AIMC Topic: Republic of Korea

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Development of a cervical cancer progress prediction tool for human papillomavirus-positive Koreans: A support vector machine-based approach.

The Journal of international medical research
OBJECTIVES: To develop a Web-based tool to draw attention to patients positive for human papillomavirus (HPV) who have a high risk of progression to cervical cancer, in order to increase compliance with follow-up examinations and facilitate good doct...

Prediction of effluent concentration in a wastewater treatment plant using machine learning models.

Journal of environmental sciences (China)
Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentratio...

Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea.

Environmental science and pollution research international
This research aimed at developing comprehensive assessments of physicochemical quality of groundwater for drinking and irrigation purposes at Dalcheon in Ulsan City, Korea. The mean concentration of major ions represented as follows: Ca (94.3 mg/L) >...

Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.

The Science of the total environment
Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vecto...

Key drivers of microcystin-producing cyanobacteria in South Korean eutrophic waters determined with data-driven models.

Journal of environmental management
The rise in cyanobacterial harmful algal blooms (CHABs), driven by eutrophication and climate change, necessitates understanding cyanotoxin conditions to mitigate risks. However, limited studies have explored the influencing factors of cyanobacterial...

Confidence-linked and uncertainty-based staged framework for phenotype validation using large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study develops and validates the confidence-linked and uncertainty-based staged (CLUES) framework by integrating large language models (LLMs) with uncertainty quantification to assist manual chart review while ensuring reliability th...

Comparative Analysis of Large Language Models for Answering Cancer-Related Questions in Korean.

Yonsei medical journal
PURPOSE: Large language models (LLMs) have shown potential in medicine, transforming patient education, clinical decision support, and medical research. However, the effectiveness of LLMs in providing accurate medical information, particularly in non...

Machine learning-based prediction of ambient CO and CH concentrations with high temporal resolution in Seoul metropolitan area.

Environmental pollution (Barking, Essex : 1987)
Machine learning has the potential to support the growing need for high-resolution greenhouse gas monitoring in urban and industrial environments, where deploying extensive sensor networks is often limited by cost and operational challenges. This stu...

Deep learning-based forecasting of daily maximum ozone levels and assessment of socioeconomic and health impacts in South Korea.

The Science of the total environment
Accurate forecasting of ground-level ozone (O) is essential for assessing its public health and socioeconomic impacts. This study evaluates the performance of three deep learning models-Deep Convolutional Neural Networks (Deep-CNN), Long Short-Term M...