Abstract:
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.
Abstract:
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.
Abstract:
A method of predicting a potential degree of Coronary Artery Calcification (CAC) risk includes receiving a patient's medical test data relating to CAC; determining a cluster to which the patient's medical test data belong based on an age of the patient; extracting a risk factor score including at least two Coronary Artery Calcification Scores (CACSs) from the medical test data; storing a plurality of prediction models used for predicting a potential degree of CAC risk; and predicting a potential degree of CAC risk at a specific point in time by applying a CACS growth rate of the patient's medical test data calculated using the at least two CACSs of the patient's medical test data and 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.
Abstract:
A semiconductor device may include a first memory cell connected to a bit-line and a first word-line, a second memory cell connected to a complementary bit-line and a second word-line, and an equalizer. The equalizer may be configured to transition a voltage of the bit-line and the complementary bit-line from a first voltage to a second voltage different from the first voltage at a first time period when the bit-line and complementary bit line are floating, and to transition the voltage of at least one of the bit-line and the complementary bit-line from the second voltage to a third voltage at a second time period after the first time period when the bit-line and complementary bit line are floating, the third voltage being different from the first and second voltages.
Abstract:
Provided are semiconductor packages and electronic systems including the same. A first memory chip may be stacked on a first portion of a substrate. A controller chip may be stacked on a second portion of the substrate, which is different from the first portion. At least one first bonding wire may directly connect the first memory chip with the controller chip. At least one second bonding wire may directly connect the first memory chip with the substrate, and may be electrically connected with the at least one first bonding wire.
Abstract:
An apparatus and a method predict an upcoming stage of carotid stenosis. The apparatus includes a receiving unit, a cluster determining unit, a risk factor score extracting unit, a prediction model storage unit, and a predicting unit. The method includes receiving a patient's medical test data relating to carotid stenosis; determining a cluster to which the patient's medical test data belong based on a gender of the patient; extracting from the patient's medical test data a risk factor score comprising a result of carotid stenosis ultrasonography; storing a plurality of prediction models used to predict an upcoming stage of carotid stenosis; and obtaining an outcome by applying a value indicating a stage of carotid stenosis corresponding to the result of carotid stenosis ultrasonography and the extracted risk factor score to the prediction model corresponding to the determined cluster among the plurality of prediction models.