AIMC Topic: Machine Learning

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Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism.

Hypertension (Dallas, Tex. : 1979)
BACKGROUND: Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of secondary hypertension, is crucial due to different treatment approaches. While adrenal venous sampling is the gold standard, its invasiveness, limite...

Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.

JMIR medical informatics
BACKGROUND: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support...

"Dictionary of immune responses" reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.

Frontiers in immunology
BACKGROUND: Intervertebral disc degeneration (IDD) is widely regarded as the primary contributor to low back pain(LBP). As an immune-privileged organ, upon the onset of IDD, various components of the nucleus pulposus (NP) are exposed to the host's im...

Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.

Anesthesia and analgesia
BACKGROUND: Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that us...

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data.

Advances in medical sciences
PURPOSE: Spontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any surgical conditions or procedures, and is a common complication of cirrhosis. With a potential mortalit...

Adaptive indefinite kernels in hyperbolic spaces.

Neural networks : the official journal of the International Neural Network Society
Learning embeddings in hyperbolic space has gained increasing interest in the community, due to its property of negative curvature, as a way of encoding data hierarchy. Recent works investigate the improvement of the representation power of hyperboli...

Deciphering the impact of cascade reservoirs on nitrogen transport and nitrate transformation: Insights from multiple isotope analysis and machine learning.

Water research
Construction of cascade reservoirs has altered nutrient dynamics and biogeochemical cycles, thereby influencing the composition and productivity of river ecosystems. The Lancang River (LCR), characterized by its cascade reservoir system, presents unc...

Chemical composition alterations in rat brain hypothalamus induced by irisin administration using spectroscopic and machine learning techniques.

Analytical biochemistry
This study employed Fourier transform infrared (FTIR) spectroscopy to determine the chemical composition of brain tissues and the changes induced by irisin at doses of 50 mg and 100 mg. Brain tissues were collected from control rats and those adminis...

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

BioFactors (Oxford, England)
Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomar...

Multiparametric MRI Radiomics With Machine Learning for Differentiating HER2-Zero, -Low, and -Positive Breast Cancer: Model Development, Testing, and Interpretability Analysis.

AJR. American journal of roentgenology
MRI radiomics has been explored for three-tiered classification of HER2 expression levels (i.e., HER2-zero, HER2-low, or HER2-positive) in patients with breast cancer, although an understanding of how such models reach their predictions is lacking. ...