AIMC Topic: Machine Learning

Clear Filters Showing 11471 to 11480 of 34417 articles

Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.

Journal of neurotrauma
Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinatio...

Machine learning model for predicting stroke recurrence in adult stroke patients with moyamoya disease and factors of stroke recurrence.

Clinical neurology and neurosurgery
OBJECT: The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence.

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review.

Computer methods and programs in biomedicine
The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic U...

Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA.

ACS sensors
Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting it in its early stages. However, despite much research and investment, cancer early diagnosis is still underdeveloped. Owing to its high sensitivity, ...

Machine Learning-Based Prediction of Reduction Potentials for Pt Complexes.

Journal of chemical information and modeling
Some of the well-known drawbacks of clinically approved Pt complexes can be overcome using six-coordinate Pt complexes as inert prodrugs, which release the corresponding four-coordinate active Pt species upon reduction by cellular reducing agents. Th...

Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning.

The journal of physical chemistry. A
Solvatochromism occurs in both homogeneous solvents and more complex biological environments, such as proteins. While in both cases the solvatochromic effects report on the surroundings of the chromophore, their interpretation in proteins becomes mor...

Improved hypertensive stroke classification based on multi-scale feature fusion of head axial CT angiogram and multimodal learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Strokes are severe cardiovascular and circulatory diseases with two main types: ischemic and hemorrhagic. Clinically, brain images such as computed tomography (CT) and computed tomography angiography (CTA) are widely used to recognize stroke...

A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models.

Environmental science and pollution research international
The research aims to propose a feature selection model for hydraulic analysis as such a model has not been proposed previously. For this purpose, hybrids of three metaheuristic algorithms, particle swarm optimization (PSO), ant colony optimization (A...

Bidirectional machine learning-assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization.

Environmental science and pollution research international
In this study, we designed a machine learning-based parallel global searching method using the Bayesian inversion framework for efficient identification of dense non-aqueous phase liquid (DNAPL) source characteristics and contaminant transport parame...

Early prediction of sepsis-induced respiratory tract infection using a biomarker-based machine-learning algorithm.

Scandinavian journal of clinical and laboratory investigation
Early and differential diagnosis of sepsis is essential to avoid unnecessary antibiotic use and further reduce patient morbidity and mortality. Here, we aimed to identify predictors of sepsis and advance a machine-learning strategy to predict sepsis-...