Hyung Chul Lee

The current anaesthesia information management system (AIMS) has limited capability for the acquisition of high-quality vital signs data. We have developed a Vital Recorder program to overcome the disadvantages of AIMS and to support research. Physiological data of surgical patients were collected from 10 operating rooms using the Vital Recorder. The basic equipment used were a patient monitor, the anaesthesia machine, and the bispectral index (BIS) monitor. Infusion pumps, cardiac output monito

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to th

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtaine

Machine learning approaches were introduced for better or comparable predictive ability than statistical analysis to predict postoperative outcomes. We sought to compare the performance of machine learning approaches with that of logistic regression analysis to predict acute kidney injury after cardiac surgery. We retrospectively reviewed 2010 patients who underwent open heart surgery and thoracic aortic surgery. Baseline medical condition, intraoperative anesthesia, and surgery-related data wer

BACKGROUND: The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. METHODS: Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empiri

These results suggest that DHA-mediated vasodilatation is mediated through CYP epoxygenase metabolites by activation of vascular BK channels.

Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosti

BACKGROUND AND OBJECTIVES: High BP variability may cause AKI because of inappropriate kidney perfusion. This study aimed to investigate the association between intraoperative BP variability and postoperative AKI in patients who underwent noncardiac surgery. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We performed a cohort study of adults undergoing noncardiac surgery in hospitals in South Korea. We studied three cohorts using the following recording windows for intraoperative BP: discovery co

BACKGROUND: A real-time model for predicting short-term mortality in critically ill patients is needed to identify patients at imminent risk. However, the performance of the model needs to be validated in various clinical settings and ethnicities before its clinical application. In this study, we aim to develop an ensemble machine learning model using routinely measured clinical variables at a single academic institution in South Korea. METHODS: We developed an ensemble model using deep learning

We present the INSPIRE dataset, a publicly available research dataset in perioperative medicine, which includes approximately 130,000 surgical operations at an academic institution in South Korea over a ten-year period between 2011 and 2020. This comprehensive dataset includes patient characteristics such as age, sex, American Society of Anesthesiologists physical status classification, diagnosis, surgical procedure code, department, and type of anaesthesia. The dataset also includes vital signs

Bispectral index (BIS), a useful marker of anaesthetic depth, is calculated by a statistical multivariate model using nonlinear functions of electroencephalography-based subparameters. However, only a portion of the proprietary algorithm has been identified. We investigated the BIS algorithm using clinical big data and machine learning techniques. Retrospective data from 5,427 patients who underwent BIS monitoring during general anaesthesia were used, of which 80% and 20% were used as training d

Importance: Massive transfusion is essential to prevent complications during uncontrolled intraoperative hemorrhage. As massive transfusion requires time for blood product preparation and additional medical personnel for a team-based approach, early prediction of massive transfusion is crucial for appropriate management. Objective: To evaluate a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitor

This paper reviews the Republic of Korea's experience with electronic tax invoices for its value-added tax regime from the perspectives of tax policy makers and administrators. The paper evaluates Korea's implementation of electronic tax invoicing and analyzes its effect on tax compliance through enhanced transparency of business transactions and taxpayer services. First implemented in 2011, mandatory electronic tax invoicing has been credited with lowering tax compliance costs and raising the t

BACKGROUND: Patients with acromegaly have a high risk of difficult laryngoscopy. However, clinical predictors, such as upper lip bite test or modified Mallampati class, show limited predictive performance for difficult laryngoscopy in such patients. In this retrospective study, we evaluated radiographic indices obtained from skull lateral x-ray and ostiomeatal unit computed tomography images to predict difficult laryngoscopy in acromegaly patients. MATERIALS AND METHODS: Data on demographics, pr