Hye Jin Kam

Differences among admitting departments in alerts and alert overrides for drug-drug interaction.

To determine differences in the incidence and risk factors of alerts for drug–drug interaction (DDI) and the rate of alert overrides by an admitting department.

Prediction of 4-year risk for coronary artery calcification using ensemble-based classification

The progression of coronary artery calcification (CAC) has been regarded as an important risk factor of coronary artery disease (CAD), which is the biggest cause of death. Because CAC occurrence increases the risk of CAD by a factor of ten, the one whose coronary artery is calcified should pay more attention to the health management. However, performing the computerized tomography (CT) scan to check if coronary artery is calcified as a regular examination might be inefficient due to its high cost. Therefore, it is required to identify high risk persons who need regular follow-up checks of CAC or low risk ones who can avoid unnecessary CT scans. Due to this reason, we develop a 4-year prediction model for a new occurrence of CAC based on data collected by the regular health examination. We build the prediction model using ensemble-based methods to handle imbalanced dataset. Experimental results show that the developed prediction models provided a reasonable accuracy (AUC 75%), which is about 5% higher than the model built by the other imbalanced classification method.

Identifying relatively high-risk group of coronary artery calcification based on progression rate: Statistical and machine learning methods

Coronary artery calcification (CAC) score is an important predictor of coronary artery disease (CAD), which is the primary cause of death in advanced countries. Early prediction of high-risk of CAC based on progression rate enables people to prevent CAD from developing into severe symptoms and diseases. In this study, we developed various classifiers to identify patients in high risk of CAC using statistical and machine learning methods, and compared them with performance accuracy. For statistical approaches, linear regression based classifier and logistic regression model were developed. For machine learning approaches, we suggested three kinds of ensemble-based classifiers (best, top-k, and voting method) to deal with imbalanced distribution of our data set. Ensemble voting method outperformed all other methods including regression methods as AUC was 0.781.