AIMC Topic: Regression Analysis
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Mathematical optimization of multilinear and artificial neural network regressions for mineral composition of different tea types infusions.
Scientific reports
Aug 7, 2024
The objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish...
Predictors of High Healthcare Cost Among Patients with Generalized Myasthenia Gravis: A Combined Machine Learning and Regression Approach from a US Payer Perspective.
Applied health economics and health policy
Jul 13, 2024
BACKGROUND: High healthcare costs could arise from unmet needs. This study used random forest (RF) and regression methods to identify predictors of high costs from a US payer perspective in patients newly diagnosed with generalized myasthenia gravis ...
Machine learning vs. regression models to predict the risk of Legionella contamination in a hospital water network.
Annali di igiene : medicina preventiva e di comunita
Jul 11, 2024
INTRODUCTION: The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers.
Radiological characterization of the tailings of an abandoned copper mine using a neural network and geostatistical analysis through the Co-Kriging method.
Environmental geochemistry and health
Jul 9, 2024
The radiological characterization of soil contaminated with natural radionuclides enables the classification of the area under investigation, the optimization of laboratory measurements, and informed decision-making on potential site remediation. Neu...
Optimization of mid-infrared noninvasive blood-glucose prediction model by support vector regression coupled with different spectral features.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Jun 26, 2024
Mid-infrared spectral analysis of glucose in subcutaneous interstitial fluid has been widely employed as a noninvasive alternative to the standard blood-glucose detection requiring blood-sampling via skin-puncturing, but improving the confidence leve...
Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy.
Journal of chemical information and modeling
Jun 19, 2024
In this work, a new model with broad utility for quantitative spectroscopy development is reported. A primary objective of this work is to create a novel modeling procedure that may allow for higher automation of the model development process. The fu...
Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling.
Nature communications
Jun 13, 2024
Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression ...
Data-driven learning of structure augments quantitative prediction of biological responses.
PLoS computational biology
Jun 3, 2024
Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experimen...
Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.
Current hypertension reports
May 28, 2024
PURPOSE OF REVIEW: Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare predict...
A systematic analysis of regression models for protein engineering.
PLoS computational biology
May 3, 2024
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process...
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