Minsu Chae

Week-ahead prediction of depressive episodes using wearable-derived circadian biomarkers: A multicenter deep learning study with risk-based operating thresholds

Byeongsu Kim, Minsu Chae, Hyungjun Seo, Jaegwon Jeong et autres

Early detection of depressive episodes is important because shorter duration of untreated illness is associated with better outcomes, yet routine care remains largely reactive and dependent on retrospective self-report. Wearables enable passive, continuous monitoring, and circadian disruptions in sleep-wake, activity, and diurnal …

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Advanced Pharmaceutical Recognition System Based on Deep Learning for Mobile Medication Identification

Minsu Chae, Jeung Min Lee, HwaMin Lee�

Medication misidentification poses a significant risk to patient safety, particularly for elderly individuals managing complex prescriptions. To address this, we developed a deep learning-based system for real-time medication recognition on mobile devices. Through a comparative analysis of convolutional neural networks, ResNet101 was …

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Early Prediction of Depressive Episodes in Mood Disorders Using Circadian Rhythm Indicators and Deep Learning

Byeongsu Kim, Minsu Chae, Yi-Hyun Kim, Yeongmin Kim et autres

The early prediction of depressive mood episodes is crucial for effective intervention in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD). This study explores a predictive framework leveraging digital phenotypic data collected from smartphones and smartwatches, with a focus on …

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Predicting Sudden Sensorineural Hearing Loss Recovery with Patient-Personalized Seigel’s Criteria Using Machine Learning

Sanghyun Shon, Minsu Chae, HwaMin Lee�, June Choi

BACKGROUND: Accurate prognostic prediction is crucial for managing Idiopathic Sudden Sensorineural Hearing Loss (ISSHL). Previous studies developing ISSHL prognosis models often overlooked individual variability in hearing damage by relying on fixed frequency domains. This study aims to develop models predicting ISSHL prognosis …

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Revolutionizing Echocardiography: A Comparative Study of Advanced AI Models for Precise Left Ventricular Segmentation

Dong-Ok Kim, Minsu Chae, HwaMin Lee�

Cardiovascular diseases, a leading cause of global mortality, underscore the urgency for refined diagnostic techniques. Among these, cardiomyopathies characterized by abnormal heart wall thickening present a formidable challenge, exacerbated by aging populations and the side effects of chemotherapy. Traditional echocardiogram analysis, demanding …

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Forward Head Posture Classification Using Deep Learning Models on Facial Recognition Data

Byeongsu Kim, Minsu Chae, Y.H. Kim, Inyong Jeong et autres

Forward Head Posture (FHP) refers to a condition where the head protrudes forward, significantly contributing to neck pain and being associated with decreased productivity and psychological distress. This study investigates the nuanced classification of FHP and proposes a universally applicable methodology for …

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Hearing Recovery Prediction for Patients with Chronic Otitis Media Who Underwent Canal-Wall-Down Mastoidectomy

Minsu Chae, Hee Soo Yoon, HwaMin Lee�, June Choi

Background: Chronic otitis media affects approximately 2% of the global population, causing significant hearing loss and diminishing the quality of life. However, there is a lack of studies focusing on outcome prediction for otitis media patients undergoing canal-wall-down mastoidectomy. Methods: This study …

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A Prediction of in-Hospital Cardiac Arrest Risk Scoring Based on Machine Learning

Minsu Chae, HwaMin Lee�

According to the Korea Disease Control and Prevention Agency (KCDC), 591 out of 33,402 cardiac arrests in 2021 occurred in hospitals. A recent study shows that the golden time to detect a cardiac arrest is less than three minutes. It means early …

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Machine Learning-Based Prediction Models of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning

Yeongmin Kim, Minsu Chae, Namjun Cho, Hyo‐Wook Gil et autres

The prognosis of patients with acute pesticide poisoning depends on their acute respiratory condition. Here, we propose machine learning models to predict acute respiratory failure in patients with acute pesticide poisoning using a decision tree, logistic regression, and random forests, support vector …

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Machine Learning-Based Cardiac Arrest Prediction for Early Warning System

Minsu Chae, Hyo‐Wook Gil, Namjun Cho, HwaMin Lee�

The early warning system detects early and responds quickly to emergencies in high-risk patients, such as cardiac arrest in hospitalized patients. However, traditional early warning systems have the problem of frequent false alarms due to low positive predictive value and sensitivity. We …

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Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

Minsu Chae, Sang-Wook Han, Hyo‐Wook Gil, Namjun Cho et autres

Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false …

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Design of Middleware to Support Auto-scaling in Docker-Based Multi Host Environment

Minsu Chae, Sang-Wook Han, HwaMin Lee�

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