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Beyond XGBoost and SHAP: Unveiling true feature importance.
J Hazard Mater
January 2025
Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-ku, Tokyo 135-8181, Japan. Electronic address:
This paper outlines key machine learning principles, focusing on the use of XGBoost and SHAP values to assist researchers in avoiding analytical pitfalls. XGBoost builds models by incrementally adding decision trees, each addressing the errors of the previous one, which can result in inflated feature importance scores due to the method's emphasis on misclassified examples. While SHAP values provide a theoretically robust way to interpret predictions, their dependence on model structure and feature interactions can introduce biases.
View Article and Find Full Text PDFMapping nutrient pollution in inland water bodies using multi-platform hyperspectral imagery and deep regression network.
J Hazard Mater
January 2025
Shanghai Municipal Institute of Surveying and Mapping, Shanghai, 200063, China.
Inland waters face multiple threats from human activities and natural factors, leading to frequent water quality issues, particularly the significant challenge of eutrophication. Hyperspectral remote sensing provides rich spectral information, enabling timely and accurate assessment of water quality status and trends. To address the challenge of inaccurate water quality mapping, we propose a novel deep learning framework for multi-parameter estimation from hyperspectral imagery.
View Article and Find Full Text PDFPotency and quantitative risk assessment are essential for determining safe concentrations for the formulation of potential skin sensitizers into consumer products. Several new approach methodologies (NAMs) for skin sensitization hazard assessment have been developed, validated, and adopted in OECD test guidelines. However, work is ongoing to develop NAMs for predicting skin sensitization potency on a quantitative scale for use as a point of departure (POD) in next-generation risk assessment (NGRA).
View Article and Find Full Text PDFAnalyzing and mitigating the risks of patient harm during operating room to intensive care unit patient handoffs.
Int J Qual Health Care
January 2025
Faculdade de Medicina, Universidade de São Paulo (USP), Av. Dr. Arnaldo, 455 - Sala 4107, São Paulo, São Paulo 01246-903, Brazil.
Patients continue to suffer from preventable harm and uneven quality outcomes. Reliable clinical outcomes depend on the quality of robust administrative systems and reliable support processes. Critically ill patient handoffs from the operating room (OR) to the intensive care unit (ICU) are known to be high-risk events.
View Article and Find Full Text PDFThe Association Between Tuberculosis and Osteoporosis: A Systematic Review and Meta-Analysis.
Cureus
December 2024
Internal Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, THA.
Recent research suggests that tuberculosis (TB) may pose a potential risk factor for osteoporosis, although the available evidence remains limited. This study aimed to comprehensively assess osteoporosis risk in TB patients through systematic review and meta-analysis methodology. Two investigators independently conducted a literature search using the Medical Literature Analysis and Retrieval System Online (MEDLINE) and Excerpta Medica Database (EMBASE) databases up to April 2024.
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