AIMC Topic: Republic of Korea
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Constructing prediction models and analyzing factors in suicidal ideation using machine learning, focusing on the older population.
PloS one
Jul 22, 2024
Suicide among the older population is a significant public health concern in South Korea. As the older individuals have long considered suicide before committing suicide trials, it is important to analyze the suicidal ideation that precedes the suici...
Machine Learning-Based Identification of Diagnostic Biomarkers for Korean Male Sarcopenia Through Integrative DNA Methylation and Methylation Risk Score: From the Korean Genomic Epidemiology Study (KoGES).
Journal of Korean medical science
Jul 8, 2024
BACKGROUND: Sarcopenia, characterized by a progressive decline in muscle mass, strength, and function, is primarily attributable to aging. DNA methylation, influenced by both genetic predispositions and environmental exposures, plays a significant ro...
Artificial intelligence-based evaluation of carotid artery compressibility via point-of-care ultrasound in determining the return of spontaneous circulation during cardiopulmonary resuscitation.
Resuscitation
Jul 5, 2024
AIM: This study introduces RealCAC-Net, an artificial intelligence (AI) system, to quantify carotid artery compressibility (CAC) and determine the return of spontaneous circulation (ROSC) during cardiopulmonary resuscitation.
Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.
Journal of medical Internet research
Jul 3, 2024
BACKGROUND: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability.
Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence.
Annals of laboratory medicine
Jul 2, 2024
BACKGROUND: Healthcare 4.0. refers to the integration of advanced technologies, such as artificial intelligence (AI) and big data analysis, into the healthcare sector. Recognizing the impact of Healthcare 4.0 technologies in laboratory medicine (LM),...
Overground Gait Training With a Wearable Robot in Children With Cerebral Palsy: A Randomized Clinical Trial.
JAMA network open
Jul 1, 2024
IMPORTANCE: Cerebral palsy (CP) is the most common developmental motor disorder in children. Robot-assisted gait training (RAGT) using a wearable robot can provide intensive overground walking experience.
Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.
Scientific reports
Jun 28, 2024
This study aimed to develop and validate a machine learning (ML) model tailored to the Korean population with type 2 diabetes mellitus (T2DM) to provide a superior method for predicting the development of cardiovascular disease (CVD), a major chronic...
Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010-2023).
Scientific reports
Jun 24, 2024
Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high risk of disability, morbidity, and mortality among older adults. However, predictions based on sequential neural network SO studies and the relationshi...
Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ.
The Science of the total environment
Jun 21, 2024
Short-term exposure to ground-level ozone (O) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O forecasting to mitigate these risks, focusing on ...
Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.
Scientific reports
Jun 18, 2024
Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and postnatal factors. This study uses machine learning and population data to evaluate the association between prepregnancy or perinatal risk factors an...
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