WO2023123913A1 - Procédé et appareil d'apprentissage de modèle de test d'affection médicale, procédé et appareil de test et dispositif électronique - Google Patents

Procédé et appareil d'apprentissage de modèle de test d'affection médicale, procédé et appareil de test et dispositif électronique Download PDF

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WO2023123913A1
WO2023123913A1 PCT/CN2022/100159 CN2022100159W WO2023123913A1 WO 2023123913 A1 WO2023123913 A1 WO 2023123913A1 CN 2022100159 W CN2022100159 W CN 2022100159W WO 2023123913 A1 WO2023123913 A1 WO 2023123913A1
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linear
data
sign data
multiple regression
regression model
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PCT/CN2022/100159
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Chinese (zh)
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余晓填
蚁韩羚
王爱波
王孝宇
陈宁
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深圳云天励飞技术股份有限公司
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    • 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
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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  • This application relates to the field of big data analysis, in particular to a disease detection model training, detection method, device and equipment.
  • the inspection results are often judged by human beings, which is very inaccurate.
  • more and more researchers began to conduct research on the detection of target diseases based on big data based on sign data.
  • a neural network with a large capacity is usually required for data training.
  • the detection process is a data In the process of running the entire neural network operation, the detection efficiency is also not high. Therefore, on the premise of ensuring the accuracy of detection, how to improve the efficiency of medical staff in identifying the condition of the target person based on the physical signs data is an urgent problem to be solved.
  • the embodiments of the present application provide a disease detection model training, detection method, device, and equipment, thereby improving the efficiency of identifying the disease of a target person based on sign data.
  • the present application provides a disease detection model training method, the method comprising: obtaining a training sample used to characterize the target disease, the training sample includes the sign data of a plurality of people and whether each person is sick Data label; use the training sample to train the preset multiple regression model, the multiple regression model includes linear parameters, and the linear parameters are used to perform linear dimension reduction on the sign data of the person to calculate whether the person has The result of the disease; when the preset condition is satisfied, the multiple regression model is output, and the disease detection model is constructed according to the multiple regression model.
  • the acquiring training samples used to characterize the target disease includes: acquiring a plurality of first sign data marked with disease labels, and using the first sign data as positive samples; acquiring a plurality of labeled There is second sign data with no disease label, and the second sign data is used as a negative sample; each positive sample and negative sample are standardized at the same sampling frequency; the normalized positive sample and the negative sample A sample set is formed to generate the training samples.
  • the method before training the preset multiple regression model, further includes: taking the sum of the first variable and the first noise parameter as a first linear expression, the first variable being the The product of the sign data and the preset first linear parameter, the first noise parameter is used to characterize the noise of the sign data; the sum of the second variable and the second noise parameter is used as a second linear expression, and the first The second variable is the product of the output result of the first linear expression and a preset second linear parameter, the second noise parameter is used to characterize the noise of the output result of the first linear expression, and the second linear expression
  • the output result of the formula is used to predict whether the person corresponding to the sign data is sick, and the preset first linear parameter and the preset second linear parameter are the linear parameters of the multiple regression model; the first The combination of the linear expression and the second linear expression constitutes the multiple regression model.
  • using the training samples to train the preset multiple regression model includes: respectively transforming the first linear expression and the second linear expression into forms representing the first noise parameter and the second noise parameter ; Establishing an objective function based on the transformed first linear expression and the second linear expression; Substituting the sign data and the data label into the objective function for iterative calculation, and performing an iterative calculation on the preset first linear parameter and The preset second linear parameter is adjusted so that the norm of the first noise parameter and the norm of the second noise parameter are decremented; output at least one set of adjusted linear parameters, and based on the adjusted The linear parameters of generate at least one corresponding multiple regression model to complete the training.
  • the constructing the disease detection model according to the multiple regression model includes: if using the training samples to train the preset multiple regression model to obtain multiple multiple regression models, then calculating multiple The expression of the mean value of the output results of the multiple regression model is used as the disease detection model.
