WO2021120936A1 - 一种基于多任务学习模型的慢病预测*** - Google Patents
一种基于多任务学习模型的慢病预测*** Download PDFInfo
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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
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Definitions
- the invention belongs to the field of medical artificial intelligence, and particularly relates to a chronic disease prediction system based on a multi-task learning model.
- Chronic disease also called chronic disease
- chronic disease is a common latent disease with long duration, including diabetes, cardiovascular disease, cancer and respiratory diseases.
- the number of patients with chronic diseases has been increasing rapidly.
- the causes of chronic diseases are complex and require continuous treatment. Therefore, chronic diseases bring harm to people's health and life, and their mortality and treatment burden continue to increase. If early detection and intervention of chronic diseases can be carried out, these problems can be effectively alleviated.
- the Chinese patent document with the publication number CN107153774A discloses the construction of a hyperbolic model of chronic disease risk assessment and a disease prediction system using this model. It relies on the vertical health management data of more than 20 health management centers in Shandong province to build a Shandong multi-center health management vertical Observe the cohort, explore the role of genetics, environment, personal lifestyle, health intervention factors, etc. in the occurrence, development and outcome of major chronic diseases, and establish a hyperbolic model and disease risk assessment for various chronic diseases applicable to the health checkup population in Shandong province Predictive system and provide scientific basis for health intervention of chronic diseases.
- the other is to use some methods to analyze the data of electronic health records and other data collected through inspections, including anthropometric characteristics (age, gender, body mass index, etc.) and physiological records (including blood routine, blood sugar, urine routine, etc.).
- anthropometric characteristics age, gender, body mass index, etc.
- physiological records including blood routine, blood sugar, urine routine, etc.
- the Chinese patent document with the publication number CN107007284A discloses a multi-disease chronic disease information management system, including a database, an application server, several hospital clients and patient clients; the database stores the patient’s various medical examination data, doctor’s recommendations, and various The health data reference range of the examination items and the health status evaluation indicators of various chronic diseases; the application server obtains various physical examination data and corresponding health data reference of the designated patient in the database according to the first query instruction sent by the hospital/patient client Scope, health evaluation indicators of various chronic diseases, and doctor’s recommendations, obtain chronic disease evaluation results, and return the chronic disease evaluation results of the current designated patient and the above-mentioned various data to the hospital/patient client.
- the present invention provides a chronic disease prediction system based on a multi-task learning model, which can predict multiple chronic diseases at the same time by using the potential connections that may exist between multiple chronic diseases.
- a chronic disease prediction system based on a multi-task learning model comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, and the computer memory stores training
- a good chronic disease prediction model which is composed of a shared layer convolutional neural network and multiple chronic disease branch networks;
- the physical examination records to be predicted are input into the shared layer convolutional neural network of the chronic disease prediction model for feature extraction, and feature maps are obtained;
- the obtained feature maps are input into each chronic disease branch network respectively, and feature extraction and prediction are performed respectively, and the chronic disease prediction results are obtained.
- the structure of the shared layer convolutional neural network is: firstly, the convolutional layer is shared by multi-layer tasks, and then 3 and 6 convolution kernels with a size of 3 ⁇ 3 are used for feature extraction, and the step size of the convolution kernel Set to 1;
- Each chronic disease branch network is equipped with 2 convolutional layers.
- Each convolutional layer is feature extraction by 9 and 12 convolution kernels.
- the step length of the convolution kernel is designed to be 2 and 1 respectively; finally, each convolutional layer has 9 and 12 convolution kernels for feature extraction.
- Each branch passes through two fully connected layers with 32 nodes and a softmax layer to get the final output.
- the training process of the chronic disease prediction model is as follows:
- the data encoding method includes a content encoding strategy and a spatial encoding strategy, where the content encoding strategy is used to realize the unification of the numerical types of the data, The spatial coding strategy is used to realize the unification of the data format of the input model;
- the physical examination data used in the present invention is csv format data, and can also be structured data in other formats for the patient's physical examination record.
- Each csv data corresponds to a patient's physical examination record, and each csv record includes multiple physical examination index items.
