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Bayes Theorem

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BAB-GSL: Using Bayesian influence with attention mechanism to optimize graph structure in basic views.

Neural networks : the official journal of the International Neural Network Society
In recent years, Graph Neural Networks (GNNs) have garnered significant attention, with a notable focus on Graph Structure Learning (GSL), a branch dedicated to optimizing graph structures to enhance network training performance. Current GSL methods ...

CureMate: A clinical decision support system for breast cancer treatment.

International journal of medical informatics
BACKGROUND: Breast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process...

Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide.

Journal of environmental management
The short-term risks associated with atmospheric trace gases, particularly carbon monoxide (CO), are critical for ecological security and human health. Traditional statistical methods, which still dominate the assessment of these risks, limit the pot...

Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.

Archives of Iranian medicine
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. T...

scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks.

Interdisciplinary sciences, computational life sciences
Cancer is a significant global public health concern, where early detection can greatly enhance curative outcomes. Therefore, the identification of cancer cells holds significant importance as the primary method for cancer diagnosis. The advancement ...

Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe.

Annals of medicine
BACKGROUND: Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abio...

Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence.

Medicina (Kaunas, Lithuania)
: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of this study was to assess the predictive performance of four machine learning algorithms for the pre...

Proof of concept study on early forecasting of antimicrobial resistance in hospitalized patients using machine learning and simple bacterial ecology data.

Scientific reports
Antibiotic resistance in bacterial pathogens is a major threat to global health, exacerbated by the misuse of antibiotics. In hospital practice, results of bacterial cultures and antibiograms can take several days. Meanwhile, prescribing an empirical...

Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Practicing mindfulness is a mental process toward interoceptive awareness, achieving stress reduction and emotion regulation through brain-function alteration. Literature has shown that electroencephalography (EEG)-derived c...

COVID-19 from symptoms to prediction: A statistical and machine learning approach.

Computers in biology and medicine
During the COVID-19 pandemic, the analysis of patient data has become a cornerstone for developing effective public health strategies. This study leverages a dataset comprising over 10,000 anonymized patient records from various leading medical insti...