Yoon, Soo-Bin
Seoul National University · 医学
研究室紹介
Professor Yoon, Soo-Bin's research lab specializes in clinical data-driven healthcare innovation, focusing on leveraging machine learning and big data analytics to improve patient outcomes in critical care and perioperative medicine. The lab investigates real-time biosignal monitoring, predictive modeling for acute complications such as acute kidney injury and cardiac arrest, and the pathophysiology of rare but high-risk conditions like pheochromocytoma. By integrating advanced physiological signals—such as ECG-derived heart rate variability and EEG-based depth of anesthesia—with statistical and machine learning models, the lab aims to develop actionable, data-driven decision support tools for clinicians.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15In 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
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
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
For pheochromocytoma and sympathetic paraganglioma (PPGL), surgery can be used as a curative treatment; however, the life-threatening risk of perioperative hemodynamic instability (HI) presents challenges. This study aimed to analyze the incidence and predictive factors of perioperative HI. The electronic medical records of 114 consecutive patients who underwent surgery for PPGLs at our institution were retrospectively reviewed. HI was defined as one or more episodes of systolic blood pressure >
Clostridium difficile infection (CDI) is one of the most common nosocomial infections. Dysbiosis of the gut microbiota due to consumption of antibiotics is a major contributor to CDI. Recently, fecal microbiota transplantation (FMT) has been applied to treat CDI. However, FMT has important limitations including uncontrolled exposure to pathogens and standardization issues. Therefore, it is necessary to evaluate alternative treatment methods, such as bacteriotherapy, as well as the mechanism thro
Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected to train machine-learning models. Long short-term memory (LSTM) and gradient-boosted regression tree
Healing chronic diabetic wounds is challenging because of excessive reactive oxygen species (ROS) and hypoxia in the wound microenvironment. To address this issue, we propose a hydrogel wound dressing composed of polyethylene glycol (PEG) cross-linked with a biomimetic catalase, Fe-containing porphyrin (FeP) (i.e., FeP hydrogel). The immobilized FeP can serve as a catalyst for both ROS scavenging and O 2 generation. The properties of the hydrogels were optimized by varying the composition ratios
BACKGROUND: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures. METHODS: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were
The American Society of Anesthesiologist's Physical Status (ASA-PS) classification system assesses comorbidities before sedation and analgesia, but inconsistencies among raters have hindered its objective use. This study aimed to develop natural language processing (NLP) models to classify ASA-PS using pre-anesthesia evaluation summaries, comparing their performance to human physicians. Data from 717,389 surgical cases in a tertiary hospital (October 2004-May 2023) was split into training, tunin
The widespread use of remifentanil during total intravenous anesthesia (TIVA) has raised concerns about the risk of postoperative remifentanil-associated pain. Although a recent meta-analysis suggests that remifentanil-associated pain is unlikely to occur in patients with TIVA because of the protective effect of co-administered propofol, the evidence is not conclusive. We retrospectively assessed 635 patients who received robotic thyroid surgery under TIVA to evaluate the risk of remifentanil-as
We propose a hydrogel immobilized with manganese porphyrin (MnP), a biomimetic superoxide dismutase (SOD), and catalase (CAT) to modulate reactive oxygen species (ROS) and hypoxia that impede the repair of large bone defects. Our hydrogel synthesis involved thiolated chitosan and polyethylene glycol-maleimide conjugated with MnPs (MnP-PEG-MAL), which enabled in situ gelation via a click reaction. Through optimization, a hydrogel with mechanical properties and catalytic effects favorable for bone
This study evaluated the effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin (SpHb) measurements in liver transplantation recipients. Overall, 1465 SpHb and laboratory hemoglobin (Hb) measurement pairs (n = 296 patients) were analyzed. Patients were grouped into normal (< 1.2 mg/dL), mild-to-moderate (1.2-3.0 mg/dL), and severe (> 3.0 mg/dL) hyperbilirubinemia groups based on the preoperative serum total bilirubin levels. Bland-Altman analysis showed a bias of 0.20