- In the process of model training there may be some patients whose multiple physical examination index items are missing, which will result in larger errors and poor results in model training. Therefore, in this step, we have eliminated these data records.
- some physical examination index items are missing in multiple patients, which will also lead to poor performance in the model training process. Therefore, these index items are eliminated.
- the preprocessing includes: correlation analysis of various indicators in the physical examination data, missing value statistics, elimination of data with a single record missing value exceeding a certain percentage from the perspective of physical examination records, and elimination of all records from the perspective of data indicators For data indicators with missing values exceeding a certain percentage, the missing data in the physical examination records are grouped according to age and the missing values are filled.
- a 5-fold cross-validation method is used to divide the data set into groups, thereby averaging the results of 5 different groupings to reduce the variance and reduce the sensitivity of the model's performance to the data division.
- the specific process of the 5-fold cross-validation method is as follows:
- Non-repetitive sampling The sample data is randomly divided into 5 parts, each of which has the same or similar number of samples; one of them is selected as the test set each time, and the remaining 4 parts are used as the training set for model training. Repeat 5 times to make 5 groups Different training set and validation set group. In this way, each subset has a chance to serve as the validation set, and the rest as the training set.
- the described content coding strategy takes the following two specific operations:
- One-hot encoding to encode continuous variables in physical examination records into categorical variables as input.
- the physical examination record after the content encoding is a one-dimensional vector, and all variables in the one-dimensional vector are analyzed for the correlation between each other; according to the sum of the correlations between a certain variable and all other variables, it is sorted in descending order; after descending sorting All the variables of are arranged in sequence to form a two-dimensional vector, which is used as the input data of the network.
- the specific process of using the training set to train the chronic disease prediction model is as follows:
- Input a set of training sets, and respectively go through the feature extraction of the potential correlation shared layer, feature extraction for a single chronic disease, and output the prediction results;
- the model stops updating and outputs the result
- the above training process also includes: after the training set of each group is trained, input the validation set of the group into the model to obtain the corresponding classification result; average the loss values obtained from all the validation sets as the performance evaluation of the model. To find the optimal parameters.
- Model performance evaluation includes the prediction accuracy of multiple single disease types.
- the present invention has the following beneficial effects:
- the present invention builds a chronic disease prediction system based on a multi-task learning model. First, it preprocesses the data of physical examination records, and encodes the content and structure of the data. Then, a multi-task learning model is designed, and the multi-task sharing layer is used to realize multiple diseases. Perform feature extraction for possible potential connections, and then perform separate feature extraction and final prediction through a single-task branch designed for a single chronic disease. This can realize simultaneous prediction of multiple chronic diseases and make full use of multiple chronic diseases. Potential associations that may exist. In the training process, the 5-fold cross-validation method is used to train the model. After multiple iterations, the model can achieve a relatively stable effect and high accuracy.
- FIG. 1 is a schematic diagram of a preprocessing flow of a physical examination record used in an embodiment of the present invention
- Figure 2 is a schematic diagram of a 5-fold cross-validation method used in an embodiment of the present invention.
- Figure 3 is a flow chart of the overall framework of the network model proposed by the present invention.
- Figure 4 is an implementation method of a content encoding strategy used in an embodiment of the present invention.
- FIG. 5 is a schematic diagram of the network structure of a chronic disease prediction model used in an embodiment of the present invention.
- Fig. 6 is the result of model prediction in the embodiment of the present invention.
- a chronic disease prediction system based on a multi-task learning model including a computer memory, a computer processor, and a computer program that is stored in the computer memory and can be executed on the computer processor, and a trained chronic disease prediction model is stored in the computer memory ,
- the chronic disease prediction model is composed of a shared layer convolutional neural network and multiple chronic disease branch networks; the following steps are implemented when the computer processor executes the computer program:
- the physical examination records to be predicted are first input into the shared layer convolutional neural network of the chronic disease prediction model for feature extraction, and feature maps are obtained; then the obtained feature maps are input into each chronic disease branch network separately, Feature extraction and prediction to get the results of chronic disease prediction.
