Abstract: A method of correcting an error in an electrocardiogram signal, which is performed by a computing device, according to an embodiment of the present disclosure includes: acquiring electrocardiogram signals; estimating an error in the acquired electrocardiogram signals based on the correlations between leads for the measurement of the acquired electrocardiogram signals; and correcting an electrocardiogram signal, estimated to have the error, based on correction information for the correction of an error in an electrocardiogram signal.

Abstract: According to an embodiment of the present disclosure, there are disclosed a method, program, and device for the training and inference of a deep learning model based on medical data, which are performed by a computing device. The training method includes: training a first neural network model based on medical data; and training a second neural network model based on the trained first neural network model by matching a first operation function representative of a neural network block included in the trained first neural network model and a second operation function representative of a neural network block included in the second neural network model.

Abstract: According to an embodiment of the present disclosure, there is provided a method of diagnosing left ventricular systolic dysfunction based on an electrocardiogram, the method being performed by a computing device including at least one processor, the method including: acquiring electrocardiogram data; and estimating the probability of occurrence of left ventricular systolic dysfunction for the subject of measurement of the electrocardiogram data based on the electrocardiogram data by using a pre-trained neural network model; wherein the neural network model has been trained based on the correlations between left ventricular systolic dysfunction and changes in electrocardiogram characteristics.

Abstract: According to an embodiment of the present disclosure, there are disclosed a method, computer program and device for measuring a continuous body condition based on deep learning, which are performed by a computing device. The method includes: acquiring electrocardiogram data; and inferring a body condition corresponding to the occurrence or progress of a disease in a subject, whose electrocardiogram data was measured, based on the electrocardiogram data by using a pre-trained neural network model. The neural network model has been trained based on at least one of a first feature related to biological information representing a body characteristic having a correlation with the disease and a second feature related to pathological information reflecting the degree of progress of the disease therein.

Abstract: According to an embodiment of the present disclosure, there is provided a method of diagnosing thyroid dysfunction based on an electrocardiogram, the method being performed by a computing device including at least one processor, the method including: acquiring electrocardiogram data; and estimating the probability of occurrence of thyroid dysfunction for the subject of measurement of the electrocardiogram data based on the electrocardiogram data by using a pre-trained neural network model; wherein the neural network model is trained based on the correlations between thyroid function and changes in electrocardiogram characteristics.

Abstract: According to an embodiment of the present disclosure, there are disclosed a method, program and device for interpreting medical data based on explainable artificial intelligence, which are performed by a computing device. The method may include: training a first neural network model to estimate a positive or negative for a disease with respect to first medical data based on the first medical data; and training a second neural network model configured to transform features of second medical data so that a positive or negative for the disease with respect to the second medical data is estimated to be opposite by using the trained first neural network model.

Abstract: The present invention relates to a method for generating a prediction result for predicting an occurrence of fatal symptoms of a subject in advance, a method for performing data classification by using data augmentation in mechanical learning for the same, and a computing device using the same. Particularly, the computing device according to the present invention acquires vital signs of the subject, converts the same into individuated data, generates analysis information from the individuated data on the basis of a machine learning model, generates a prediction result by referring to the analysis information, and provides the prediction result to an external entity.

Abstract: The present invention relates to a method for generating a prediction result for predicting an occurrence of fatal symptoms of a subject in advance, a method for performing data classification by using data augmentation in mechanical learning for the same, and a computing device using the same. Particularly, the computing device according to the present invention acquires vital signs of the subject, converts the same into individuated data, generates analysis information from the individuated data on the basis of a machine learning model, generates a prediction result by referring to the analysis information, and provides the prediction result to an external entity.

Abstract: The present invention relates to a method for generating a prediction. result for predicting an occurrence of fatal symptoms of a subject in advance, a method for performing data classification by using data augmentation in mechanical learning for the same, and a computing device using the same. Particularly, the computing device according to the present invention acquires vital signs of the subject, converts the same into individuated data, generates analysis information from the individuated data on the basis of a machine learning model, generates a prediction result by referring to the analysis information, and provides the prediction result to an external entity.