AIMC Topic: Republic of Korea

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Developing a novel Temporal Air-quality Risk Index using LSTM autoencoder: A case study with South Korean air quality data.

The Science of the total environment
As public awareness of environmental and health issues grows, providing accurate and accessible environmental risk information is essential for informed decision-making. Environmental indices simplify the complex impacts of various environmental fact...

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study.

JMIR aging
BACKGROUND: The global increase in life expectancy has not shown a similar rise in healthy life expectancy. Accurate assessment of biological aging is crucial for mitigating diseases and socioeconomic burdens associated with aging. Current biological...

Analyzing the impact of COVID-19 on seasonal infectious disease outbreak detection using hybrid SARIMAX-LSTM model.

Journal of infection and public health
BACKGROUND: This study estimates the incidence of seasonal infectious diseases, including influenza, norovirus, severe fever with thrombocytopenia syndrome (SFTS), and tsutsugamushi disease, in the Republic of Korea from 2005 to 2023. It also examine...

Development of a Mobile Intervention for Procrastination Augmented With a Semigenerative Chatbot for University Students: Pilot Randomized Controlled Trial.

JMIR mHealth and uHealth
BACKGROUND: Procrastination negatively affects university students' academics and mental health. Traditional time management apps lack therapeutic strategies like cognitive behavioral therapy to address procrastination's psychological aspects. Theref...

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Postoperative acute kidney injury (AKI) is a significant risk associated with surgeries under general anesthesia, often leading to increased mortality and morbidity. Existing predictive models for postoperative AKI are usually limited to ...

Communication challenges and experiences between parents and providers in South Korean paediatric emergency departments: a qualitative study to define AI-assisted communication agents.

BMJ open
OBJECTIVES: This study aimed to explore communication challenges between parents and healthcare providers in paediatric emergency departments (EDs) and to define the roles and functions of an artificial intelligence (AI)-assisted communication agent ...

Development and validation of a machine learning model for predicting pediatric metabolic syndrome using anthropometric and bioelectrical impedance parameters.

International journal of obesity (2005)
OBJECTIVE: Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This study developed a machine learning model to predict MS using anthropometric and bioelectrical imped...

Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women.

BMC women's health
BACKGROUND: The aim of this study was to develop a machine learning (ML) model for classifying osteoporosis in Korean women based on a large-scale population cohort study. This study also aimed to assess ML model performance compared with traditional...

An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea.

Journal of global health
BACKGROUND: Modelling can contribute to disease prevention and control strategies. Accurate predictions of future cases and mortality rates were essential for establishing appropriate policies during the COVID-19 pandemic. However, no single model yi...

The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.

Journal of environmental management
This study examines the complex relationships among artificial intelligence (AI) adoption in organizations, employee work overload, and pro-environmental behavior at work (PEBW), while examining the moderating role of self-efficacy in AI learning. Dr...