KR-20260066384-A - METHOD FOR PREDICTING RISK OF RETINOPATHY IN DIABETIC PATIENT AND SYSTEM THEREFOR

KR20260066384AKR 20260066384 AKR20260066384 AKR 20260066384AKR-20260066384-A

Abstract

A method and system for predicting retinopathy in diabetic patients are provided. A method for predicting retinopathy according to some embodiments may include the steps of acquiring one or more machine-learning models prepared to predict risk information related to retinopathy in diabetic patients, acquiring status information of a target diabetic patient, and predicting risk information related to retinopathy in the target diabetic patient from the status information acquired through one or more machine-learning models. According to this method, the risk of developing retinopathy among various complications that may occur in diabetic patients can be accurately predicted at an early stage.

Inventors

이상열
황지영
김선영
김재원
우호걸
연동건
임현정
우세린
상현지

Assignees

경희대학교 산학협력단

Dates

Publication Date: 20260512
Application Date: 20241104

Claims (15)

As a method performed by at least one processor, A step of acquiring one or more machine-learning models prepared to predict risk information related to retinopathy in diabetic patients; A step of obtaining condition information of a target diabetic patient; and A method comprising the step of predicting risk information of the subject diabetic patient related to the retinopathy from the state information through the above one or more machine learning models. Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, The above one or more machine learning models include at least one of XGBoost (eXtreme Gradient Boosting), Random Forest, and LightGBM (Light Gradient Boosting Machine). Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, The above status information includes demographic characteristics, disease history, medication history, blood test results, and physical examination results. Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, The above status information includes a history of diseases related to dyslipidemia, cancer, hypertension, chronic kidney disease, neuropathy, and cardiovascular disease, Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, The above status information includes test results regarding glycated hemoglobin (HbA1c), blood glucose, LDL (low-density lipoprotein) cholesterol, HDL (high-density lipoprotein) cholesterol, and triglycerides, as well as drug history regarding cilostazol and statins. Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, The preparation process for the above one or more machine learning models is: A step of constructing a training set based on the records of diabetic patients of the first cohort; Step of constructing a test set based on the records of diabetic patients of a second cohort - the second cohort is a cohort independent of the first cohort, and the test set does not include the records of diabetic patients of the first cohort -; A step of training a specific machine learning model using the above training set; and A method comprising the step of evaluating the performance of the specific machine learning model using the above test set, Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, The preparation process for the above one or more machine learning models is: A step of preparing a training set and a test set based on the records of diabetic patients of the first cohort and the records of diabetic patients of the second cohort - the second cohort is an independent cohort from the first cohort -; A step of training a plurality of candidate machine learning models using the above training set; A step of evaluating the performance of each of the plurality of candidate machine learning models using the above test set; and A method comprising the step of selecting, among the plurality of candidate machine learning models, a model whose evaluated performance is above a threshold as one or more machine learning models. Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, The preparation process for the above one or more machine learning models is: A step of training a first machine learning model using a training set - each sample constituting the training set includes condition information of an individual diabetic patient, and the condition information of the individual diabetic patient relates to multiple variables -; A step of deriving the importance of each of the plurality of variables using the training results of the first machine learning model - the plurality of variables are classified into core variables, intermediate variables, and non-core variables based on the importance -; A step of reinforcing the training set based on the above importance; and The method includes the step of training a second machine learning model using the above-mentioned enhanced training set, and The step of strengthening the above training set is, A step of removing the non-core variables from the training set; and The method includes the step of generating a plurality of synthetic samples by sampling the value of the core variable within a preset range while fixing the value of the intermediate variable, and adding the generated synthetic samples to the training set. The step of predicting risk information for the above-mentioned target diabetes patient is, A step comprising determining risk information for the target diabetes patient by combining the prediction result of the first machine learning model and the prediction result of the second machine learning model. Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, Among the one or more machine learning models mentioned above, a specific machine learning model is: An embedder that generates one or more state embeddings by embedding state information of the above-mentioned diabetic patient; An integrated encoder that generates an integrated embedding by encoding one or more of the above state embeddings; A first predictor that performs a first task of predicting risk information related to the retinal disease based on the above integrated embedding; and It includes a second predictor that performs a second task of predicting risk information of the retinopathy and other diabetes complications based on the integrated embedding above, and The above specific machine learning model is trained by performing the above first task and the above second task, and The step of predicting risk information for the above-mentioned target diabetes patient is, A step comprising predicting risk information of the subject diabetic patient related to the retinopathy through the first predictor, Method for predicting the risk of retinopathy in diabetic patients.
In Paragraph 9, The specific machine learning model mentioned above is: It further includes an image encoder that encodes an image of the diabetic patient associated with the above-mentioned retinopathy to generate an image embedding, and The above-mentioned integrated encoder further encodes the image embedding to generate the above-mentioned integrated embedding, Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, Among the one or more machine learning models mentioned above, a specific machine learning model is: An embedder that generates one or more state embeddings by embedding state information of the above-mentioned diabetic patient; An integrated encoder that generates an integrated embedding by encoding one or more of the above state embeddings; A first predictor that predicts the risk of the retinopathy occurring within a first period based on the above integrated embedding; and It includes a second predictor that predicts the risk of the retinopathy occurring within a second period different from the first period based on the above integrated embedding, and The step of predicting risk information for the above-mentioned target diabetes patient is, A step of predicting a first risk of the retinopathy occurring in the target diabetic patient within the first period through the first predictor; A step of predicting a second risk of the retinopathy occurring in the target diabetic patient within the second period through the second predictor; and A step comprising deriving an increasing risk trend for the retinal degeneration based on the difference between the first risk level and the second risk level, Method for predicting the risk of retinopathy in diabetic patients.
In paragraph 1, The above one or more machine learning models include a first machine learning model that outputs a risk of the retinopathy occurring within a specific period and a second machine learning model that outputs the time of occurrence of the retinopathy. The step of predicting risk information for the above-mentioned target diabetes patient is, A step of predicting the risk of the retinopathy occurring in the target diabetic patient through the first machine learning model; and If the predicted risk level is greater than or equal to a threshold, the method includes the step of predicting the time when the retinopathy occurs in the target diabetic patient through the second machine learning model. Method for predicting the risk of retinopathy in diabetic patients.
One or more processors; and It includes memory for storing computer programs executed by one or more of the above processors, and The above computer program is: The action of acquiring one or more machine-learning models prepared to predict risk information related to retinopathy in diabetic patients; An action of acquiring status information of a target diabetic patient; and Instructions for an operation to predict risk information of the subject diabetic patient associated with the retinopathy from the state information through the above one or more machine learning models, Prediction system for the risk of retinopathy in diabetic patients.
In Paragraph 13, The preparation process for the above one or more machine learning models is: Action to construct a training set based on the records of diabetic patients in the first cohort; Operation of constructing a test set based on the records of diabetic patients of a second cohort - the second cohort is a cohort independent of the first cohort, and the test set does not include the records of diabetic patients of the first cohort -; The operation of training a specific machine learning model using the above training set; and A method including an operation to evaluate the performance of the specific machine learning model using the above test set, Prediction system for the risk of retinopathy in diabetic patients.
Combined with the computer processor, A step of acquiring one or more machine-learning models prepared to predict risk information related to retinopathy in diabetic patients; A step of obtaining condition information of a target diabetic patient; and A computer-readable recording medium stored therein to execute the step of predicting risk information of the subject diabetic patient related to the retinopathy from the state information through the above one or more machine learning models. Computer program.

