AIMC Topic: Machine Learning

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Can we rely on machine learning algorithms as a trustworthy predictor for recurrence in high-grade glioma? A systematic review and meta-analysis.

Clinical neurology and neurosurgery
Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence from treatment-related changes, such as radionecrosis, is a major diagnostic challenge. Machine lear...

InterDIA: Interpretable prediction of drug-induced autoimmunity through ensemble machine learning approaches.

Toxicology
Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to its idiosyncratic nature, complex pathogenesis, and diverse clinical manifestations. To address these ...

ESG introduction mechanism of construction firms based on Bayesian network coupled with machine learning: Evidence from Zhengzhou.

Journal of environmental management
Chinese construction enterprises are at a pivotal point in their transition to sustainable development, with Environmental, Social, and Governance (ESG) emerging as a key driver. However, limited understanding of ESG mechanisms hampers effective mana...

Gait-to-Gait Emotional Human-Robot Interaction Utilizing Trajectories-Aware and Skeleton-Graph-Aware Spatial-Temporal Transformer.

Sensors (Basel, Switzerland)
The emotional response of robotics is crucial for promoting the socially intelligent level of human-robot interaction (HRI). The development of machine learning has extensively stimulated research on emotional recognition for robots. Our research foc...

Machine Learning-Based Spectral Analyses for Cultivar Identification.

Molecules (Basel, Switzerland)
is a plant species with high cultural and biological relevance. Besides being used as an ornamental plant species, has relevant biological properties. Due to hybridization, thousands of cultivars are known, and their accurate identification is mand...

HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.

BMC bioinformatics
BACKGROUND: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention ...

Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.

BMC public health
BACKGROUND: Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understandin...

Age group classification based on optical measurement of brain pulsation using machine learning.

Scientific reports
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monito...

A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness.

Scientific reports
Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the ef...

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Scientific reports
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but...