  • the present application provides a disease detection method, the method comprising: acquiring the sign data of the target person; substituting the sign data into the disease detection model generated according to any optional implementation manner of the first aspect. Calculation: judging whether the target person suffers from the target disease according to the output result of the disease detection model.
  • the present application provides a disease detection model training device, the device includes: a data collection module, used to obtain training samples used to characterize the target disease, the training samples include a plurality of people's sign data and The data label of whether each person is sick; the model training module is used to use the training sample to train the preset multiple regression model, and the multiple regression model includes linear parameters, and the linear parameters are used for the sign data of the personnel Carry out linear dimensionality reduction to calculate the result of whether the person is sick; the model output module is used to output the multiple regression model when the preset conditions are met, and construct the disease detection model according to the multiple regression model.
  • the present application provides a disease detection device, the device includes: a second data collection module, which acquires the physical sign data of the target person; a calculation module, which is used to substitute the physical sign data into any The disease detection model generated in an optional implementation manner is used for calculation; the result output module is used to judge whether the target person suffers from the target disease according to the output result of the disease detection model.
  • the present application provides an electronic device, including: a memory and a processor, the memory and the processor are communicatively connected to each other, and computer instructions are stored in the memory, and the processor executes the The computer has instructions to execute the method described in the first aspect, the second aspect, or any optional implementation manner of the first aspect and the second aspect.
  • an embodiment of the present application provides a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the first aspect and the second aspect, Or the method described in any optional implementation manner of the first aspect and the second aspect.
  • the technical solution provided by this application obtains a large amount of physical sign data, including but not limited to body temperature data, blood oxygen saturation data, and heart rate data as training samples, and then according to the data noise that can be measured from the collected data, establishes the multidimensional data through linear parameters.
  • Reduce the multiple regression model of the one-dimensional recognition result so as to reduce the data noise of the body temperature data, the data noise of the blood oxygen saturation data and the data noise of the heart rate data, and use the training samples to train the linear parameters of the multiple regression model. Therefore, after the linear parameter training is completed, the multiple regression model based on simple linear calculation can be used to calculate the sign data of the target person, so as to determine whether the target person is sick.
  • the calculation amount is far less than Machine learning models such as large-capacity neural networks, as a diagnostic model for preliminary diagnosis of diseases, can greatly improve diagnostic efficiency.
  • Fig. 1 shows a schematic diagram of the steps of a disease detection model training method in an embodiment of the present application
  • Figure 2 shows a schematic diagram of the steps of a disease detection method in an embodiment of the present application
  • Fig. 3 shows a schematic structural diagram of a disease detection model training device in an embodiment of the present application
  • Fig. 4 shows a schematic structural diagram of a disease detection device in an embodiment of the present application
  • Fig. 5 shows a schematic structural diagram of an electronic device in an embodiment of the present application.
  • a kind of disease detection model training method specifically comprises the following steps:
  • Step S101 Obtain a training sample used to characterize the target disease, the training sample includes the sign data of multiple people and the data label of whether each person is sick or not. Specifically, in this embodiment, at least based on the sign data of a large number of people, including but not limited to body temperature data, blood oxygen saturation data, heart rate data, and tags of whether each person is sick (for example, the tag of a sick person is " 1", the label of the person without the disease is "0") as the training sample of the multi-symptom dimension, so that when the big data analysis model established in the subsequent steps is trained, the external manifestations of the target person's disease are considered from various aspects, Thereby improving the training accuracy of the big data analysis model.
  • the sign data of a large number of people including but not limited to body temperature data, blood oxygen saturation data, heart rate data, and tags of whether each person is sick (for example, the tag of a sick person is " 1", the label of the person without the disease is "0") as the training sample of the multi-symptom dimension, so that when the big
  • Step S102 use the training samples to train the preset multiple regression model, the multiple regression model includes linear parameters, and the linear parameters are used to linearly reduce the dimension of the person's sign data to calculate whether the person is sick or not.
  • Step S103 When the preset condition is satisfied, a multiple regression model is output, and a disease detection model is constructed according to the multiple regression model.
