SinoXivClustering is widely used to discover interpretable structure in data, but conventional unsupervised methods group examples by \emph{feature similarity} alone, ignoring any available prediction tar...
The current data validation systems are mostly reactive, static, and resource-heavy which may lead to interruptions of pipelines and will not be able to detect data corruption in real-time settings. T...
Adverse pregnancy outcomes (APOs), including preeclampsia, preterm birth, and maternal heart failure, pose substantial risks for women with pre-existing heart disease. Soluble ST2 (sST2), a biomarker ...
Anthropogenic activities have increasingly pressured biodiversity worldwide. The cactus Melocactus pachyacanthus, an endemic and endangered species of Brazil´s Caatinga biome, is particularly vulnerab...
Modeling air quality concentration plays a crucial role in predicting and mitigating airborne pollutant levels. This study collected and organized daily average air quality data and meteorological dat...
Purpose: To assess prostate biopsy uptake and its predictors among patients previously found to have elevated Prostate-Specific Antigen results at the Kyabirwa Surgical Center. Method: An analytical c...
A fully reproducible expected-goals (xG) modelling pipeline is presented as an interpretable machine learning approach using open football event data from StatsBomb for La Liga 2015/2016 and the 2018 ...
The growing prevalence of diabetes highlights the need for scalable, accurate, and privacy-conscious testing technologies. To train models, traditional machine learning (ML) techniques often rely on c...
Joint models of longitudinal and time-to-event data are essential for dynamic prediction in chronic disease research, but standard formulations typically assume linear biomarker–hazard associations an...
Healthcare revenue cycle management (RCM) loses billions annually to claim denials, yet existing machine learning approaches treat billing as a prediction problem rather than a decision problemthey pr...
