AIMC Topic: Machine Learning

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Enhancing automatic sleep stage classification with cerebellar EEG and machine learning techniques.

Computers in biology and medicine
Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as the primary research method. Conventional polysomnography primarily relies on cerebral electroencep...

MRI classification of progressive supranuclear palsy, Parkinson disease and controls using deep learning and machine learning algorithms for the identification of regions and tracts of interest as potential biomarkers.

Computers in biology and medicine
BACKGROUND: Quantitative magnetic resonance imaging (MRI) analysis has shown promise in differentiating neurodegenerative Parkinsonian syndromes and has significantly advanced our understanding of diseases like progressive supranuclear palsy (PSP) in...

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles.

Chemosphere
As nanotechnology advances, metal oxide nanoparticles (MeONPs) increasingly come into contact with humans. The inhaled MeONPs cannot be effectively cleared by cilia or lung mucus. In the last decade, potential immune toxicity arising from exposure to...

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

mSystems
UNLABELLED: Bacterial vaginosis (BV) is an abnormal gynecological condition caused by the overgrowth of specific bacteria in the vagina. This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS...

Utilizing deep learning-based causal inference to explore vancomycin's impact on continuous kidney replacement therapy necessity in blood culture-positive intensive care unit patients.

Microbiology spectrum
Patients with positive blood cultures in the intensive care unit (ICU) are at high risk for septic acute kidney injury requiring continuous kidney replacement therapy (CKRT), especially when treated with vancomycin. This study developed a machine lea...

Spectrum of errors in nodule detection and characterization using machine learning: A pictorial essay.

Current problems in diagnostic radiology
In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms ar...

Unveiling the effects of artificial intelligence and green technology convergence on carbon emissions: An explainable machine learning-based approach.

Journal of environmental management
Green technology and artificial intelligence (AI) are playing a positive role in reducing carbon emissions. Technology convergence, as a typical form of technological innovation, can expedite the realization of low-carbon goals through the outcomes o...

Artificial intelligence terminology, methodology, and critical appraisal: A primer for headache clinicians and researchers.

Headache
OBJECTIVE: The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicians and researchers in the headache field to facilitate interdisciplinary communications and research...

Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Journal of chemical information and modeling
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, r...

Influence of Data Curation and Confidence Levels on Compound Predictions Using Machine Learning Models.

Journal of chemical information and modeling
While data curation principles and practices are a major topic in data science, they are often not explicitly considered in machine learning (ML) applications in chemistry. We have been interested in evaluating the potential effects of data curation ...