AIMC Topic: Female

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Selection of representative electrodes for stereoscopic visual comfort studies in conjunction with brain mechanism analysis.

Brain research
The widespread use of 3D stereoscopic technology has drawn attention to the problem of visual discomfort, several studies have used EEG to assess visual comfort and discomfort phenomena, but there is a lack of scientific basis for the selection of el...

Interpretable Machine Learning approach for predicting clinically significant suicide risk: A case study of patients with major depressive disorder in Greece.

Psychiatry research
Suicide prevention is currently a global public health priority, since suicide has been a prevalent cause of death or potential loss of life. Multiple factors contribute to suicide risk, such as depression and a history of attempted suicide among oth...

PMFF-Net: A deep learning-based image classification model for UIP, NSIP, and OP.

Computers in biology and medicine
BACKGROUND: High-resolution computed tomography (HRCT) is helpful for diagnosing interstitial lung diseases (ILD), but it largely depends on the experience of physicians. Herein, our study aims to develop a deep-learning-based classification model to...

Prediction of 30-day readmission in diabetes management using Machine learning.

Computers in biology and medicine
This study aims to develop a robust and accurate model to forecast 30-day readmissions for patients with diabetes by leveraging machine learning techniques. Diabetes, being a chronic condition with complex care needs, often leads to frequent hospital...

Integrative multi-omics and machine-learning approaches uncover a novel metabolic-related signature associated with cancer-associated fibroblasts in gastric cancer development.

Computers in biology and medicine
Gastric cancer (GC) ranks as the fifth most commonly diagnosed malignancy and the fourth leading cause of cancer-related mortality worldwide. The integration of machine learning in the analysis of GC metabolomics data for biomarker identification rem...

A strategy based on paraconsistent random forest for sEMG gesture recognition systems robust to contaminated data.

Computers in biology and medicine
Applying machine learning algorithms to physical signals is always challenging since undesirable events can occur when signals are acquired outside a controlled environment. Among several applications, movement recognition through sEMG signals is esp...

Optimizing treatment to control LDL cholesterol using machine learning.

Computers in biology and medicine
INTRODUCTION: Increased LDL cholesterol is one of the main risk factors for cardiovascular diseases; therefore, adequate therapy reduces the risk of developing cardiovascular disease. Artificial intelligence (AI) is a tool that can significantly help...

PED-IA, a CDSS to support decision in pediatrics telephone triage: a crossover evaluation.

Computers in biology and medicine
BACKGROUND: Pediatric emergency departments face overcrowding, often driven by non-urgent consultations. Telephone triage, supported by clinical decision support systems (CDSSs), offers a potential solution to improve decision accuracy and reduce unn...

Quantification of Breast Arterial Calcification in Mammograms Using a UNet-Based Deep Learning for Detecting Cardiovascular Disease.

Academic radiology
BACKGROUND: Breast arterial calcification (BAC) is increasingly recognized as a significant indicator of cardiovascular risk, necessitating improvements in detection and quantification methods through mammographic screening.

A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.

Journal of biomedical informatics
OBJECTIVE: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have s...