AIMC Topic: Machine Learning

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Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States.

Diabetes, obesity & metabolism
AIMS: Numerous risk factors for the development of obesity have been identified, yet the aetiology is not well understood. Traditional statistical methods for analysing observational data are limited by the volume and characteristics of large dataset...

Evolving biomaterials design from trial and error to intelligent innovation.

Acta biomaterialia
The design and exploration of biomaterials plays a pivotal role in many fields, including medical and engineering. The prevailing approach to biomaterials discovery relies on orthogonal experiments, with repeated attempts to optimize experimental con...

Using Machine Learning Models to Diagnose Chronic Rhinosinusitis: Analysis of Pre-Treatment Patient-Generated Health Data to Predict Cardinal Symptoms and Sinonasal Inflammation.

American journal of rhinology & allergy
BackgroundThe diagnosis of chronic rhinosinusitis (CRS) relies upon patient-reported symptoms and objective nasal endoscopy and/or computed tomography (CT) findings. Many patients, at the time of evaluation by an otolaryngologist or rhinologist, lack...

Leveraging machine learning for drug repurposing in rheumatoid arthritis.

Drug discovery today
Rheumatoid arthritis (RA) presents a significant challenge in clinical management because of the dearth of effective drugs despite advances in understanding its mechanisms. Drug repurposing has emerged as a promising strategy to address this gap, off...

Artificial Intelligence in Gas Sensing: A Review.

ACS sensors
The role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in enhancing and automating gas sensing methods and the implications of these technologies for emergent gas sensor systems is reviewed. Applications of AI-based i...

MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug-Drug Interaction Prediction.

Journal of chemical information and modeling
Drug-drug interactions influence drug efficacy and patient prognosis, providing substantial research value. Some existing methods struggle with the challenges posed by sparse networks or lack the capability to integrate data from multiple sources. In...

Evaluation of factors associated with adult skeletal fluorosis in coal-burning type of endemic fluorosis and initial screening model based on machine learning in Guizhou, Southwest China.

Ecotoxicology and environmental safety
Skeletal fluorosis caused by coal-burning type endemic fluorosis greatly affects the health of the population in the affected areas, but large-scale diagnostic work is limited by human and material resources, making it difficult to implement comprehe...

A Novel Explainable Attention-Based Meta-Learning Framework for Imbalanced Brain Stroke Prediction.

Sensors (Basel, Switzerland)
The accurate prediction of brain stroke is critical for effective diagnosis and management, yet the imbalanced nature of medical datasets often hampers the performance of conventional machine learning models. To address this challenge, we propose a n...

PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach.

BMC research notes
OBJECTIVES: Osteoporosis, prevalent among the elderly population, is primarily diagnosed through bone mineral density (BMD) testing, which has limitations in early detection. This study aims to develop and validate a machine learning approach for ost...

Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?

BMC medical informatics and decision making
OBJECTIVE: This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without osteoporosis. Various machine learning algorithms were employed for training and testing the model, ...