- sample data records and preprocess them. Obtain sample data sets from 5 partner hospitals. The sample data sets contain 48,953 physical examination records. A single physical examination record includes up to 55 physical examination data. Each physical examination item has a different range of reference values. There are also some outliers, and each record is carefully marked by more than 3 professional doctors to distinguish patients with hypertension, diabetes, both or normal.
- the acquired sample data set is preprocessed, and the data is eliminated based on feature correlation and feature lack.
- the Pearson correlation coefficient is mainly used to calculate the correlation between features. For paired variables with a Pearson coefficient greater than 0.8, the feature that has a large amount of missing data among the variable pairs is eliminated. In addition, for all patients, if the amount of missing features is greater than 0.2, the patient's data is also discarded. After the elimination of the data, a total of 13,358 physical examination records, 49 physical examination indicators, the missing value of the data variable in each of the data are less than 0.2.
- age is one of the risk factors for hypertension and diabetes. Therefore, age is an important grouping basis for filling in missing values.
- the patients are first grouped according to age, and divided into 7 groups in total. Then, for a certain feature to be filled, the mode of the feature value in the group is selected for filling.
- the specific steps of data set preprocessing are shown in Figure 1.
- the spatial mapping method For the 49 index items in each record, first use the one-hot encoding method in the content encoding strategy to encode the data of the value bit text corresponding to the index item.
- the encoding method is shown in Figure 4.
- use the spatial coding strategy to map 49 index items to a 7 ⁇ 7 matrix as the input of the network model, as shown in the left part of Figure 3.
- the spatial mapping method here complies with the method described in the present invention.
- the correlation between each of the 49 index items is calculated, and the correlations between a certain index and all other indexes are arranged in descending order, and Furthermore, the one-dimensional index sequence is mapped to the two-dimensional space, and the h-th value of the 49 indexes is mapped to the i-th, j-th position m ij of the matrix M.
- the same mapping method is maintained, that is, an index in a set of experiments is mapped to a fixed position in all samples to ensure subsequent correlation analysis).
- the chronic disease prediction model of the present invention takes a two-dimensional vector as input.
- a shared layer convolutional neural network shared by multiple diseases is first designed to extract features of potential correlations that may exist in multiple diseases;
- the feature maps after common feature extraction are used for feature extraction and prediction respectively for each branch of different kinds of chronic diseases.
- This embodiment constructs a network model for two specific diseases of diabetes and hypertension, and performs feature extraction and disease prediction for the two diseases.
- the training data set in the first group of data encoded in the above step S03 is input into the model on an individual basis, that is, each input data is data of a two-dimensional matrix containing a physical examination record.
- the data is input into the model for feature extraction and prediction.
- the detailed structure of the model is shown in Figure 5.
- 3 and 6 convolution kernels with a size of 3 ⁇ 3 are used for feature extraction, and the step size of the convolution kernel is set to 1.
- the feature extraction of diabetes physical examination data and the feature extraction of hypertension physical examination data are carried out respectively.
- Each branch is designed with two convolutional layers, and each convolutional layer consists of 9 and Twelve convolution kernels perform feature extraction, and the step lengths of the convolution kernels are designed to be 2 and 1, respectively.
- the two branches of predicting diabetes and hypertension respectively pass through two fully connected layers with 32 nodes and a softmax layer to obtain the final output.
- Each branch is based on the features extracted by the model to determine whether the patient has diabetes or hypertension, branch 1 is relative to hypertension, and branch 2 is relative to diabetes.
- the discriminant result output by the model and the annotation corresponding to the physical examination record marked by the expert in step 1 are used to calculate the loss through the cross-entropy loss function.
- the sum of the loss values of the two branches is used as the loss function of the entire model to optimize the model.
- the prediction accuracy rate for hypertension can reach 73%, and the prediction accuracy rate for diabetes can reach 82%.
- the AUC index can reach 79% and more than 85%, which has great advantages and better results compared with the single-task model.
