AIMC Topic: Machine Learning

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Radiomics-enhanced modelling approach for predicting the need for ECMO in ARDS patients: a retrospective cohort study.

Scientific reports
Decisions regarding veno-venous extracorporeal membrane oxygenation (vv-ECMO) in patients with acute respiratory distress syndrome (ARDS) are often based solely on clinical and physiological parameters, which may insufficiently reflect severity and h...

Immune system development-related signature predicts prognosis and sorafenib-treatment resistance of hepatocellular carcinoma by intergrating machine learning and single-cell analyses.

Scientific reports
The development of the immune system (ISD) plays a pivotal role in both the genesis and progression of tumors, yet its specific functions in hepatocellular carcinoma (HCC) and the mechanisms behind sorafenib resistance remain elusive. In our investig...

Role of human collaboration in artificial intelligence with fuzzy based mathematical models and decision making problems.

Scientific reports
The rapid development of artificial intelligence (AI) and machine learning (ML) has revolutionized computer technology, enabling it to make intelligent decisions, exhibit adaptive behavior, and foster synergistic human-AI environments. To ensure that...

Explainable artificial intelligence identifies and localizes left ventricular scar in hypertrophic cardiomyopathy using 12-Lead electrocardiogram.

Scientific reports
Left ventricular (LV) scar is a major risk factor for sudden death and heart failure in hypertrophic cardiomyopathy (HCM). LV scar evolves over time and needs longitudinal assessment. Currently, LV scar detection relies on late gadolinium enhancement...

Advanced MRI based Alzheimer's diagnosis through ensemble learning techniques.

Scientific reports
Alzheimer's Disease is a condition that affects the brain and causes changes in behavior and memory loss while making it hard to carry out tasks properly. It's vital to spot the illness early, for effective treatment. MRI technology has advanced in d...

Machine learning for stroke prediction using imbalanced data.

Scientific reports
The research focused on predicting strokes, a significant threat to health and well-being. The primary challenge addressed was the use of a highly imbalanced dataset. Various data preprocessing techniques were employed to tackle this, enabling the co...

SARST2 high-throughput and resource-efficient protein structure alignment against massive databases.

Nature communications
The flood of protein structural Big Data is coming. With the belief that biotech researchers deserve powerful analysis engines to overcome the challenge of rapidly increasing computational demands, we are devoted to developing efficient protein struc...

Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.

Journal of medical Internet research
BACKGROUND: Sepsis-associated acute kidney injury (SA-AKI) is a frequent and life-threatening complication in patients in the intensive care unit (ICU), significantly increasing both mortality rates and the risk of chronic kidney dysfunction. However...

Electrocardiogram heart rate variability for machine learning diagnosis of obstructive sleep Apnoea: A bayesian meta-analysis.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Obstructive sleep apnoea syndrome (OSA) is a common yet underdiagnosed condition associated with significant health risks. Although polysomnography is the diagnostic gold standard, it is resource-intensive and unsuitable for widespread scree...

TFDISNet: Temporal-frequency domain-invariant and domain-specific feature learning network for enhanced auditory attention decoding from EEG signals.

Biomedical physics & engineering express
Auditory Attention Decoding (AAD) from Electroencephalogram (EEG) signals presents a significant challenge in brain-computer interface (BCI) research due to the intricate nature of neural patterns. Existing approaches often fail to effectively integr...