Patent Application Titled “Chronic Disease Prediction System Based On Multi-Task Learning Model” Published Online (USPTO 20220254493): Patent Application

2022 AUG 31 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Cao, Yan (Hangzhou, Zhejiang Province, Cn); Feng, Ruiwei (Hangzhou, Zhejiang Province, Cn); Jiang, Xiaohong (Hangzhou, Zhejiang Province, Cn); Liu, Xuechen (Hangzhou, Zhejiang Province, Cn); Wu, Jian (Hangzhou, Zhejiang Province, Cn); Ying, Haochao (Hangzhou, Zhejiang Province, Cn), filed on November 12, 2020, was made available online on August 11, 2022.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “Chronic diseases are a type of latent and long-term common diseases, including diabetes, cardiovascular diseases, cancers and respiratory diseases. In recent years, the number of patients with chronic diseases is increasing rapidly. Generally speaking, the causes of chronic diseases are complex, so continuous treatment is required. Therefore, chronic diseases bring harm to people’s health and life, and the death rate and treatment burden are continuously increasing. If the chronic diseases can be discovered and intervened early, these problems can be effectively alleviated.

“At present, there have been some methods which try to discover and treat chronic diseases as early as possible. These methods may be generally divided into two categories: one category is to focus on researching data containing people’s living habit and demographic variable so as to find out body conditions or living habits which may cause a certain chronic disease, thereby preventing the chronic disease.

“For example, Chinese patent document with the publication number CN107153774A discloses construction of a chronic disease risk assessment hyperbolic model and a disease prediction system applying the model. It relies on the longitudinal health management data of more than 20 health management centers in Shandong Province to build a Shandong multi-center health management longitudinal observation queue, discuss the effect of heredity, environment, personal lifestyle and health intervention factor in the occurrence, development and prognosis processes of major chronic diseases, establish a risk assessment hyperbolic model and disease prediction system suitable for various chronic diseases of healthy physical examination people in Shandong Province, and provide scientific basis for health intervention of the chronic diseases.

“The other one is to analyze data of electronic health record and other data collected through examination through some methods, including human body measurement features (age, gender, body mass index and the like) and physiological record (including blood routine examination, blood glucose, routine urine examination and the like), and the dangerous factor of a certain disease is discovered by looking for the relation between the medical index and the chronic disease, so that the chronic disease is predicted. At the same time, some studies have explored the potential relation between the common dangerous factors and some common chronic diseases.

“For example, 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, wherein the database stores various physical examination data, doctor suggestion, health data reference range of various examination items and health state assessment index of patients; and the application server acquires various physical examination data and corresponding health data reference range, the health state assessment index of various chronic diseases and doctor suggestion of the specified patient in the database according to a first query instruction sent by the hospital/patient client to obtain the chronic disease assessment result, and returns the chronic disease assessment result of the current specified patient and the above various data to the hospital/patient client.

“However, there is still no method to predict various chronic diseases at the same time by applying potential relations possibly existing among the various chronic diseases.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “The prevent invention provides a chronic disease prediction system based on a multi-task learning model, which is capable of predicting various chronic diseases at the same time by applying potential relations possibly existing among the various chronic diseases.

“A chronic disease prediction system based on a multi-task learning model comprises a computer memory, a computer processor and a computer program which is stored in the computer memory and executable on the computer processor, wherein a trained chronic disease prediction model is stored in the computer memory, and the chronic disease prediction model is composed of a shared layer convolutional neural network and a plurality of chronic disease branch networks.

“When executing the computer program, the computer processor implements the following steps:

“preprocessing a to-be-predicted physical examination record and then inputting the record into the shared layer convolutional neural network of the chronic disease prediction model for feature extraction to obtain a feature map; and

“inputting the obtained feature map into each chronic disease branch network and performing feature extraction and prediction respectively to obtain a chronic disease prediction result.

“A structure of the shared layer convolutional neural network is as follows: firstly, through a multi-layer task shared convolutional layer, feature extraction is performed by using 3 and 6 convolutional cores with a size of 3*3, and a step length of the convolutional core is set as 1;

“each chronic disease branch network is provided with 2 convolutional layers respectively, feature extraction is performed on each convolutional layer by 9 and 12 convolutional layers respectively, and step lengths of the convolutional layers are designed as 2 and 1 respectively; and finally, each branch sequentially passes through two full-connection layers with a node number of 32 and one softmax layer to obtain a final output.