  • this embodiment optimizes the model parameters based on the data noise that is easily detected by various types of sign data. objective function. Therefore, through the above steps, the establishment of a machine learning model based on the multiple regression algorithm is realized, so that the subsequent means of detecting human diseases based on physical signs data is essentially a simple linear calculation, which can greatly improve the efficiency of disease detection.
  • the preset conditions that can be met by the training process include but are not limited to: end the training when the number of training times reaches the preset number of times; end when the linear parameters of the multiple regression model tend to be stable, and the amount of change is less than the preset threshold train.
  • the multivariate regression model that has been trained is used as the disease detection model, and the detection of the target disease based on the multiple regression model can be realized.
  • step S101 specifically includes the following steps:
  • Step 1 Obtain multiple first sign data marked with disease labels, and use the first sign data as positive samples.
  • Step 2 Obtain a plurality of second sign data marked with undiseased labels, and use the second sign data as negative samples.
  • Step 3 Normalize each positive sample and negative sample with the same sampling frequency.
  • Step 4 Combine the normalized positive samples and negative samples into a sample set to generate training samples.
  • the collection of multiple sign data may have different data sparsity due to different sampling frequencies. If the data with different sparsity is not processed, the training effect will not be accurate. Sampling the data and normalizing the sampling frequency. Then, the standardized positive and negative samples are used to complete the positive and negative training of the multiple regression model, which makes the multiple regression model more accurate.
  • physical sign data includes the following three dimensions, 1. minute-level body temperature x t measured based on the thermometer of the wearable device, 2. ten-minute-level heart rate x h measured based on the smart bracelet, 3. measured based on the smart bracelet Hour-level blood oxygen saturation x b . Since the sign data of the three dimensions have different sampling frequencies, the average method is technically used for data sampling standardization.
  • the hour-level body temperature is the average temperature of the past sixty minutes of history, expressed as x T .
  • the hourly heart rate is the historical sixty-minute heart rate average, expressed as x H .
  • the sign data sample is a three-dimensional vector collected every hour.
  • step S102 the following steps are further included:
  • Step 5 The sum of the first variable and the first noise parameter is used as the first linear expression, the first variable is the product of the sign data and the preset first linear parameter, and the first noise parameter is used to represent the noise of the sign data.
  • Step 6 The sum of the second variable and the second noise parameter is used as the second linear expression, the second variable is the product of the output result of the first linear expression and the preset second linear parameter, and the second noise parameter is used to characterize The noise of the output result of the first linear expression, the output result of the second linear expression is used to predict whether the person corresponding to the sign data is sick, the preset first linear parameter and the preset second linear parameter are the multiple regression model Linear parameters.
  • Step 7 Combine the first linear expression and the second linear expression to form a multiple regression model.
  • Y ⁇ R 1 ⁇ 3 represents the hidden variable (that is, the output result of the first linear expression)
  • X ⁇ R W ⁇ 3 represents the input training samples (that is, sign data), A ⁇ R 1 ⁇ W is the first linear parameter, ⁇ Y ⁇ R 1 ⁇ 3 is the data of body temperature data, blood oxygen saturation data and heart rate data A noise parameter (ie, the first noise parameter).
  • y ⁇ R is used to indicate whether the person is sick or not (that is, the output result of the second linear expression)
  • B ⁇ R 3 ⁇ 1 is the second linear parameter
  • ⁇ y ⁇ R is used after the first linear calculation
  • AX is the first variable
  • YB is the second variable.
  • the actual collected sign data X contains data noise, which can be calculated by but not limited to the variance formula. If it is assumed that there are linear parameters A and B.
  • the useful components in the sign data X can be extracted, so that the dimensionally reduced one-dimensional data contains the most useful components and the least noise components. After two linear processes, the noise components at input and the noise at output The components can be expressed as:
  • step S102 specifically includes the following steps:
  • Step 8 respectively transforming the first linear expression and the second linear expression into forms representing the first noise parameter and the second noise parameter;
  • Step 9 Establishing an objective function based on the transformed first linear expression and the second linear expression
  • Step 10 Substituting the sign data and data labels into the objective function for iterative calculation, adjusting the preset first linear parameter and the preset second linear parameter, so that the norm of the first noise parameter and the norm of the second noise parameter The norm is decremented.
