AIMC Topic: Machine Learning

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Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic rev...

Social determinants of health and disparities in spine surgery: a 10-year analysis of 8,565 cases using ensemble machine learning and multilayer perceptron.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: The influence of SDOH on spine surgery is poorly understood. Historically, researchers commonly focused on the isolated influences of race, insurance status, or income on healthcare outcomes. However, analysis of SDOH is becoming ...

Comparative evaluating laser ionization and iKnife coupled with rapid evaporative ionization mass spectrometry and machine learning for geographical authentication of Larimichthys crocea.

Food chemistry
Larimichthys crocea (LYC) holds significant economic value as a marine fish species. However, inaccuracies in labeling its origin can adversely affect consumer interests. Herein, a laser assisted rapid evaporative ionization mass spectrometry (LA-REI...

A Multi-Group Multi-Stream attribute Attention network for fine-grained zero-shot learning.

Neural networks : the official journal of the International Neural Network Society
Fine-grained visual categorization in zero-shot setting is a challenging problem in the computer vision community. It requires algorithms to accurately identify fine-grained categories that do not appear during the training phase and have high visual...

Prediction of pollutant emission characteristics in ISO50001 energy management in the Americas: Uni and multivariate machine learning approach.

The Science of the total environment
The American continent is experiencing significant economic and industrial development driven by sustainability principles. In this context, discussions on improving energy consumption have become increasingly frequent and dynamic across various sect...

MoCab: A framework for the deployment of machine learning models across health information systems.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and divers...

Interpretable medical image Visual Question Answering via multi-modal relationship graph learning.

Medical image analysis
Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficienc...

Machine learning and experimental analyses identified miRNA expression models associated with metastatic osteosarcoma.

Biochimica et biophysica acta. Molecular basis of disease
Osteosarcoma (OS), as the most common primary bone cancer, has a high invasiveness and metastatic potential, therefore, it has a poor prognosis. This study identified early diagnostic biomarkers using miRNA expression profiles associated with osteosa...

Artificial Intelligence and Pediatric Otolaryngology.

Otolaryngologic clinics of North America
Artificial intelligence (AI) studies show how to program computers to simulate human intelligence and perform data interpretation, learning, and adaptive decision-making. Within pediatric otolaryngology, there is a growing body of evidence for the ro...

Enhancing electrochemical detection through machine learning-driven prediction for canine mammary tumor biomarker with green silver nanoparticles.

Analytical and bioanalytical chemistry
This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15-3 (CA 15-3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learni...