Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 3,631 to 3,640 of 168,679 articles

ToxAI_assistant: a web platform for a comprehensive study of the acute toxicity of xenobiotics following oral and intravenous administration in rats.

SAR and QSAR in environmental research
The existing QSAR approaches for mammalian acute toxicity have been limited in scope, often relying on small or narrowly focused datasets and on classification endpoints. In contrast, our work leverages a sufficiently large curated dataset (9843 rat ... read more 

Boosted neural network modeling of psychological and social factors of work affecting safety performance and job satisfaction in the process industry.

BMC psychology
Psychological and social factors of work were found to influence workers' safety performance and job satisfaction. This study aimed to assess the effects of psychological and social factors of work affecting safety performance and job satisfaction of... read more 

Wind speed and power forecasting using Bayesian optimized machine learning models in Gabal Al-Zayt, Egypt.

Scientific reports
Accurate wind speed and power forecasts are essential for applications involving renewable wind energy. Ten machine learning techniques, including single and ensemble models, are compared, and evaluated in this study over a range of time scales. The ... read more 

Short and long-term impact of intravitreal anti-VEGF therapy interruption in retinal vein occlusion during the COVID-19 pandemic: functional outcomes and AI-based fluid analysis of macular edema.

International journal of retina and vitreous
BACKGROUND: The aim of the study is to investigate the short- and long-term effects of delayed intravitreal anti-VEGF injections (IVI) for macular edema (ME) in retinal vein occlusion (RVO) patients during the first wave of the COVID-19 pandemic. read more 

VR-based gamma sensory stimulation: a pilot feasibility study.

Scientific reports
Alzheimer's disease (AD) presents a critical global health challenge, with current therapies offering limited efficacy and safety in halting disease progression. Gamma sensory stimulation (GSS) has emerged as a promising non-invasive neuromodulation ... read more 

Clinical correlates of errors in machine-learning diagnostic model of autism spectrum disorder: Impact of sample cohorts.

Autism : the international journal of research and practice
Machine-learning models can assist in diagnosing autism but have biases. We examines the correlates of misclassifications and how training data affect model generalizability. The Social Responsive Scale data were collected from two cohorts in Taiwan:... read more 

Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.

Scientific reports
Current evidence for predictive models of post-stroke depression (PSD) risk based on machine learning (ML) remains limited. The aim of this study is to develop a superior predictive model based on ML algorithms for PSD in the Chinese population. We r... read more 

Short-term rainfall prediction based on radar echo using an efficient spatio-temporal recurrent unit.

Scientific reports
Accurate short-term precipitation prediction is critical for agricultural production, transportation safety, and water resource management. In this paper, we propose an Efficient Spatio-Temporal Recurrent Unit (ESTRU) for short-term precipitation pre... read more 

A lightweight hybrid DL model for multi-class chest x-ray classification for pulmonary diseases.

Biomedical physics & engineering express
Pulmonary diseases have become one of the main reasons for people's health decline, impacting millions of people worldwide. Rapid advancement of deep learning has significantly impacted medical image analysis by improving diagnostic accuracy and effi... read more 

Partial feature reparameterization and shallow-level interaction for remote sensing object detection.

Scientific reports
Remote sensing object detection has recently emerged as one of the challenging topics in the field of deep learning applications due to the demand for both high detection performance and computational efficiency. To address these problems, this study... read more