  • Step eleven output at least one set of adjusted linear parameters, and generate at least one corresponding multiple regression model based on the adjusted linear parameters to complete the training.
  • ⁇ ⁇ (0, 1) is an artificially adjustable hyperparameter
  • N is the number of negative samples
  • M is the number of positive samples
  • the output y of positive samples is 1, indicating disease
  • the output y of negative samples is 0,
  • 2 is the norm of the first noise parameter
  • 2 is the norm of the second noise parameter.
  • the optimization of the first linear parameter and the second linear parameter is realized through the above-mentioned optimization objective function.
  • the objective function is a linear parameter parameter in It is a random assignment. Even for the same training sample, after multiple rounds of training, it is possible to obtain multiple sets of different linear parameters that meet the constraints of the objective function.
  • the linear parameters of the first training may be more focused on body temperature data, and the linear parameters of the second training may be more focused on blood oxygen saturation data.
  • multiple rounds of training can be performed to generate multiple sets of linear parameters, and then the obtained multiple sets of linear parameters are substituted into the multiple regression model to obtain Multiple trained models that focus on different dimensions, and then input the sign data into each model to obtain multiple prediction results, and then average the prediction results, and combine them to obtain the final prediction result, so that the disease prediction based on the sign data is in Considering the impact of data in various dimensions in a deeper level makes the prediction probability of whether the target person is sick or not more accurate.
  • step S103 specifically includes the following steps:
  • Step 12 If multiple regression models are obtained by using the training samples to train the preset multiple regression model, the expression for calculating the average value of the output results of the multiple regression models is used as the disease detection model.
  • the disease detection model does not directly use a single multiple regression model, but averages the output values of multiple multiple regression models.
  • the detailed principle description of this step please refer to the relevant descriptions of the above steps 10 to 11, and will not be repeated here.
  • a disease detection method specifically includes the following steps:
  • Step S201 Obtain the physical sign data of the target person.
  • Step S202 Substituting the sign data into the disease detection model generated by the above training method for calculation.
  • Step S203 Determine whether the target person suffers from the target disease according to the output result of the disease detection model.
  • the technical solution provided by this application obtains a large amount of physical sign data, including but not limited to body temperature data, blood oxygen saturation data, and heart rate data as training samples, and then establishes a linear parameter based on the data noise that can be measured from the collected data.
  • Reduce multidimensional data to a multiple regression model of one-dimensional recognition results so as to reduce the data noise of body temperature data, data noise of blood oxygen saturation data and data noise of heart rate data, and use training samples to linear parameters of multiple regression model to train. Therefore, after the linear parameter training is completed, the multiple regression model based on simple linear calculation can be used to calculate the sign data of the target person, so as to determine whether the target person is sick.
  • the calculation amount is far less than A large-capacity neural network, as a diagnostic model for the initial diagnosis of diseases, can greatly improve the efficiency of diagnosis.
  • the present embodiment also provides a disease detection model training device, the device includes:
  • the data acquisition module 101 is configured to acquire training samples used to characterize the target disease, the training samples include the sign data of multiple persons and the data label of whether each person is sick. For details, refer to the relevant description of step S101 in the above method embodiment, and details are not repeated here.
  • the model training module 102 is used to use the training samples to train the preset multiple regression model, the multiple regression model includes linear parameters, and the linear parameters are used to perform linear dimensionality reduction on the sign data of the person to calculate whether the person is sick result.
  • the multiple regression model includes linear parameters
  • the linear parameters are used to perform linear dimensionality reduction on the sign data of the person to calculate whether the person is sick result.
  • the model output module 103 is configured to output a multiple regression model when the preset condition is satisfied, and construct a disease detection model according to the multiple regression model. For details, refer to the relevant description of step S103 in the above method embodiment, and details are not repeated here.
  • the disease detection model training device provided in the embodiment of the present application is used to implement the disease detection model training method provided in the above embodiment, and its implementation method is the same as the principle. For details, please refer to the relevant description of the above method embodiment, and will not repeat.
