AIMC Topic: Humans

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Enhancing diabetes risk prediction through focal active learning and machine learning models.

PloS one
To improve the effectiveness of diabetes risk prediction, this study proposes a novel method based on focal active learning strategies combined with machine learning models. Existing machine learning models often suffer from poor performance on imbal...

Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.

PloS one
PURPOSE: Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As show...

Predicting errors in accident hotspots and investigating satiotemporal, weather, and behavioral factors using interpretable machine learning: An analysis of telematics big data.

PloS one
BACKGROUND: Road traffic accidents (RTAs) are a major public health concern with significant health and economic burdens. Identifying high-risk areas and key contributing factors is essential for developing targeted interventions. While machine learn...

Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network.

PloS one
Accurate risk assessment in international trade settlement has become increasingly critical as global financial transactions grow in scale and complexity. This study proposes a hybrid model-Genetic Algorithm-optimized Fuzzy Neural Network (GA-FNN)-to...

Rapid forensic differentiation of human and animal bones using handheld near-infrared spectroscopy and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The forensic differentiation of human and animal bones is critical in various investigations, mainly when dealing with fragmented skeletal remains. This study explores the practical application of handheld near-infrared spectroscopy combined with art...

A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.

Renal failure
Artificial intelligence (AI) and machine learning (ML) are transforming nephrology by enhancing diagnosis, risk prediction, and treatment optimization for conditions such as acute kidney injury (AKI) and chronic kidney disease (CKD). AI-driven models...

Groundwater health probability risk prediction through oral intake using advanced optimization methods.

Journal of contaminant hydrology
Examining the cancer risk associated with oral groundwater (GW) intake is crucial, particularly in regions heavily reliant on GW for human consumption and agriculture. The study was based on real field investigations and controlled laboratory experim...

Artificial intelligence in nutrition and ageing research - A primer on the benefits.

Maturitas
Artificial intelligence (AI) is increasingly impacting multiple domains. The application of AI in nutrition and ageing research has significant potential to transform healthcare outcomes for the ageing population. This review provides critical insigh...

Cellular senescence in renal ischemia-reperfusion injury.

Chinese medical journal
Acute kidney injury (AKI) affects more than 20% of hospitalized patients and is a significant contributor to morbidity and mortality, primarily due to ischemia-reperfusion injury (IRI), which is one of the leading causes of AKI. IRI not only exacerba...

Beyond the human touch: A critical review of the promise and challenges of animal- and robot-assisted therapy in loneliness and mental healthcare.

Asian journal of psychiatry
Mental health disorders, including depression, anxiety, post-traumatic stress disorder, and dementia, are increasingly recognized as exacerbated by social isolation and loneliness, prompting growing interest in Artificial Intelligence (AI) driven and...