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Abstract
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Claims (9)
- 一种基于多任务学习模型的慢病预测***,包括计算机存储器、计算机处理器以及存储在所述计算机存储器中并可在所述计算机处理器上执行的计算机程序,其特征在于,所述计算机存储器中存有训练好的慢病预测模型,所述的慢病预测模型由共享层卷积神经网络和多个慢病分支网络组成;所述计算机处理器执行所述计算机程序时实现以下步骤:将待预测的体检记录经过预处理后先输入慢病预测模型的共享层卷积神经网络中进行特征提取,特得到特征图;然后将得到的特征图分别输入每个慢病分支网络,分别进行特征提取和预测,得到慢病预测结果。
- 根据权利要求1所述的基于多任务学习模型的慢病预测***,其特征在于,所述共享层卷积神经网络的结构为:首先通过多层任务共享的卷积层,然后分别使用3个和6个尺寸为3×3的卷积核进行特征提取,卷积核的步长设置为1;每个慢病分支网络分别设有2个卷积层,每个卷积层分别由9个和12个卷积核进行特征提取,卷积核的步长分别设计为2和1;最后,每个分支分别依次通过两个节点数为32的全连接层和一个softmax层得到最后的输出。
- 根据权利要求1所述的基于多任务学习模型的慢病预测***,其特征在于,所述慢性病预测模型的训练过程如下:获取慢性病检查相关的体检数据作为样本数据,经过预处理之后,标注标签,标注后的样本数据通过5折交叉验证方法分成训练集和验证集;设计针对体检数据中结构化数据的数据编码方法,得到慢病预测模型 的输入数据;所述数据编码方法包括内容编码策略和空间编码策略,其中,内容编码策略用于实现数据的数值类型统一,空间编码策略用于实现输入模型的数据格式统一;搭建基于多任务学习的慢病预测模型,使用深度学习方法对编码的结构化数据进行特征提取和分类,同时输出多种慢病的预测结果;使用训练集对所述慢病预测模型进行训练,根据模型的预测结果与标签的重合度对模型的参数进行调整,直至模型收敛。
- 根据权利要求3所述的基于多任务学习模型的慢病预测***,其特征在于,所述的预处理包括:对于体检数据中的各项指标进行相关性分析、缺失值统计,从体检记录角度剔除单条记录缺失值超过一定比例的数据,从数据指标角度剔除所有记录中缺失值超过一定比例的数据指标,对体检记录中的缺失数据,根据年龄分组并进行缺失值填充。
- 根据权利要求3所述的基于多任务学习模型的慢病预测***,其特征在于,所述的5折交叉验证方法具体过程如下:不重复抽样将样本数据随机分为5份,每份数据样本数量相等或相接近;每一次挑选其中1份作为测试集,剩余4份作为训练集用于模型训练,重复5次,制作5组不同的训练集和验证集组。
- 根据权利要求3所述的基于多任务学习模型的慢病预测***,其特征在于,所述的内容编码策略采取如下两种具体操作:使用标签编码方式将体检记录中的文本信息编码成数值信息;使用One-hot编码方式将体检记录中的文本信息编码成数值信息,作为输入。
- 根据权利要求3所述的基于多任务学习模型的慢病预测***,其特征在于,所述的空间编码策略具体操作过程如下:内容编码之后的体检记录是一维向量,对一维向量中的所有变量进行 两两之间相关性分析;根据某个变量和其他所有变量之间的相关性之和进行降序排序;降序排列之后的所有变量依次排列,形成二维向量,作为网络的输入数据。
- 根据权利要求3所述的基于多任务学习模型的慢病预测***,其特征在于,使用训练集对所述慢病预测模型进行训练的具体过程如下:输入一组训练集,分别经过潜在相关性的共享层特征提取、针对单种慢性病的特征提取,输出预测结果;将输出的预测结果与数据所对应的标签进行对比,运用ACC函数作为当前模型的损失,并回传到模型中,对所述模型中的参数进行更新;当达到设定的ACC阈值或者达到指定迭代次数时,所述模型停止更新,输出结果;使用上述方法依次输入剩余的训练集进行训练,直至模型收敛。
- 根据权利要求8所述的基于多任务学***均值,作为模型的性能评估,用于寻找最优参数。
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