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Regression Analysis

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A Regression Framework for Predicting Cognitive Decline in Frontotemporal Dementia using Recurrent Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Frontotemporal dementia (FTD) is a progressive neurodegenerative disorder with a diverse range of symptoms, including personality changes, behavioral disturbances, language deficits, and impaired executive functions. FTD has three main subtypes: beha...

Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep learning models are being adopted and applied across various critical medical tasks, yet they are primarily trained to provide point predictions without providing degrees of confidence. Medical practitioner's trustworthiness of deep learning mod...

Predicting dry matter intake in cattle at scale using gradient boosting regression techniques and Gaussian process boosting regression with Shapley additive explanation explainable artificial intelligence, MLflow, and its containerization.

Journal of animal science
Dry matter intake (DMI) is a measure critical to managing and evaluating livestock. Methods exist for quantifying individual DMI in dry lot settings that employ expensive intake systems. No methods exist to accurately measure individual DMI of grazin...

On spectral bias reduction of multi-scale neural networks for regression problems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we derive diffusion equation models in the spectral domain to study the evolution of the training error of two-layer multiscale deep neural networks (MscaleDNN) (Cai and Xu, 2019; Liu et al., 2020), which is designed to reduce the spec...

Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.

Annals of surgical oncology
BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).

Reducing bias in source-free unsupervised domain adaptation for regression.

Neural networks : the official journal of the International Neural Network Society
Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to tr...

Nonlinear feature selection for support vector quantile regression.

Neural networks : the official journal of the International Neural Network Society
This paper discusses the nuanced domain of nonlinear feature selection in heterogeneous systems. To address this challenge, we present a sparsity-driven methodology, namely nonlinear feature selection for support vector quantile regression (NFS-SVQR)...

Classification and regression machine learning models for predicting mixed toxicity of carbamazepine and its transformation products.

Environmental research
Carbamazepine (CBZ) and its transformation products (TPs) often occur in aquatic environments in the form of mixtures, posing potential risks to ecosystems. However, establishing standardized protocols for synthesizing, isolating, and acquiring these...

Do Machine Learning Approaches Perform Better Than Regression Models in Mapping Studies? A Systematic Review.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: To identify how machine learning (ML) approaches were implemented in mapping studies and to determine the extent to which ML improved performance compared with regression models (RMs).

Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling.

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias
OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.