이형철 교수
Hyung Chul Lee
서울대학교 · 의학
연구실 소개
이 교수의 연구실은 마취 과정 중 수집되는 고해상도 생체신호 데이터의 정밀한 기록과 분석을 핵심으로 하며, 특히 애널레시아 인포메이션 관리 시스템(AIMS)의 한계를 보완하기 위한 전용 기록 소프트웨어 '바이탈 레코더' 개발로 출발했습니다. 생체신호 데이터베이스(VitalDB)를 구축하여 기계학습 기반의 생체신호 분석 연구를 활성화하고, 심장·간 이식 수술 후 급성 신손상(AKI) 예측 모델 개발을 위해 머신러닝 기법(랜덤 포레스트, 그래디언트 부스팅 등)과 전통적 회귀분석을 비교·적용하고 있습니다. 특히 딥러닝 기반의 뇌전도 지수(BIS) 예측 모델 개발을 통해 마취 약물 동역학 예측의 정확도를 향상시키는 데도 기여하고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15The 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
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
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
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
The deep learning model-predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.
These results suggest that DHA-mediated vasodilatation is mediated through CYP epoxygenase metabolites by activation of vascular BK channels.
Higher intraoperative BP variability is associated with higher risks of postoperative AKI after noncardiac surgery, independent of hypotension and other clinical characteristics.
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
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
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
The findings of this prognostic study suggest that the real-time prediction model for massive transfusion showed high accuracy of prediction performance, enabling early intervention for high-risk patients. It suggests strong confidence in artificial intelligence-assisted clinical decision support systems in the operating field.
The McGrath MAC videolaryngoscope showed a higher first-attempt success rate for tracheal intubation and a shorter intubation time than the Optiscope video stylet in cervical spine patients with manual inline stabilization during tracheal intubation. These results suggest that the McGrath MAC videolaryngoscope may be a better option for tracheal intubation in such patients.
Old age and radiographic predictors indicating large tongue size (large TA, long alveolar line of the mandible to the hyoid bone and mandible to the hyoid bone) were associated with an increased rate of difficult laryngoscopy in acromegaly patients. Preoperative radiographic measurements of tongue size can be helpful for safe airway management in such patients.
대표 연구 분야
이형철 교수의 연구를 Nubint에서 더 깊이 살펴보세요
이 연구실의 논문을 앱에서 열어 AI와 함께 읽고, 핵심을 요약하고, 내 글에 인용하세요.