Description

Method for Predicting Risk of Retinopathy in Diabetic Patients and System Therefor The present disclosure relates to a technology for predicting the risk of developing retinopathy (i.e., diabetic retinopathy) among various complications that may occur in diabetic patients. Diabetic retinopathy is the most common complication in diabetic patients. This disease is accompanied by symptoms such as vision loss, but in many cases, the disease has already progressed significantly by the time patients notice symptoms. Accordingly, the U.S. Centers for Disease Control and Prevention recommends that even asymptomatic diabetic patients undergo regular screenings for retinopathy. Since diabetic retinopathy is one of the preventable causes of blindness, preventive interventions for diabetic patients can significantly lower the incidence of the disease and substantially reduce the social costs associated with it. However, while it is known that the risk of developing the disease increases with longer duration of diabetes or poor blood sugar control, no method has yet been proposed to accurately predict the risk of developing retinopathy in diabetic patients at an early stage. FIG. 1 is an exemplary drawing for explaining the operation of a system for predicting the risk of retinopathy in diabetic patients according to some embodiments of the present disclosure at the system level. FIG. 2 is an exemplary drawing for further explaining the operation of a diabetic patient retinopathy risk prediction system according to some embodiments of the present disclosure. FIG. 3 is an exemplary drawing showing input and output information of a machine-learning model according to some embodiments of the present disclosure. FIG. 4 illustrates a case in which a machine learning model is implemented based on XGBoost (eXtreme Gradient Boosting) according to some embodiments of the present disclosure. FIG. 5 illustrates a case in which a machine learning model is implemented based on a neural network according to some other embodiments of the present disclosure. FIG. 6 illustrates a case in which a machine learning model is implemented based on a neural network according to some other embodiments of the present disclosure. FIG. 7 is an exemplary flowchart schematically illustrating a method for predicting the risk of retinopathy in diabetic patients according to some embodiments of the present disclosure. Figure 8 is an exemplary flowchart illustrating an example of the detailed process of the machine learning model preparation step shown in Figure 7. FIGS. 9 and FIGS. 10 are exemplary drawings for explaining detailed embodiments related to the training set and test set preparation steps illustrated in FIG. 8. FIG. 11 is an exemplary diagram to further explain the candidate machine learning model training and performance evaluation steps illustrated in FIG. 8. FIG. 12 is an exemplary drawing for illustrating a method of building an additional machine learning model using variable importance according to some embodiments of the present disclosure. FIGS. 13 to 15 are exemplary drawings for explaining performance tests conducted by the inventors of the present disclosure. FIG. 16 illustrates an exemplary computing device capable of implementing a system for predicting the risk of retinopathy in diabetic patients according to some embodiments of the present disclosure. Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the attached drawings. The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the attached drawings. However, the technical concept of the present disclosure is not limited to the following embodiments but can be implemented in various different forms. The following embodiments are provided merely to complete the technical concept of the present disclosure and to fully inform those skilled in the art of the scope of the present disclosure, and the technical concept of the present disclosure is defined only by the scope of the claims. In describing the various embodiments of the present disclosure, if it is determined that a detailed description of related known configurations or functions could obscure the essence of the present disclosure, such detailed description is omitted. Unless otherwise defined, terms used in the following embodiments (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which this disclosure pertains, but this may vary depending on the intent of those skilled in the art, case law, the emergence of new technology, etc. The terms used in this disclosure are for describing the embodiments and are not intended to limit the scope of this disclosure. In the following embodiments, singular expressions include plural concepts unless the context clearly specifies them as si