KR-20260067421-A - METHOD FOR PREDICTING RISK OF NEURODEGENERATIVE DISEASE IN DIABETIC PATIENT AND SYSTEM THEREFOR

KR20260067421AKR 20260067421 AKR20260067421 AKR 20260067421AKR-20260067421-A

Abstract

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

Inventors

이상열
김선영
우호걸
임현정
연동건
우세린
황지영
김재원
상현지

Assignees

경희대학교 산학협력단

Dates

Publication Date: 20260513
Application Date: 20241104

Claims (14)

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 neurodegenerative diseases 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 target diabetic patient associated with the neurodegenerative disease from the state information through the above one or more machine learning models. Method for predicting the risk of neurodegenerative disease in diabetic patients.
In paragraph 1, The above one or more machine learning models include at least one of AdaBoost (Adaptive Boost), LightGBM (Light Gradient Boosting Machine), and Random Forest. Method for predicting the risk of neurodegenerative disease 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 neurodegenerative disease in diabetic patients.
In paragraph 1, The above status information includes age and gender and a history of diseases regarding cardiovascular disease, cancer, neuropathy, dyslipidemia, hypertension, and chronic kidney disease, Method for predicting the risk of neurodegenerative disease in diabetic patients.
In paragraph 1, The above status information includes test results regarding ALP (alanine aminotransferase), LDL (low-density lipoprotein) cholesterol, body mass index (BMI), and glucose, as well as drug history regarding calcium channel blockers, metformin, and meglitinide. Method for predicting the risk of neurodegenerative disease 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 neurodegenerative disease 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 neurodegenerative disease 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 enhancing 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 neurodegenerative disease 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 risk information related to the neurodegenerative disease based on the above integrated embedding; and It includes a second predictor that predicts risk information of the neurodegenerative disease and other diabetes complications based on the above integrated embedding, and The step of predicting risk information for the above-mentioned target diabetes patient is, A method comprising the step of predicting risk information of the target diabetic patient associated with the neurodegenerative disease through the first predictor. Method for predicting the risk of neurodegenerative disease in diabetic patients.
In Paragraph 9, The specific machine learning model mentioned above is: An image encoder that generates image embeddings by encoding an image of the diabetic patient associated with the above-mentioned neurodegenerative disease; and It further includes a voice encoder that generates voice embeddings by encoding the voice features or voice data of the above-mentioned diabetic patient, and The above integrated encoder further encodes the image embedding and the voice embedding to generate the integrated embedding. Method for predicting the risk of neurodegenerative disease 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 neurodegenerative disease occurring within a first period based on the above integrated embedding; and It includes a second predictor that predicts the risk of the neurodegenerative disease 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 neurodegenerative disease occurring in the target diabetic patient within the first period through the first predictor; A step of predicting a second risk of the neurodegenerative disease occurring in the target diabetic patient within the second period through the second predictor; and A method comprising the step of deriving an increasing risk trend for the neurodegenerative disease based on the difference between the first risk level and the second risk level. Method for predicting the risk of neurodegenerative disease 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 operation of acquiring one or more machine-learning models prepared to predict risk information related to neurodegenerative diseases 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 target diabetic patient associated with the neurodegenerative disease from the state information through the above one or more machine learning models, Prediction system for the risk of neurodegenerative disease in diabetic patients.
In Paragraph 12, 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 -; 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 neurodegenerative disease 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 neurodegenerative diseases 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 associated with the neurodegenerative disease from the state information through the above one or more machine learning models. Computer program.

Description

Method for Predicting Risk of Neurodegenerative Disease in Diabetic Patients and System Therefor The present disclosure relates to a technology for predicting the risk of developing neurodegenerative diseases among various complications that may occur in diabetic patients. Major complications of diabetes include retinopathy, neuropathy, chronic kidney disease, and cardiovascular disease. Recently, there has been increasing interest in the association between diabetes (e.g., type 2 diabetes) and neurodegenerative disease, and neurodegenerative disease is receiving attention as another complication of diabetes. Neurodegenerative diseases are characterized by the progressive dysfunction of synapses, neurons, glial cells, and their networks. Neurodegenerative diseases include dementia, Parkinson's disease, multiple sclerosis, and Huntington's disease. However, while it is known that aging, genetic factors (e.g., family history of dementia), cardiovascular and metabolic risk factors such as hypertension, obesity, and diabetes, smoking, and traumatic brain injury influence the development of neurodegenerative diseases, no method has yet been proposed to accurately predict the risk of developing neurodegenerative diseases 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 neurodegenerative disease 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 system for predicting the risk of neurodegenerative disease in diabetic patients 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 where a machine learning model is implemented based on AdaBoost (Adaptive Boost) 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 neurodegenerative disease 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 neurodegenerative disease 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