AIMC Topic: Machine Learning

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Big Data, Machine Learning, and Personalization in Health Systems: Ethical Issues and Emerging Trade-Offs.

Science and engineering ethics
The use of big data and machine learning has been discussed in an expanding literature, detailing concerns on ethical issues and societal implications. In this paper we focus on big data and machine learning in the context of health systems and with ...

Machine learning insights into obesity related genes XRCC4 and ARL6 in obstructive sleep apnea.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Obstructive sleep apnea (OSA) is highly prevalent among obese individuals, with a complex and bidirectional relationship wherein obesity not only serves as a primary risk factor for OSA but also exacerbates its severity. This interconnection...

Assessing future hydrological and sediment transport response of an urban watershed using a machine learning-based land cover change model.

Environmental monitoring and assessment
Assessing the impacts of land cover change (LCC) on hydrology and sediment load is essential for the sustainable management of urban watersheds. Modeling LCC using machine learning techniques enhances the ability to generate realistic future scenario...

Prioritizing geochemical drivers of groundwater quality and health risks in coastal aquifers of Bangladesh using machine learning algorithms.

Environmental geochemistry and health
This study aims to evaluate key parameters of groundwater quality and associated health risks in three coastal aquifers of Cox's Bazar, Bangladesh, with a focus on manganese contamination and geochemical processes. A total of 288 groundwater samples ...

Prediction of Personalised Hypertension Using Machine Learning in Indonesian Population.

Journal of medical systems
This study aims to enhance individual hypertension risk prediction in Indonesia using machine learning (ML) models. The research investigates the predictive accuracy of models with and without incorporating personal hypertension history, seeking to u...

Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.

Journal of the Egyptian National Cancer Institute
BACKGROUND: Computed tomography imaging, a non-invasive tool, is used around the globe by medical professionals to identify and diagnose lung cancer; a lethal disease with high rates of occurrence and mortality globally. Radiomics extracted from medi...

Integrated multi-omics and machine learning approach reveals the mechanism of nicotinamide alleviating PFOS-induced hepatotoxicity.

Food & function
: Perfluorooctane sulphonate (PFOS) is a persistent environmental contaminant with well-documented hepatotoxic properties. Nicotinamide, the amide derivative of vitamin B3, is widely utilized as a nutritional supplement and exerts multiple biological...

Machine learning framework for forecasting air pollution: Evaluating seasonal and climatic influences in Istanbul, Turkey.

PloS one
Air pollution, driven by seasonal and meteorological variations, poses a significant threat to public health and urban sustainability. Despite numerous forecasting approaches, the influence of seasonal patterns on air pollutant levels remains underex...

Non-invasive assessment techniques for renal fibrosis: advances and perspectives.

Renal failure
Renal fibrosis is a critical pathological process driving chronic kidney disease (CKD) and end-stage renal disease (ESRD). Early diagnosis is essential for timely intervention, yet traditional methods like renal biopsy are invasive and present signif...

Drivers of herpes zoster vaccine hesitancy in adults aged 50 and above: A machine learning approach.

Vaccine
BACKGROUND: Herpes zoster (HZ) poses a growing public health challenge among adults aged 50 and above, with vaccine hesitancy being a major barrier to improving immunization rates. Understanding the factors driving HZ vaccine hesitancy is essential f...