  • this embodiment also provides a disease detection device, which includes:
  • the second data acquisition module 201 acquires the physical sign data of the target person. For details, refer to the relevant description of step S201 in the above method embodiment, and details are not repeated here.
  • the calculation module 202 is used to substitute the sign data into the disease detection model generated by the above disease detection model training method for calculation. For details, refer to the relevant description of step S202 in the above method embodiment, and details are not repeated here.
  • the result output module 203 is used for judging whether the target person suffers from the target disease according to the output result of the disease detection model. For details, refer to the relevant description of step S203 in the above method embodiment, and details are not repeated here.
  • the disease detection device provided in the embodiment of the present application is used to implement the disease detection method provided in the above embodiment.
  • the implementation method is the same as the principle. For details, please refer to the relevant description of the above method embodiment, and will not repeat them here.
  • the technical solution provided by this application can obtain a large amount of physical sign data, including but not limited to body temperature data, blood oxygen saturation data, and heart rate data as training samples of physical sign data, and then can measure according to the collected data Data noise, establish a multiple regression model that reduces multidimensional data to one-dimensional recognition results through linear parameters, so as to reduce the data noise of body temperature data, data noise of blood oxygen saturation data and data noise of heart rate data, using training samples Train the linear parameters of a multiple regression model. Therefore, after the linear parameter training is completed, the multiple regression model based on simple linear calculation can be used to calculate the sign data of the target person, so as to determine whether the target person is sick. In the actual use process, the calculation amount is far less than A large-capacity neural network, as a diagnostic model for the initial diagnosis of diseases, can greatly improve the efficiency of diagnosis.
  • FIG. 5 shows an electronic device according to an embodiment of the present application.
  • the device includes a processor 901 and a memory 902, which may be connected through a bus or in other ways.
  • connection through a bus is taken as an example.
  • the processor 901 may be a central processing unit (Central Processing Unit, CPU).
  • the processor 901 can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate array (Field-Programmable Gate Array, FPGA) or Other chips such as programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above-mentioned types of chips.
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • Other chips such as programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above-mentioned types of chips.
  • the memory 902 as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as program instructions/modules corresponding to the methods in the above method embodiments.
  • the processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 902, that is, implements the methods in the above method embodiments.
  • the memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created by the processor 901 and the like.
  • the memory 902 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices.
  • the storage 902 may optionally include storages that are remotely located relative to the processor 901, and these remote storages may be connected to the processor 901 through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • One or more modules are stored in the memory 902, and when executed by the processor 901, the methods in the foregoing method embodiments are executed.
  • the storage medium can be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive) , abbreviation: HDD) or solid-state drive (Solid-State Drive, SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memory.

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Abstract

La présente demande divulgue un procédé et un appareil d'apprentissage de modèle de test d'affection médicale, un procédé et un appareil de test et un dispositif. Le procédé d'apprentissage de modèle de test d'affection médicale consiste : à acquérir des échantillons d'apprentissage pour représenter une maladie cible, les échantillons d'apprentissage comprenant des données de signes physiques d'une pluralité de personnes et des étiquettes de données indiquant si les personnes sont malades ; à former un modèle de régression multivariable prédéfini à l'aide des échantillons d'apprentissage, le modèle de régression multivariable comprenant des paramètres linéaires, et les paramètres linéaires étant utilisés pour effectuer une réduction de dimension linéaire sur des données de signes physiques d'une personne, de façon à calculer un résultat indiquant si la personne est malade ; et lorsqu'une affection prédéfinie est satisfaite, à délivrer un modèle de régression multivariable et à créer un modèle de test d'affection médicale selon le modèle de régression multivariable. La solution technique fournie dans la présente demande permet d'améliorer l'identification d'une affection médicale sur une personne cible sur la base de données de signes physiques.
PCT/CN2022/100159 2021-12-31 2022-06-21 Procédé et appareil d'apprentissage de modèle de test d'affection médicale, procédé et appareil de test et dispositif électronique WO2023123913A1 (fr)

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CN112801224A (zh) * 2021-03-26 2021-05-14 平安科技（深圳）有限公司 糖尿病的分型概率预测方法、装置、设备及存储介质

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