AIMC Topic: Machine Learning

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Using machine learning models for predicting monthly iPTH levels in hemodialysis patients.

Computer methods and programs in biomedicine Nov 30, 2024
BACKGROUND AND OBJECTIVE: Intact parathyroid hormone (iPTH), also known as active parathyroid hormone, is an important indicator commonly for monitoring secondary hyperparathyroidism (SHPT) in patients undergoing hemodialysis. The aim of this study w...
Humans Machine Learning Retrospective Studies Female Male Parathyroid Hormone Taiwan Hyperparathyroidism, Secondary Predictive Value of Tests Registries Risk Assessment Renal Dialysis Time Factors
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The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging.

Journal of hazardous materials Nov 30, 2024
Assessing chemical toxicity in materials like plastic packaging is critical to safeguarding public health. This study presents the development of classification-based machine learning models to predict the toxicity of chemicals associated with plasti...
Product Packaging Plastics Toxicity Tests Hazardous Substances Humans Machine Learning Support Vector Machine
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Identification of an immune-related gene panel for the diagnosis of pulmonary arterial hypertension using bioinformatics and machine learning.

International immunopharmacology Nov 30, 2024
OBJECTIVE: This study aimed to screen an immune-related gene (IRG) panel and develop a novel approach for diagnosing pulmonary arterial hypertension (PAH) utilizing bioinformatics and machine learning (ML).
Male Lung Gene Expression Profiling Machine Learning Biomarkers Animals Mice, Inbred C57BL Pulmonary Artery Pulmonary Arterial Hypertension Mice Computational Biology Disease Models, Animal Humans Superoxide Dismutase-1
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Predicting sinonasal inverted papilloma attachment using machine learning: Current lessons and future directions.

American journal of otolaryngology Nov 30, 2024
BACKGROUND: Hyperostosis is a common radiographic feature of inverted papilloma (IP) tumor origin on computed tomography (CT). Herein, we developed a machine learning (ML) model capable of analyzing CT images and identifying IP attachment sites.
Aged Retrospective Studies Middle Aged Humans Tomography, X-Ray Computed Papilloma, Inverted Adult Machine Learning Female Paranasal Sinus Neoplasms Male Algorithms
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Toward automated small bowel capsule endoscopy reporting using a summarizing machine learning algorithm: The SUM UP study.

Clinics and research in hepatology and gastroenterology Nov 30, 2024
BACKGROUND AND OBJECTIVES: Deep learning (DL) algorithms demonstrate excellent diagnostic performance for the detection of vascular lesions via small bowel (SB) capsule endoscopy (CE), including vascular abnormalities with high (P2), intermediate (P1...
Intestine, Small Humans Deep Learning Capsule Endoscopy Machine Learning Algorithms
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Construction and validation of a machine learning-based prediction model for short-term mortality in critically ill patients with liver cirrhosis.

Clinics and research in hepatology and gastroenterology Nov 30, 2024
OBJECTIVE: Critically ill patients with liver cirrhosis generally have a poor prognosis due to complications such as multiple organ failure. This study aims to develop a machine learning-based prediction model to forecast short-term mortality in crit...
Female Aged Male Middle Aged Liver Cirrhosis Prognosis Critical Illness Intensive Care Units Humans Machine Learning
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Simulation and prediction of sulfamethazine migration, transformation and risk diffusion during cross-media infiltration from surface water to groundwater driven by dynamic water level: Machine learning coupled HYDRUS-GMS model.

Journal of environmental management Nov 30, 2024
Seasonal water level fluctuations in rivers significantly influenced the cross-media migration, transformation, and risk diffusion of antibiotics from the vadose zone into groundwater. This study developed a coupled model integrating machine learning...
Sulfamethazine Water Pollutants, Chemical Machine Learning Seasons Rivers Groundwater
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Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs.

The Knee Nov 30, 2024
BACKGROUND: Poor results occasionally occur after unicompartmental knee replacement (UKR). It is often difficult, even for experienced surgeons, to determine why patients have poor outcomes from radiographs. The aim was to compare the ability of expe...
Humans Machine Learning Knee Joint Retrospective Studies Female Arthroplasty, Replacement, Knee Aged Osteoarthritis, Knee Male Middle Aged Radiography
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Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of Iran.

Environmental science and pollution research international Nov 30, 2024
Land use changes are of critical importance in understanding and managing environmental sustainability and resource utilization. Machine learning algorithms (MLAs) have emerged as powerful tools for analyzing and predicting land use changes, offering...
Support Vector Machine Forests Iran Conservation of Natural Resources Machine Learning Algorithms Environmental Monitoring
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Integrating Metabolomics Domain Knowledge with Explainable Machine Learning in Atherosclerotic Cardiovascular Disease Classification.

International journal of molecular sciences Nov 30, 2024
Metabolomic data often present challenges due to high dimensionality, collinearity, and variability in metabolite concentrations. Machine learning (ML) application in metabolomic analyses is enabling the extraction of meaningful information from comp...
Female Aged Cross-Sectional Studies Humans Machine Learning Biomarkers Case-Control Studies Metabolomics Atherosclerosis Male Cardiovascular Diseases Middle Aged
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