AIMC Topic: Machine Learning

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Heterogeneity in the association between internet use and dementia among older adults: A machine-learning analysis.

Archives of gerontology and geriatrics
BACKGROUND & AIMS: Internet use among older adults may reduce the risk of dementia, but it remains unknown how the effects vary across individuals. The aim of this study was to rigorously examine heterogeneity in the association between internet use ...

Quantitative computed tomography imaging classification of cement dust-exposed patients-based Kolmogorov-Arnold networks.

Artificial intelligence in medicine
BACKGROUND: Occupational health assessment is critical for detecting respiratory issues caused by harmful exposures, such as cement dust. Quantitative computed tomography (QCT) imaging provides detailed insights into lung structure and function, enha...

Analyzing the impact of occupational exposures on male fertility indicators: A machine learning approach.

Reproductive toxicology (Elmsford, N.Y.)
Occupational exposures are critical factors affecting workers' reproductive health. This study investigates the impact of magnetic fields, electric fields, whole-body vibration, noise levels, and heat stress on male reproductive indicators using adva...

DNA forensics at forty: the way forward.

International journal of legal medicine
Forensic DNA analysis has transformed criminal investigations since its inception in 1985. Over four decades, this field has evolved through various phases-from the early stages of exploration to today's highly sophisticated methodologies. Key advanc...

Symbolic and hybrid AI for brain tissue segmentation using spatial model checking.

Artificial intelligence in medicine
Segmentation of 3D medical images, and brain segmentation in particular, is an important topic in neuroimaging and in radiotherapy. Overcoming the current, time consuming, practise of manual delineation of brain tumours and providing an accurate, exp...

Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices.

Neural networks : the official journal of the International Neural Network Society
Drug-drug interactions (DDIs) are critical in pharmaceutical research, as adverse interactions can pose significant risks for patient treatment plans. Accurate prediction of DDI events risk levels can provide valuable guidance for designing safer and...

Machine learning-guided prediction of chlorinated/chloraminated disinfection by-product formation in drinking water treatment.

Water research
Chlorination and chloramination as common water disinfection methods are challenged by the unintended formations of hazardous disinfection by-products (DBPs). Accurately predicting DBP formation is essential for improving water treatment processes an...

Developing a quantitative structure-property relationships (QSPR) model using Caco-2 cell bioavailability indicators (BA) to predict the BA of phytochemicals.

Journal of the science of food and agriculture
BACKGROUND: The present study aimed to measure bioavailability (BA) indicators, including epithelial barrier function, apparent permeability (P) and efflux ratio, of 84 types of phytochemicals using Caco-2 cell and to develop predictive model systems...

Hepatitis B In Silico Trials Capture Functional Cure, Indicate Mechanistic Pathways, and Suggest Prognostic Biomarker Signatures.

Clinical pharmacology and therapeutics
In silico trials, utilizing mathematical models calibrated with clinical data, present a transformative approach to expedite drug development. We propose a virtual trial framework for chronic Hepatitis B, accurately simulating clinical protocols, pat...