AIMC Topic: Republic of Korea

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Effects of bisphosphonates after denosumab discontinuation and treatment effect heterogeneity using causal machine learning.

Scientific reports
Discontinuation of denosumab is associated with a rebound increase in osteoporotic fracture (OF) risk, and bisphosphonates (BPs) are commonly recommended as sequential therapy to mitigate this risk. However, their real-world effectiveness-and whether...

Analyzing the impact of the automatic ball strike system in professional baseball through a case study on KBO league data.

Scientific reports
Recent advancements in professional baseball have led to the introduction of the Automated Ball-Strike (ABS) system, or "robot umpires," which utilize machine learning, computer vision, and precise tracking technologies to automate ball-strike calls....

Reweaving the Threads of Korean History: AI-Driven Restoration of the Daegu-bu Household Registers (1681-1876).

Scientific data
In this study, we have applied advanced masked language models (MLMs)-BERT, DistilBERT, ELECTRA, and RoBERTa-to infer missing and misinterpreted values in comprehensive family register data. Our data compiles Daegu-bu household register books, trienn...

Predicting and explaining life satisfaction among older adults using tree-based ensemble models and SHAP: Evidence from the digital divide survey.

PloS one
As digital transformation continues to penetrate various sectors of society, the issue of the digital divide has become increasingly prominent. Against the backdrop of accelerating population aging, the barriers that older adults face in accessing an...

Factors Associated With Suicidal Ideation Among Persons With Disabilities in South Korea: Retrospective Observational Study.

JMIR formative research
BACKGROUND: South Korea has the highest suicide rate among the Organisation for Economic Co-operation and Development nations, with particularly elevated figures among persons with disabilities. Research has shown a strong correlation between suicida...

Development of machine learning models for prediction of current and future dementia.

PloS one
Dementia is among the most distressing and burdensome health challenges in aging populations. Treatment efficacy is limited; however, early diagnosis can delay or prevent disease progression. Previous machine learning-based prediction models have lim...

Performance of large language models in non-English medical ethics-related multiple choice questions: comparison of ChatGPT performance across versions and languages.

BMC medical ethics
BACKGROUND: As large language models (LLMs) evolve, assessing their competence in ethically sensitive domains such as medical ethics has become increasingly important. Since medical ethics is a universal component of medical education, disparities in...

COVID-19 severity analysis for clinical decision support based on machine learning approach.

Scientific reports
The COVID-19 pandemic has placed immense pressure on global healthcare systems, underscoring the urgent need for early and accurate prediction of disease severity to improve patient care and optimize resource allocation. Failure in ward allocation ca...

Exploring Age-Related Patterns in Smartphone Keystroke Dynamics Considering Temporal Variability: Cross-Sectional Study With AI-Based Analysis.

JMIR mHealth and uHealth
BACKGROUND: Keystroke dynamics on smartphones have emerged as a promising form of passive digital biomarker. While previous studies have explored their utility in several diseases and disorders, relatively few have examined how these dynamics change ...

Development of explainable machine learning models to predict side effects in patients with rheumatoid arthritis taking methotrexate treatment: a nationwide multicentre cohort study.

BMJ open
OBJECTIVES: Methotrexate (MTX) effectively controls rheumatoid arthritis (RA) but often leads to side effects (SE) such as gastrointestinal (GI) issues, liver toxicity and bone marrow suppression. To develop clinically interpretable machine learning ...