AIMC Topic: Models, Statistical

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Mitigating biases in feature selection and importance assessments in predictive models using LASSO regression.

Oral oncology
Yuan et al. developed a predictive model for early response using sub-regional radiomic features from multi-sequence MRI alongside clinical factors. However, biases in feature selection and assessment may lead to misleading conclusions regarding feat...

An Efficient Muscle Segmentation Method via Bayesian Fusion of Probabilistic Shape Modeling and Deep Edge Detection.

IEEE transactions on bio-medical engineering
OBJECTIVE: Paraspinal muscle segmentation and reconstruction from MR images are critical to implement quantitative assessment of chronic and recurrent low back pains. Due to unclear muscle boundaries and shape variations, current segmentation methods...

Fixing imbalanced binary classification: An asymmetric Bayesian learning approach.

PloS one
Most statistical and machine learning models used for binary data modeling and classification assume that the data are balanced. However, this assumption can lead to poor predictive performance and bias in parameter estimation when there is an imbala...

Deep Neural Network-Based Accelerated Failure Time Models Using Rank Loss.

Statistics in medicine
An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure tim...

Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis.

Medical & biological engineering & computing
PURPOSE: In silico trials using computational modeling and simulations can complement clinical trials to improve the time-to-market of complex cardiovascular devices in humans. This study aims to investigate the significance of synthetic data in deve...

From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.

Journal of computational biology : a journal of computational molecular cell biology
Understanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Original methods for GRN inference from bulk expression data often struggled with the high dimensionality and inhere...

Machine learning and statistical models to predict all-cause mortality in type 2 diabetes: Results from the UK Biobank study.

Diabetes & metabolic syndrome
AIMS: This study aims to compare the performance of contemporary machine learning models with statistical models in predicting all-cause mortality in patients with type 2 diabetes mellitus and to develop a user-friendly mortality risk prediction tool...

Uncertainty-aware probabilistic graph neural networks for road-level traffic crash prediction.

Accident; analysis and prevention
Traffic crashes present substantial challenges to human safety and socio-economic development in urban areas. Developing a reliable and responsible traffic crash prediction model is crucial to address growing public safety concerns and improve the sa...

Applying support vector machines to a diagnostic classification model for polytomous attributes in small-sample contexts.

The British journal of mathematical and statistical psychology
Over several years, the evaluation of polytomous attributes in small-sample settings has posed a challenge to the application of cognitive diagnosis models. To enhance classification precision, the support vector machine (SVM) was introduced for esti...

Weighted Expectile Regression Neural Networks for Right Censored Data.

Statistics in medicine
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 ...