AIMC Topic: Algorithms

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Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.

Scientific reports
Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective study at Emtiaz Hospital analyzed 3,180 traumatic brain injury (TBI) patients. Nineteen variables were...

Enhancing diabetic retinopathy diagnosis: automatic segmentation of hyperreflective foci in OCT via deep learning.

International ophthalmology
OBJECTIVE: Hyperreflective foci (HRF) are small, punctate lesions ranging from 20 to 50 m and exhibiting high reflective intensity in optical coherence tomography (OCT) images of patients with diabetic retinopathy (DR). The purpose of the model prop...

Identifying key characteristics of developed artificial intelligence algorithms to achieve meaningful impact on Canadian healthcare: a scoping review protocol.

BMJ open
INTRODUCTION: Empirical data on the barriers limiting artificial intelligence (AI)'s impact on healthcare are scarce, particularly within the Canadian context. This study aims to address this gap by conducting a scoping review to identify and evaluat...

Predicting diabetes self-management education engagement: machine learning algorithms and models.

BMJ open diabetes research & care
INTRODUCTION: Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes management. However, the utilization of DSME remains limited in the USA. This study aimed to investig...

Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms.

Environmental monitoring and assessment
Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing...

An integrated approach for advanced vehicle classification.

PloS one
This study is dedicated to addressing the trade-off between receptive field size and computational efficiency in low-level vision. Conventional neural networks (CNNs) usually expand the receptive field by adding layers or inflation filtering, which o...

Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms.

PloS one
BACKGROUND: Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. The...

Multi-agent deep reinforcement learning-based robotic arm assembly research.

PloS one
Due to the complexity and variability of application scenarios and the increasing demands for assembly, single-agent algorithms often face challenges in convergence and exhibit poor performance in robotic arm assembly processes. To address these issu...

Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.

PloS one
OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the intensive care unit (ICU).

Unsupervised neural network-based image stitching method for bladder endoscopy.

PloS one
Bladder endoscopy enables the observation of intravesical lesion characteristics, making it an essential tool in urology. Image stitching techniques are commonly employed to expand the field of view of bladder endoscopy. Traditional image stitching m...