邦迁移单元包括:模型获取单元,用于获取所述训练好的所述原始神经网络模型;联邦迁移学习单元,用于利用各节点存储的各类神经退行性疾病的训练集样本对所述原始神经网络模型进行联邦迁移学习;梯度更新单元,用于利用各节点训练得到的梯度值对所述原始神经网络模型进行更新,得到最终神经网络模型。
- 根据权利要求4所述的基于联邦迁移学习的神经退行性疾病建模装置,其中,所述联邦迁移学习单元包括:权重判断单元,用于在每一轮联邦迁移学习的训练过程中,获取各节点的模型权重,判断各节点的模型权重是否相等;权重更新单元,用于若各节点的模型权重不相等,则对各节点的模型权重进行更新,以用于本轮联邦迁移学习的训练过程。
- 根据权利要求5所述的基于联邦迁移学习的神经退行性疾病建模装置,其中,所述权重更新单元包括:权重更新子单元,用于按下述公式对各节点的模型权重进行更新:ω k+1←ω k+μF k(ω k)ω k表示当前节点在当前训练轮次的模型权重,ω k+1表示当前节点更新后的模型权重,μ为学习率,F k表示当前节点的训练数据量占所有节点的总训练数据量的比重。
- 根据权利要求5所述的基于联邦迁移学习的神经退行性疾病建模装置,其中,所述梯度更新单元包括:加权平均单元,用于按各节点的模型权重对各节点训练得到的梯度值进行加权平均,得到平均梯度;梯度更新子单元,用于利用所述平均梯度对所述原始神经网络模型进行更新,得到最终神经网络模型。
- 根据权利要求2所述的基于联邦迁移学习的神经退行性疾病建模装置,其中,所述数据输入单元包括:加密单元,用于采用同态加密技术对所述训练集样本中的数据进行加密。
- 一种计算机设备,包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序,其中,所述处理器执行所述计算机程序时实现以下步骤:获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;从所述病例数据集A和病例数据集B之间选取交集数据,将所述交集数据作为训练集样本;基于所述训练集样本,对原始神经网络模型进行训练;基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
- 根据权利要求9所述的计算机设备,其中,基于所述训练集样本,对原始神经网络模型进行训练,包括:将所述训练集样本中用于描述患者患病状态的特征项的数据,作为所述原始神经网络模型的标签;以及将用于描述患者临床表现数据的特征项的数据,作为所述原始神经网络模型的输入数据;基于所述输入数据对所述原始神经网络模型进行训练,得到训练好的所述原始神经网络模型。
- 根据权利要求9所述的计算机设备,其中,基于所述训练集样本,对原始神经网络模型进行训练,还包括:将所述病例数据集A和病例数据集B之间的非交集数据作为测试集样本;基于所述测试集样本对所述原始神经网络模型进行测试。
- 根据权利要求9所述的计算机设备,其中,基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型,包括:获取所述训练好的所述原始神经网络模型;利用各节点存储的各类神经退行性疾病的训练集样本对所述原始神经网络模型进行联邦迁移学习;利用各节点训练得到的梯度值对所述原始神经网络模型进行更新,得到最终神经网络模型。
- 根据权利要求12所述的计算机设备,其中,利用各节点存储的各类神经退行性疾病的训练集样本对所述原始神经网络模型进行联邦迁移学习,包括:在每一轮联邦迁移学习的训练过程中,获取各节点的模型权重,判断各节点的模型权重是否相等;若各节点的模型权重不相等,则对各节点的模型权重进行更新,以用于本轮联邦迁移学习的训练过程。
- 根据权利要求13所述的计算机设备,其中,若各节点的模型权重不相等,则对各节点的模型权重进行更新,以用于本轮联邦迁移学习的训练过程,包括:按下述公式对各节点的模型权重进行更新:ω k+1←ω k+μF k(ω k)ω k表示当前节点在当前训练轮次的模型权重,ω k+1表示当前节点更新后的模型权重,μ为学习率,F k表示当前节点的训练数据量占所有节点的总训练数据量的比重。
- 一种计算机可读存储介质,其中,所述计算机可读存储介质存储有计算机程序,所述计算机程序当被处理器执行时使所述处理器执行如下步骤:获取第一类神经退行性疾病患者的病历数据集A和病历数据集B,所述病历数据集A包括由第一特征项集描述的多条数据,所述病历数据集B包括由第二特征项集描述的多条数据;第一特征项集包括用于描述患者身份的特征项以及用于描述患者患病状态的特征项;第二特征项集包括用于描述患者身份的特征项以及用于描述患者临床表现数据的特征项;从所述病例数据集A和病例数据集B之间选取交集数据,将所述交集数据作为训练集样本;基于所述训练集样本,对原始神经网络模型进行训练;基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型。
- 根据权利要求15所述的计算机可读存储介质,其中,基于所述训练集样本,对原始神经网络模型进行训练,包括:将所述训练集样本中用于描述患者患病状态的特征项的数据,作为所述原始神经网络模型的标签;以及将用于描述患者临床表现数据的特征项的数据,作为所述原始神经网络模型的输入数据;基于所述输入数据对所述原始神经网络模型进行训练,得到训练好的所述原始神经网络模型。
- 根据权利要求15所述的计算机可读存储介质,其中,基于所述训练集样本,对原始神经网络模型进行训练,还包括:将所述病例数据集A和病例数据集B之间的非交集数据作为测试集样本;基于所述测试集样本对所述原始神经网络模型进行测试。
- 根据权利要求15所述的计算机可读存储介质,其中,基于包含第一类神经退行性疾病在内的多类神经退行性疾病的训练集样本,对所述原始神经网络模型进行联邦迁移学习,得到用于预测神经退行性疾病患病程度的最终神经网络模型,包括:获取所述训练好的所述原始神经网络模型;利用各节点存储的各类神经退行性疾病的训练集样本对所述原始神经网络模型进行联邦迁移学习;利用各节点训练得到的梯度值对所述原始神经网络模型进行更新,得到最终神经网络模型。
- 根据权利要求18所述的计算机可读存储介质,其中,利用各节点存储的各类神经退行性疾病的训练集样本对所述原始神经网络模型进行联邦迁移学习,包括:在每一轮联邦迁移学习的训练过程中,获取各节点的模型权重,判断各节点的模型权重是否相等;若各节点的模型权重不相等,则对各节点的模型权重进行更新,以用于本轮联邦迁移学习的训练过程。
- 根据权利要求19所述的计算机可读存储介质,其中,若各节点的模型权重不相等,则对各节点的模型权重进行更新,以用于本轮联邦迁移学习的训练过程,包括:按下述公式对各节点的模型权重进行更新:ω k+1←ω k+μF k(ω k)ω k表示当前节点在当前训练轮次的模型权重,ω k+1表示当前节点更新后的模型权重,μ为学习率,F k表示当前节点的训练数据量占所有节点的总训练数据量的比重。
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