AIMC Topic: Machine Learning

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Prediction of Postoperative Venous Thromboembolism in Patients With Traumatic Brain Injury: Model Development and Validation Study.

JMIR medical informatics
BACKGROUND: Venous thromboembolism (VTE) remains a critical cause of mortality among patients who are hospitalized. Patients with traumatic brain injury (TBI) are particularly susceptible to VTE due to coagulation abnormalities and immobilization. De...

Machine learning-driven geochemical fingerprinting and risk characterization of mineral dust across different operational settings in El-Gedida Iron Mine, Egypt.

Environmental geochemistry and health
Investigating mineral dust emitted from mining activities enables the assessment of environmental risks posed by potentially toxic elements (PTEs) and the discrimination of geochemical fingerprints characteristic of distinct operational settings. Acc...

Machine learning-based prediction model for omental metastasis in right-sided colon cancer patients: a retrospective multicenter study.

International journal of colorectal disease
PURPOSE: Current diagnostic modalities lack sufficient sensitivity for detecting omental metastasis (OM), often underestimating metastatic burden. Unlike traditional statistical model, machine learning (ML) model is designed to detect subtle variable...

Zero-shot image classification based on class representation learning and attribute embedding learning.

PloS one
Zero-shot learning (ZSL) aims to classify unseen classes by leveraging semantic information from seen classes, addressing the challenge of limited labeled data. In recent years, ZSL methods have focused on extracting attribute-level features from ima...

Classifying complex multimorbidity using latent class analysis and machine learning to generate insights into clustering of mental and cardiometabolic conditions.

PloS one
Machine learning techniques earn higher accuracy and robustness in multimorbidity prediction at this moment in time. Among various forms of multimorbidity, complex multimorbidity, especially the intersection of cardiometabolic disorders and mental he...

Non-destructive defect detection in powder metallurgy automotive oil pump stators using acoustic signals and machine learning classification.

PloS one
Defects such as cracks and mass reduction frequently occur during the production of powder metallurgy (PM) automotive oil pump stators, making rigorous inspection essential for reliable operation. Conventional human visual inspection is threshold-bas...

Fast machine learning image reconstruction of radially undersampled k-space data for low-latency real-time MRI.

PloS one
Fast data acquisition and fast image reconstruction are essential to enable low-latency real-time magnetic resonance (MR) imaging applications with high temporal resolution such as interstitial percutaneous needle interventions or MR-guided radiother...

Machine learning recovers corrupted pharmaceutical 3D printing formulation data.

International journal of pharmaceutics
Pharmaceutical 3D printing is an emerging digital manufacturing technology capable of autonomously producing personalised medicines. However, the same reliance on digital workflows that enables this innovation also introduces new vulnerabilities, mos...

A rapid wine brand identification method based on the joint application of SERS and machine learning techniques.

Food chemistry
In this paper, an innovative approach is proposed to achieve no-preparation, rapid, and accurate identification of red wine brands by combining Surface-Enhanced Raman Scattering (SERS) spectroscopy with machine learning. SERS detects trace molecular ...

Machine Learning Identifies FLNA as a Key Molecular Target Regulating Neuronal Apoptosis after Spinal Cord Injury.

Journal of molecular neuroscience : MN
Spinal cord injury (SCI), a traumatic type of central nervous system injury, is closely associated with neuronal apoptosis. However, the specific biomarkers and regulatory mechanisms of neuronal apoptosis in SCI patients remain unclear. In this study...