AIMC Topic: Machine Learning

Clear Filters Showing 5801 to 5810 of 34417 articles

Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach.

PLoS neglected tropical diseases
BACKGROUND: Leptospirosis is a neglected zoonotic disease prevalent worldwide, particularly in tropical regions experiencing frequent rainfall and severe cyclones, which are further aggravated by climate change. This bacterial zoonosis, caused by the...

Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients.

PloS one
PURPOSE: Left atrial thrombus or spontaneous echo contrast (LAT/SEC) are widely recognized as significant contributors to cardiogenic embolism in non-valvular atrial fibrillation (NVAF). This study aimed to construct and validate an interpretable pre...

Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis.

PloS one
The health of poultry flock is crucial in sustainable farming. Recent advances in machine learning and speech analysis have opened up opportunities for real-time monitoring of the behavior and health of flock. However, there has been little research ...

Artificial intelligence in healthcare applications targeting cancer diagnosis-part II: interpreting the model outputs and spotlighting the performance metrics.

Oral surgery, oral medicine, oral pathology and oral radiology
BACKGROUND: The lack of standardized performance assessment metrics and the inconsistent reporting of results can lead to the presentation of overly optimistic outcomes that fail to accurately represent key aspects of the Machine Learning framework a...

A machine learning-based analysis for the effectiveness of online teaching and learning in Pakistan during COVID-19 lockdown.

Work (Reading, Mass.)
BackgroundThe COVID-19 pandemic has significantly disrupted daily life and education, prompting institutions to adopt online teaching.ObjectiveThis study delves into the effectiveness of these methods during the lockdown in Pakistan, employing machin...

Unveiling the molecular mechanisms of Haitang-Xiaoyin Mixture in psoriasis treatment based on bioinformatics, network pharmacology, machine learning, and molecular docking verification.

Computational biology and chemistry
OBJECTIVE: Psoriasis is a common clinical skin inflammatory disease. Haitang-Xiaoyin Mixture (HXM) represents a traditional Chinese medicine formulation utilized clinically for the management of psoriasis, which can reduce the psoriasis area and seve...

UnICLAM: Contrastive representation learning with adversarial masking for unified and interpretable Medical Vision Question Answering.

Medical image analysis
Medical Visual Question Answering aims to assist doctors in decision-making when answering clinical questions regarding radiology images. Nevertheless, current models learn cross-modal representations through residing vision and text encoders in dual...

Identifying semaphorin 3C as a biomarker for sarcopenia and coronary artery disease via bioinformatics and machine learning.

Archives of gerontology and geriatrics
OBJECTIVE: Sarcopenia not only affects patients' quality of life but also may exacerbate the pathological processes of coronary artery disease (CAD). This study aimed to identify potential biomarkers to improve the combined diagnosis and treatment of...

Novel Machine-Learning Modeling of Facial Trauma Volume With Regional Event and Weather Data.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Facial trauma volume is difficult to predict accurately. We aim to understand the capacity of climate and regional events to predict daily facial trauma volume. This can provide epidemiologic understanding and subsequently tailor workforce...

Predicting the risk of a high proportion of three/multiple pronuclei (3PN/MPN) zygotes in individual IVF cycles using comparative machine learning algorithms.

European journal of obstetrics, gynecology, and reproductive biology
BACKGROUND: The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting...