AIMC Topic: Machine Learning

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Stabilizing machine learning for reproducible and explainable results: A novel validation approach to subject-specific insights.

Computer methods and programs in biomedicine
INTRODUCTION: Machine Learning (ML) is transforming medical research by enhancing diagnostic accuracy, predicting disease progression, and personalizing treatments. While general models trained on large datasets identify broad patterns across populat...

Ecological risk assessment of oilfield soil through the use of machine learning combining with spatial interaction effects.

Ecotoxicology and environmental safety
With the intensification of oil extraction activities, total petroleum hydrocarbons (TPHs) and toxic elements contamination in soil around oil wells have become severe environmental problems. This paper proposed a novel method based on machine learni...

Transcriptomic analysis reveals novel targets in benign schwannoma using machine learning.

Neuroscience
BACKGROUND & OBJECTIVE: This study aimed to identify key immune-related biomarkers of benign schwannoma through machine learning-assisted transcriptomic and single-cell analyses, and to construct a predictive model for disease evaluation.

Identification of Polymeric Nanoparticles Using Strategic Peptide Sensor Configurations and Machine Learning.

ACS sensors
Environmental pollution by miniaturized plastics such as micro- and nanoplastics continues to escalate, posing serious risks to ecosystems and human health. Therefore, there is an urgent need to detect or identify the plastics. Although the technique...

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...

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...

Combination of 2D and 3D nnU-Net for ground glass opacity segmentation in CT images of Post-COVID-19 patients.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: The COVID-19 pandemic plays a significant roles in the global health, highlighting the imperative for effective management of post-recovery symptoms. Within this context, Ground Glass Opacity (GGO) in lung computed tomograph...

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...