Survey-based diagnosis method and system therefor

A survey-based diagnosis system comprising:

• one or more processors;
• and
• a memory for storing one or more instructions,
• wherein the one or more processors execute one or more of the stored instructions to perform
• an operation of acquiring response information about a diagnosee with respect to a plurality of survey questions, wherein the plurality of survey questions include a first survey question for diagnosing an energy and blood generation function, a second survey question for diagnosing an energy and blood circulation function, and a third survey question for diagnosing an energy and blood balance adjustment function;
• and
• an operation of performing a diagnosis for the diagnosee based on the acquired response information,
• wherein the performing of the diagnosis comprises Steps (i) or Steps (ii):
• Steps (i):
• acquiring a first autoencoder-based deep learning model trained using response information about a normal class and a patient class with respect to the plurality of survey questions, wherein the first autoencoder-based deep learning model includes a first encoder, a first decoder, and a first classifier for classifying the normal class and the patient class, the first encoder and the first decoder are trained based on a first reconstruction loss value, and the first encoder is further trained based on a first classification loss value of the first classifier for a first latent vector while the first decoder is frozen;
• extracting the first latent vector for the acquired response information by the first encoder;
• acquiring a first confidence score for each class for the extracted first latent vector by the first classifier;
• and
• diagnosing the health condition of the diagnosee based on the acquired first confidence score,
• Steps (ii):
• acquiring a second autoencoder-based deep learning model trained using response information about a patient class with respect to the plurality of survey questions, wherein the patient class consists of patients in a first disease class and a second disease class, the first disease class is a different disease class from the second disease class, the second autoencoder-based deep learning model includes a second encoder, a second decoder, and a second classifier for classifying the first disease class and the second disease class, the second encoder and the second decoder are trained based on a second reconstruction loss value, and the second encoder is further trained based on a second classification loss value of the second classifier for a second latent vector while the second decoder is frozen;
• extracting the second latent vector for the acquired response information by the second encoder;
• acquiring a second confidence score for each class for the extracted second latent vector by the second classifier;
• and
• diagnosing a disease type of the diagnosee based on the acquired second confidence score.

A computer program connected with a computing device and stored in a non-transitory computer-readable recording medium to execute steps of:

• acquiring response information about a diagnosee with respect to a plurality of survey questions, wherein the plurality of survey questions include a first survey question for diagnosing an energy and blood generation function, a second survey question for diagnosing an energy and blood circulation function, and a third survey question for diagnosing an energy and blood balance adjustment function;
• and
• performing a diagnosis for the diagnosee based on the acquired response information,
• wherein the performing of the diagnosis comprises Steps (i) or Steps (ii):
• Steps (i):
• acquiring a first autoencoder-based deep learning model trained using response information about a normal class and a patient class with respect to the plurality of survey questions, wherein the first autoencoder-based deep learning model includes a first encoder, a first decoder, and a first classifier for classifying the normal class and the patient class, the first encoder and the first decoder are trained based on a first reconstruction loss value, and the first encoder is further trained based on a first classification loss value of the first classifier for a first latent vector while the first decoder is frozen;
• extracting the first latent vector for the acquired response information by the first encoder;
• acquiring a first confidence score for each class for the extracted first latent vector by the first classifier;
• and
• diagnosing the health condition of the diagnosee based on the acquired first confidence score,
• Steps (ii):
• acquiring a second autoencoder-based deep learning model trained using response information about a patient class with respect to the plurality of survey questions, wherein the patient class consists of patients in a first disease class and a second disease class, the first disease class is a different disease class from the second disease class, the second autoencoder-based deep learning model includes a second encoder, a second decoder, and a second classifier for classifying the first disease class and the second disease class, the second encoder and the second decoder are trained based on a second reconstruction loss value, and the second encoder is further trained based on a second classification loss value of the second classifier for a second latent vector while the second decoder is frozen;
• extracting the second latent vector for the acquired response information by the second encoder;
• acquiring a second confidence score for each class for the extracted second latent vector by the second classifier;
• and
• diagnosing a disease type of the diagnosee based on the acquired second confidence score.