SinoXivA 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...
A comparative study on the accuracy of different machine learning methods for lung cancer prediction
Background The incidence and mortality rates of lung cancer (LC) are extremely high and continue to rise. Early diagnosis combined with timely intervention can effectively reduce mortality in LC patie...
The transition from effortful to automatic processing is a defining feature of skill acquisition, and converging evidence implicates theta-gamma phase-amplitude coupling as the neural signature of thi...
Stroke remains a major global health burden and is a frequent and severe complication among patients with coronary heart disease (CHD). Early identification of individuals at high risk is essential fo...
Purpose To evaluate the prognostic value of preoperative hemoglobin-lymphocyte-neutrophil ratio (HLNR) and hemoglobin-lymphocyte-platelet ratio (HLPR) for predicting tumor recurrence at control cystos...
The Madden-Julian Oscillation (MJO) is an important driver of global weather and climate extremes, but its prediction in operational dynamical models remains challenging, with skillful forecasts typic...