“The training process of the chronic disease prediction model is as follows:

“acquiring chronic disease examination related physical examination data as sample data, labeling the sample data after preprocessing, and dividing the labeled sample data into a training set and a validation set by a five-fold cross validation method;

“designing a data coding method for structured data in physical examination data to acquire input data of the chronic disease prediction data, wherein the data coding method comprises a content coding strategy and a spatial coding strategy, the content coding strategy being used to unify value types of data, and the spatial coding strategy being used to unify data formats the input model/data;

“establishing a multi-task learning-based chronic disease prediction model, performing feature extraction and classification on the coded structured data by a deep learning method, and outputting prediction results of various chronic diseases at the same time; and

“training the chronic prediction model by the training set, and adjusting parameters of the model according to the prediction result of the model and the coincidence degree of the label until the model converges.

“Physical examination data used in the present invention is data in a csv format, and may also be structured data in other formats for a physical record of a patient. Each piece of csv data corresponding to a physical examination record of one patient, and each csv record comprises a plurality of physical examination index items. In the model training process, there may be some patients whose physical examination index items are missing, which will lead to large error and poor effect in model training. Therefore, in this step, these data records are eliminated. Meanwhile, some physical examination index items are missing in many patients, which will also lead to poor performance in the model training process. Therefore, these index items are eliminated.

“Specifically, the preprocessing comprises: performing correlation analysis and missing value counting on various indexes in the physical examination data, eliminating data with missing values in a single record exceeding a certain ratio from the perspective of physical examination records, eliminating data indexes with missing values in all the records exceeding a certain ratio from the perspective of data indexes, grouping according to ages, and performing missing value filling on missing data in the physical examination records.

“Specifically, patients are grouped according to their ages, and the missing item of data in each group is filled according to the average value or mode of the item in the group.

“In order to improve the stability of the model performance, a five-fold cross validation method is selected and the data set is grouped, so that the training results of five different groups are averaged to reduce a variance, thereby reducing the sensitivity of the model performance on data division. The specific process of the five-fold cross validation method is as follows:

“randomly dividing the sample data into five parts without repeated sampling, the number of each part of data samples being equal or close; and selecting one part as a test set at each time and the remaining four parts as the training set for model training, and repeating five times to make five different training set and validation set groups. Hence, each sub-set has a chance to serve as a validation set, and the rest of sets as training sets.

“The content coding strategy adopts the following two specific operations:

“coding text information in the physical examination record into numerical information by a label coding mode; and

“coding a continuous variable in the physical examination record into a category variable by a one-hot coding mode to serve as input.

“The specific operation process of the spatial coding strategy is as follows:

“analyzing a correlation between any two of all variables in a one-dimensional vector, wherein the physical examination record after content coding is the one-dimensional vector; sorting in a descending order according to the sum of correlations between a certain variable and all other variables; and sequentially sorting all the variables after the descending sort to form a two-dimensional vector to serve as input data of a network.

“The specific process of training the chronic disease prediction model by the training set is as follows:

“inputting one group of training sets, and outputting a prediction result respectively through feature extraction of a shared layer with a potential correlation and feature extraction for a single chronic disease;

“comparing the output prediction result with a label corresponding to data, applying an ACC (prediction accurate rate) function as loss of a current model and returning to the model, and updating parameters in the model;

“when reaching a set ACC (prediction accurate rate) threshold or a specified number of iterations, stopping updating the model and outputting a result; and

“sequentially inputting the remaining training sets by the above method for training until the model converges.

“The training process further comprises: after each group of training sets are trained, inputting validation sets in the group into the model to obtain a corresponding classification result; and averaging loss values obtained by all the validation sets to serve as performance assessment of the model for finding an optimal parameter. Model performance assessment includes prediction accuracy on various single diseases.

“Compared with the prior art, the present disclosure has the following beneficial effects:

“the present invention builds the chronic disease prediction system based on the multi-task learning model. Firstly, data recorded by physical examination is preprocessed, and the data content and structure are coded, then a multi-task learning model is designed, feature extraction is performed on the potential relations possibly existing among various diseases by a multi-task shared layer, and feature extraction and final prediction are performed respectively through a single-task branch designed for single chronic disease, so that various chronic diseases can be predicted at the same time, and the potential relations possibly existing among various chronic diseases can be completely applied. In the training process, the model is trained by the five-fold cross validation method, and a stable effect and high accuracy rate can be achieved after many iterations.”

The claims supplied by the inventors are:

“1. A chronic disease prediction system based on a multi-task learning model, comprising a computer memory, a computer processor and a computer program which is stored in the computer memory and executable on the computer processor, wherein a trained chronic disease prediction model is stored in the computer memory, and the chronic disease prediction model is composed of a shared layer convolutional neural network and a plurality of chronic disease branch networks; and when executing the computer program, the computer processor implements the following steps: preprocessing a to-be-predicted physical examination record and then inputting the record into the shared layer convolutional neural network of the chronic disease prediction model for feature extraction to obtain a feature map, and inputting the obtained feature map into each chronic disease branch network and performing feature extraction and prediction respectively to obtain a chronic disease prediction result.

