AIMC Topic: Machine Learning

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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...

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...

Autonomous screening of infants at high risk for neurodevelopmental impairments using a radar sensor and machine learning.

Scientific reports
Neurodevelopmental impairments (NDIs) are significant long-term complications in preterm infants. While early recognition of infants at high risk for NDIs is essential for enabling timely intervention, it remains a challenging endeavor. Autonomous sc...

Machine learning identifies KRT8 dysregulation and endothelial remodeling in Moyamoya disease.

Scientific reports
Moyamoya disease (MMD) is a rare occlusive cerebrovascular disease, and its pathological mechanism remains unclear at present. The abnormal vascular remodeling may be involved in vascular endothelial cells. In this study, RNA seq was performed on the...

Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning.

Scientific reports
Spinal cord injury (SCI) is a severe neurological disorder, with glucocorticoids like methylprednisolone commonly used for treatment. However, their efficacy and risks remain controversial. Programmed cell death (PCD) mechanisms have been increasingl...

Fused federated learning framework for secure and decentralized patient monitoring in healthcare 5.0 using IoMT.

Scientific reports
Federated Learning (FL) enables artificial intelligence frameworks to train on private information without compromising privacy, which is especially useful in the medical and healthcare industries where the knowledge or data at hand is never enough. ...

Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data.

Scientific reports
Neonatal mortality poses a critical challenge in global health, particularly in low- and middle-income countries. Leveraging advancements in technology, such as machine learning (ML) algorithms, offers the potential to improve neonatal care by enabli...

Data-driven synthetic microbes for sustainable future.

NPJ systems biology and applications
The escalating global environmental crisis demands transformative biotechnological solutions that are both sustainable and scalable. This perspective advocates Data-Driven Synthetic Microbes (DDSM); engineered microorganisms designed through integrat...

Nuclear morphometrics coupled with machine learning identifies dynamic states of senescence across age.

Nature communications
Cellular senescence is an irreversible state of cell cycle arrest with a complex role in tissue repair, aging, and disease. However, inconsistencies in identifying cellular senescence have led to varying conclusions about their functional significanc...