AIMC Topic: Machine Learning

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Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models.

BMC musculoskeletal disorders
BACKGROUND: Identifying determinants of low bone mineral density (BMD) is crucial for understanding the underlying pathobiology and developing effective prevention and management strategies. Here we applied machine learning (ML) algorithms to predict...

Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.

Clinical and experimental medicine
Investigating the essential function of CD300LG within the tumor microenvironment in triple-negative breast cancer (TNBC). Transcriptomic and single-cell data from TNBC were systematically collected and integrated. Four machine learning algorithms we...

Hidden threats beneath: uncovering the bio-accessible hazards of chromite-asbestos mine waste and their impacts on rice components via multi-machine learning algorithm.

Environmental geochemistry and health
The chromite-asbestos mining leaves behind tonnes of toxic waste, contaminating nearby agricultural fields with potentially toxic elements (PTEs). Over time, wind and water erosion spread these pollutants, severely impacting the ecosystem, food chain...

AdapTree: Data-Driven Approach to Assessing Plant Stress Through the AI-Sensor Synergy.

Sensors (Basel, Switzerland)
This study investigates plant stress assessment by integrating advanced sensor technologies and Artificial Intelligence (AI). Multi-sensor data-including electrical impedance spectroscopy, temperature, and humidity-were used to capture plant physiolo...

MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning.

Scientific reports
This paper facilitates proactive health management, advanced patient care, and early identification of possible health hazards by using MyWear. It is a wearable T-shirt that continuously monitors and predicts physiological parameters such as stress a...

Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain.

Scientific reports
Pain is common in middle-aged and older adults, has also been identified as a fall risk factor, whereas the mechanism of falls in pain is unclear. This study included 13,074 middle-aged and older adults from the China health and retirement longitudin...

Metabolomics and machine learning identify urine metabolic characteristics and potential biomarkers for severe Mycoplasma pneumoniae pneumonia.

Scientific reports
To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarker...

Predicting hospital outpatient volume using XGBoost: a machine learning approach.

Scientific reports
Hospital outpatient volume is influenced by a variety of factors, including environmental conditions and healthcare resource availability. Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allo...

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.

BMJ open
OBJECTIVES: In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques t...

Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity.

Psychological medicine
BACKGROUND: Internet addiction (IA) refers to excessive internet use that causes cognitive impairment or distress. Understanding the neurophysiological mechanisms underpinning IA is crucial for enabling an accurate diagnosis and informing treatment a...