AIMC Topic: Machine Learning

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Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning.

Scientific reports
The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims t...

Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials.

Scientific reports
Membrane incompatibility poses significant health risks, including severe complications and potential fatality. Surface modification of membranes has emerged as a pivotal technology in the membrane industry, aiming to improve the hemocompatibility an...

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.

Scientific reports
Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. T...

A pediatric emergency prediction model using natural language process in the pediatric emergency department.

Scientific reports
This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021...

Dilated SE-DenseNet for brain tumor MRI classification.

Scientific reports
In the field of medical imaging, particularly MRI-based brain tumor classification, we propose an advanced convolutional neural network (CNN) leveraging the DenseNet-121 architecture, enhanced with dilated convolutional layers and Squeeze-and-Excitat...

The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets.

JMIR medical informatics
BACKGROUND: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients'...

Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial Intelligence (XAI).

Langenbeck's archives of surgery
PURPOSE: This brief report aims to summarize and discuss the methodologies of eXplainable Artificial Intelligence (XAI) and their potential applications in surgery.

Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.

Brain topography
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, a...

Identification of WDR74 and TNFRSF12A as biomarkers for early osteoarthritis using machine learning and immunohistochemistry.

Frontiers in immunology
BACKGROUND: Osteoarthritis (OA) is a chronic joint condition that causes pain, limited mobility, and reduced quality of life, posing a threat to healthy aging. Early detection is crucial for improving prognosis. Recent research has focused on the rol...

A Survey of Few-Shot Learning for Biomedical Time Series.

IEEE reviews in biomedical engineering
Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data. Data-driven models have tremendous potential to assist clinical diagnosis and improve ...