Publications by authors named "Byeolhee Kim"
Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of cardiovascular health. While high-dose statin therapy is often recommended for LDL-C reduction, careful consideration is needed due to patient-specific factors and potential side effects. This study aimed to develop a machine learning (ML) model to estimate the likelihood of achieving target LDL-C levels in patients hospitalized for coronary artery disease and treated with moderate-dose statins.
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- Predicting major adverse cardiovascular events (MACE) is important due to high readmission rates and serious health consequences, but current models rely on limited patient data at a single time point.
- A new self-attention-based model was developed to predict MACE within 3 years using extensive time series data from electronic medical records, enhancing accuracy by considering multiple patient features.
- Transfer learning techniques were applied to enable effective predictions even in hospitals with less data, and the model's performance improved significantly, showing higher AUROC scores and confirming predictive effectiveness through survival analysis.
Loop diuretics are prevailing drugs to manage fluid overload in heart failure. However, adjusting to loop diuretic doses is strenuous due to the lack of a diuretic guideline. Accordingly, we developed a novel clinician decision support system for adjusting loop diuretics dosage with a Long Short-Term Memory (LSTM) algorithm using time-series EMRs.
View Article and Find Full Text PDFBackground: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling.
Objective: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods.