AIMC Topic: Regression Analysis
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Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning.
Frontiers in immunology
Nov 1, 2024
INTRODUCTION: The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed to explore the predictive value of a novel model for assessing the risk of death in this vulnerable...
Machine learning assisted rapid approach for quantitative prediction of biochemical parameters of blood serum with FTIR spectroscopy.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Oct 12, 2024
This study develops regression models for predicting blood biochemical data using Fourier-transform infrared spectroscopy (FTIR) analysis. Absorption at specific wavelengths of blood serum is revealed to have strong correlations with biochemical para...
Weighted Expectile Regression Neural Networks for Right Censored Data.
Statistics in medicine
Sep 29, 2024
As a favorable alternative to the censored quantile regression, censored expectile regression has been popular in survival analysis due to its flexibility in modeling the heterogeneous effect of covariates. The existing weighted expectile regression ...
Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.
Traffic injury prevention
Sep 26, 2024
OBJECTIVES: The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe...
Progression to refractory status epilepticus: A machine learning analysis by means of classification and regression tree analysis.
Epilepsy & behavior : E&B
Sep 21, 2024
BACKGROUND AND OBJECTIVES: to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique.
Prediction of Fatty Acid Intake from Serum Fatty Acid Levels Using Machine Learning Technique in Women Living in Toyama Prefecture.
Journal of oleo science
Sep 20, 2024
Preventing lifestyle-related diseases requires understanding and managing the intake of total fats and specific types of fatty acids, especially trans fatty acids. There are several methods for measuring fat intake, each with its own strengths and li...
A hybrid approach for modeling bicycle crash frequencies: Integrating random forest based SHAP model with random parameter negative binomial regression model.
Accident; analysis and prevention
Sep 16, 2024
To effectively capture and explain complex, nonlinear relationships within bicycle crash frequency data and account for unobserved heterogeneity simultaneously, this study proposes a new hybrid framework that combines the Random Forest-based SHapley ...
Prediction model for lateral lymph node metastasis of papillary thyroid carcinoma in children and adolescents based on ultrasound imaging and clinical features: a retrospective study.
BMC medical imaging
Aug 29, 2024
BACKGROUND: The presence of lateral lymph node metastases (LNM) in paediatric patients with papillary thyroid cancer (PTC) is an independent risk factor for recurrence. We aimed to identify risk factors and establish a prediction model for lateral LN...
Thyroid Cancer, Papillary
Predictive Learning Models
Regression Analysis
Predictive Value of Tests
Area Under Curve
Reference Values
ROC Curve
Lymph Nodes
Humans
Lymphatic Metastasis
Retrospective Studies
Risk Assessment
Female
Adolescent
Risk Factors
Thyroid Neoplasms
Young Adult
Ultrasonography
Male
Convolutional neural network-based regression analysis to predict subnuclear chromatin organization from two-dimensional optical scattering signals.
Journal of biomedical optics
Aug 28, 2024
SIGNIFICANCE: Azimuth-resolved optical scattering signals obtained from cell nuclei are sensitive to changes in their internal refractive index profile. These two-dimensional signals can therefore offer significant insights into chromatin organizatio...
Bayesian learning of feature spaces for multitask regression.
Neural networks : the official journal of the International Neural Network Society
Aug 13, 2024
This paper introduces a novel approach to learn multi-task regression models with constrained architecture complexity. The proposed model, named RFF-BLR, consists of a randomised feedforward neural network with two fundamental characteristics: a sing...
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