Apparatus and method for predicting potential change of coronary artery calcification (CAC) level

摘要

An apparatus and a method predict a patient's potential change of Coronary Artery Calcification (CAC) level using various risk factors including a Coronary Artery Calcification Score (CACS). The apparatus includes a receiving unit, a cluster determining unit, a risk factor score extracting unit, a prediction model storage unit, a prediction model learning unit, and a predicting unit, and the method includes a receiving process, a risk factor score extracting process, and an operation performing process.

主权项

1. An apparatus for predicting a potential change of a Coronary Artery Calcification (CAC) level, the apparatus comprising:
a receiving processor configured to receive a patient's medical test data relating to CAC and corresponding operation information; a cluster determining processor configured to determine a cluster to which the patient's medical test data belong based on a characteristic of the patient; a risk factor score extracting processor configured to extract a risk factor score from the patient's medical test data; a prediction model storage processor configured to store a plurality of prediction models used for predicting a potential CAC level; a prediction model learning processor configured to perform machine learning by applying the extracted risk factor score to a prediction model corresponding to the determined cluster to which the patient's medical test data belong among the plurality of prediction models; and a predicting processor configured to obtain an outcome by applying the extracted risk factor score to the prediction model corresponding to the determined cluster to which the patient's medical test data belong, wherein the extracted risk factor score comprises a Coronary Artery Calcification Score (CACS) and a corresponding measurement date, wherein the prediction model learning processor is further configured to classify all CACSs into at least two sections; and each section of the at least two sections is representative of a specific CAC level or a specific range of CAC levels, and wherein the prediction model learning processor is further configured to: assign a first outcome to the patient's medical test data when a CAC level corresponding to a last measured CACS of the patient's medical test data is higher than a CAC level corresponding to a first measured CACS of the patient's medical test data; and assign a second outcome to the patient's medical test data in other cases.