Dongwoo Chae

Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared more towards making accurate predictions. ML is a field of artificial intelligence concerned with developing algorithms and models to perform prediction tasks in the absence of explicit instructions. Most ML applications, despite being highly variable in the topics that they deal with, generally follow a common workflow. For classification tasks, a re

Postoperative nausea and vomiting is the most common side-effect of opioid-based intravenous patient-controlled analgesia. Apfel's simplified risk score is popular but it has some limitations. We developed and validated a dynamic predictive model for nausea or vomiting up to 48 postoperative hours, available as an online web application. Fentanyl was used by 22,144 adult patients for analgesia after non-cardiac surgery under general anaesthesia: we randomly divided them into development (80%) an

Phage therapy is progressively being recognized as a viable alternative to conventional antibiotic treatments, particularly in the context of multi-drug resistant bacterial challenges. However, the intricacies of the pharmacokinetics and pharmacodynamics (PKPD) pertaining to phages remain inadequately elucidated. A salient characteristic of phage PKPD is the inherent ability of phages to undergo replication. In this review, I proffer mathematical models that delineate the intricate dynamics enco

Item response theory (IRT) has been recently adopted to successfully characterize the progression of Parkinson's disease using serial Unified Parkinson's Disease Rating Scale (UPDRS) measurements. However, it has yet to be applied in predicting the longitudinal changes of levodopa dose requirements in the real-world setting. Here we use IRT to extract two latent variables that represent tremor and non-tremor-related symptoms from baseline assessments of UPDRS Part III scores. We show that relati

The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged >18 years who h

The early detection of graft failure in pediatric liver transplantation is crucial for appropriate intervention. Graft failure is associated with numerous perioperative risk factors. This study aimed to develop an individualized predictive model for 90-days graft failure in pediatric liver transplantation using machine learning methods. We conducted a single-center retrospective cohort study. A total of 87 liver transplantation cases performed in patients aged < 12 years at the Severance Hospita

Our predictive model, which incorporated postoperative laboratory values, especially SCr levels, in addition to preoperative and intraoperative factors, effectively predicted the occurrence of CKD 1 year after RN or PN and may be helpful for comprehensive management planning.

We aimed to develop a risk scoring system for 1-week and 1-month mortality after major non-cardiac surgery, and assess the impact of postoperative factors on 1-week and 1-month mortality using machine learning algorithms. We retrospectively reviewed the medical records of 21,510 patients who were transfused with red blood cells during non-cardiac surgery and collected pre-, intra-, and postoperative features. We derived two patient cohorts to predict 1-week and 1-month mortality and randomly spl

Our findings suggest that the telmisartan/rosuvastatin FDC is bioequivalent to coadministration of separate tablets, and both treatments were safe and well tolerated. Administration of this FDC tablet is expected to improve patient compliance.

Quantitative systems pharmacology (QSP) can be regarded as a hybrid of pharmacometrics and systems biology. Here, we introduce the basic concepts related to dynamical systems theory that are fundamental to the analysis of systems biology models. Determination of the fixed points and their local stabilities constitute the most important step. Illustration of a phase portrait further helps investigate multistability and bifurcation behavior. As a motivating example, we examine a cell circuit model

Bepotastine, a second-generation antihistamine for allergic rhinitis and urticaria, is widely used in all age groups but lacks appropriate dosing guidelines for pediatric patients, leading to off-label prescriptions. We conducted this study to propose an optimal dosing regimen for pediatric patients based on population pharmacokinetic (popPK) and physiologically based pharmacokinetic (PBPK) models using data from two previous trials. A popPK model was built using NONMEM software. A one-compartme

Telmisartan, an angiotensin receptor blocker (ARB), is indicated for the treatment of essential hypertension. This study aimed to develop a mechanistic model of telmisartan drug effect in human beings using non-invasive markers. Data were acquired from a previous study where telmisartan 80 mg was given once daily for 6 days. Systolic (SBP) and diastolic blood pressure (DBP) and heart rate (HR) were measured before dosing for days 1-5 and serially after the last dose. Mean arterial pressure (MAP)