“2. The chronic disease prediction system based on the multi-task learning model according to claim 1, wherein a structure of the shared layer convolutional neural network is as follows: firstly, through a multi-layer task shared convolutional layer, feature extraction is performed by using 3 and 6 convolutional cores with a size of 3*3, and a step length of the convolutional core is set as 1; each chronic disease branch network is provided with 2 convolutional layers respectively, feature extraction is performed on each convolutional layer by 9 and 12 convolutional layers respectively, and step lengths of the convolutional layers are designed as 2 and 1 respectively; and finally, each branch sequentially passes through two full-connection layers with a node number of 32 and one softmax layer to obtain a final output.

“3. The chronic disease prediction system based on the multi-task learning model according to claim 1, wherein the training process of the chronic disease prediction model is as follows: acquiring chronic disease examination related physical examination data as sample data, labeling the sample data after preprocessing, and dividing the labeled sample data into a training set and a validation set by a five-fold cross validation method; designing a data coding method for structured data in physical examination data to acquire input data of the chronic disease prediction data, the data coding method comprising a content coding strategy and a spatial coding strategy, the content coding strategy being used to unify value types of data, and the spatial coding strategy being used to unify data formats the input type; establishing a multi-task learning-based chronic disease prediction model, performing feature extraction and classification on the coded structured data by a deep learning method, and outputting prediction results of various chronic diseases at the same time; and training the chronic prediction model by the training set, and adjusting parameters of the model according to the prediction result of the model and the coincidence degree of the label until the model converges.

“4. The chronic disease prediction system based on the multi-task learning model according to claim 3, wherein the preprocessing comprises: performing correlation analysis and missing value counting on various indexes in the physical examination data, eliminating data with missing values in a single record exceeding a certain ratio from the perspective of physical examination records, eliminating data indexes with missing values in all the records exceeding a certain ratio from the perspective of data indexes, grouping according to ages, and performing missing value filling on missing data in the physical examination records.

“5. The chronic disease prediction system based on the multi-task learning model according to claim 3, wherein the specific process of the five-fold cross validation method is as follows: randomly dividing the sample data into five parts without repeated sampling, the number of each part of data samples being equal or close; and selecting one part as a test set at each time and the remaining four parts as the training set for model training, and repeating five times to make five different training set and validation set groups.

“6. The chronic disease prediction system based on the multi-task learning model according to claim 3, wherein the content coding strategy adopts the following two specific operations: coding text information in the physical examination record into numerical information by a label coding mode; and coding text information in the physical examination record into numerical information by a one-hot coding mode to serve as input.

“7. The chronic disease prediction system based on the multi-task learning model according to claim 3, wherein the specific process of the spatial coding strategy is as follows: analyzing a correlation between any two of all variables in a one-dimensional vector, wherein the physical examination record after content coding is the one-dimensional vector; sorting in a descending order according to the sum of correlations between a certain variable and all other variables; and sequentially sorting all the variables after the descending sort to form a two-dimensional vector to serve as input data of a network.

“8. The chronic disease prediction system based on the multi-task learning model according to claim 3, wherein the specific process of training the chronic disease prediction model by the training set is as follows: inputting one group of training sets, and outputting a prediction result respectively through feature extraction of a shared layer with a potential correlation and feature extraction for a single chronic disease; comparing the output prediction result with a label corresponding to data, applying an ACC function as loss of a current model and returning to the model, and updating parameters in the model; when reaching a set ACC threshold or a specified number of iterations, stopping updating the model and outputting a result; and sequentially inputting the remaining training sets by the above method for training until the model converges.

“9. The chronic disease prediction system based on the multi-task learning model according to claim 8, wherein the training process further comprises: after each group of training sets are trained, inputting validation sets in the group into the model to obtain a corresponding classification result; and averaging loss values obtained by all the validation sets to serve as performance assessment of the model for finding an optimal parameter.”

For more information, see this patent application: Cao, Yan; Feng, Ruiwei; Jiang, Xiaohong; Liu, Xuechen; Wu, Jian; Ying, Haochao. Chronic Disease Prediction System Based On Multi-Task Learning Model. Filed November 12, 2020 and posted August 11, 2022. Patent URL: https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220220254493%22.PGNR.&OS=DN/20220254493&RS=DN/20220254493

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