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Linear Models

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A pilot study using a machine-learning approach of morphological and hemodynamic parameters for predicting aneurysms enhancement.

International journal of computer assisted radiology and surgery
PURPOSE: The development of straightforward classification methods is needed to identify unstable aneurysms and rupture risk for clinical use. In this study, we aim to investigate the relative importance of geometrical, hemodynamic and clinical risk ...

Predicting host taxonomic information from viral genomes: A comparison of feature representations.

PLoS computational biology
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...

Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method.

Computer methods and programs in biomedicine
BACKGROUND: Intradialytic hypotension (IDH) is commonly occurred and links to higher mortality among patients undergoing hemodialysis (HD). Its early prediction and prevention will dramatically improve the quality of life. However, predicting the occ...

Regularized least squares locality preserving projections with applications to image recognition.

Neural networks : the official journal of the International Neural Network Society
Locality preserving projection (LPP), as a well-known technique for dimensionality reduction, is designed to preserve the local structure of the original samples which usually lie on a low-dimensional manifold in the real world. However, it suffers f...

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India.

PloS one
A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in ...

Generation of Scale-Invariant Sequential Activity in Linear Recurrent Networks.

Neural computation
Sequential neural activity has been observed in many parts of the brain and has been proposed as a neural mechanism for memory. The natural world expresses temporal relationships at a wide range of scales. Because we cannot know the relevant scales a...

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes.

Current rheumatology reports
PURPOSE OF REVIEW: The propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of arthritis patients through knowing, understanding, and having access to an interactive process allowin...

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Computational and mathematical methods in medicine
Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specif...

Artificial Intelligence Models Predict Operative Versus Nonoperative Management of Patients with Adult Spinal Deformity with 86% Accuracy.

World neurosurgery
OBJECTIVE: Patients with ASD show complex and highly variable disease. The decision to manage patients operatively is largely subjective and varies based on surgeon training and experience. We sought to develop models capable of accurately discrimina...