AIMC Topic: Republic of Korea
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Development and validation of an automated machine for self-injury assessment via young Koreans' natural writings.
PloS one
Jan 16, 2025
Self-injury is common in all countries, and 20% of South Korean youths experience self-injury. One of the barriers to assessment and treatment planning is the tendency of young self-injurers to conceal their identities. Following a new stream of rese...
Development of deep learning auto-encoder algorithms for predicting alcohol use in Korean adolescents based on cross-sectional data.
Social science & medicine (1982)
Jan 10, 2025
Alcohol is a highly addictive substance, presenting significant global public health concerns, particularly among adolescents. Previous studies have been limited by traditional research methods, making it challenging to encompass diverse risk factors...
Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.
Scientific reports
Jan 7, 2025
Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims...
Key risk factors of generalized anxiety disorder in adolescents: machine learning study.
Frontiers in public health
Jan 7, 2025
Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, including Generalized Anxiety Disorder (GAD), being particularly prevalent. Despite its significant impact, GAD in adolescents often remains underdiag...
Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study.
Scientific reports
Jan 6, 2025
Kawasaki disease (KD) is a leading cause of acquired heart disease in children, often resulting in coronary artery complications such as dilation, aneurysms, and stenosis. While intravenous immunoglobulin (IVIG) is effective in reducing immunologic i...
Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.
International journal of medical informatics
Dec 26, 2024
BACKGROUND: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve ...
Identifying potential medical aid beneficiaries using machine learning: A Korean Nationwide cohort study.
International journal of medical informatics
Dec 25, 2024
OBJECTIVE: To identify potential medical aid beneficiaries using demographic and medical history of individuals and analyzing important features qualitatively.
Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study.
BMC public health
Dec 20, 2024
BACKGROUND: Given the rapid increase in the prevalence of prostate cancer (PCa), identifying its risk factors and developing suitable risk prediction models has important implications for public health. We used machine learning (ML) approach to scree...
Prognostic models for progression-free survival in atypical meningioma: Comparison of machine learning-based approach and the COX model in an Asian multicenter study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 19, 2024
BACKGROUND AND PURPOSE: Atypical meningiomas are prevalent intracranial tumors with varied prognoses and recurrence rates. The role of adjuvant radiotherapy (ART) in atypical meningiomas remains debated. This study aimed to develop and validate a pro...
Profiling the AI speaker user: Machine learning insights into consumer adoption patterns.
PloS one
Dec 18, 2024
The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do s...
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