HbA1c RISK ESTIMATION DEVICE, HbA1c RISK ESTIMATION METHOD, AND COMPUTER PROGRAM
AssigneeNissin Foods Holdings Co., Ltd.
InventorsNoritaka ANDO, Naohisa SHOBAKO
Life of this patent
Estimating the term…
Abstract
Conventionally, it has been necessary to collect blood of a subject and perform biochemical analysis to measure HbA1c. However, in this method, a needle or the like needs to be invasively inserted into the skin of the subject, which causes a psychological or physical burden on the subject. According to the present invention, it is possible to non-invasively estimate an HbA1c risk based on attribute information and/or non-invasive biological information of a predetermined user by generating an HbA1c risk estimation model by machine learning based on attribute information and non-invasive biological information acquired from a plurality of subjects in advance and examination data of a blood examination.
Claims12
An HbA1c risk estimation device comprising:
- an information acquisition unit configured to acquire attribute information and non-invasive biological information of a predetermined user;
- an estimation model storage unit configured to store an HbA1c risk estimation model;
- and
- an estimation processing unit configured to calculate an HbA1c risk estimated value of the predetermined user based on the attribute information and/or the non-invasive biological information of the predetermined user by using the HbA1c risk estimation model.
An HbA1c risk estimation method comprising:
- a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured HbA1c measured value of a subject;
- a step of generating an HbA1c risk estimation model by machine learning based on the training data set;
- and
- a step of calculating an HbA1c risk estimated value of the predetermined user based on attribute information and/or non-invasive biological information of a predetermined user by using the HbA1c risk estimation model.
A computer program configured to cause a computer to execute:
- a step of storing a training data set including attribute information, non-invasive biological information, and a blood-measured HbA1c measured value of a subject;
- a step of generating an HbA1c risk estimation model by machine learning based on the training data set;
- and
- a step of calculating an HbA1c risk estimated value of the predetermined user based on attribute information and/or non-invasive biological information of a predetermined user by using the HbA1c risk estimation model.
Dependencies are inferred from the claim text.