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An examination of daily CO emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models.
Environ Sci Pollut Res Int
January 2025
Department of Geology and Mineral Science, Kwara State University, Malete, P.M.B. 1530, Ilorin, Kwara State, Nigeria.
Human-induced global warming, primarily attributed to the rise in atmospheric CO, poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, India, the USA, and the EU27&UK).
View Article and Find Full Text PDFKMeansGraphMIL: A Weakly Supervised Multiple Instance Learning Model for Predicting Colorectal Cancer Tumor Mutational Burden.
Am J Pathol
January 2025
The Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Xinhu Street, Guangming New District, Shenzhen, 518107, Guangdong, China. Electronic address:
Colorectal cancer (CRC) is one of the top three most lethal malignancies worldwide, posing a significant threat to human health. Recently proposed immunotherapy checkpoint blockade treatments have proven effective for CRC, but their use depends on measuring specific biomarkers in patients. Among these biomarkers, Tumor Mutational Burden (TMB) has emerged as a novel indicator, traditionally requiring Next-Generation Sequencing (NGS) for measurement, which is time-consuming, labor-intensive, and costly.
View Article and Find Full Text PDFEvaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.
Atherosclerosis
December 2024
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address:
Background And Aims: An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, coronary artery disease (CAD) sequelae, and mortality. Large-scale metabolite biomarker profiling provides increased portability and objectivity in machine learning for disease prediction and gradation. However, these models have not been fully leveraged.
View Article and Find Full Text PDFDeveloping a decision support tool to predict delayed discharge from hospitals using machine learning.
BMC Health Serv Res
January 2025
Department of Industrial Engineering, Dalhousie University, PO Box 15000, Halifax, B3H 4R2, NS, Canada.
Background: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow.
View Article and Find Full Text PDFIdentification of biomarkers for knee osteoarthritis through clinical data and machine learning models.
Sci Rep
January 2025
Department of Orthopaedics, Traditional Chinese Medical Hospital of Gansu Province, Qilihe District, Guazhou Street 418, Lanzhou, 730050,, Gansu, China.
Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio.
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