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Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
Loading articles data…
Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
4 articles
Newest firstFraud in the health landscape is an aggravating issue, with far-reaching consequences burdening the financial stability of the health industry and threatening the quality of medical care. It results from vulnerabilities within the current healthcare framework that are exploited by the fraudsters in their favor. In spite of many developed models that aim to detect fraudulent patterns in insurance claims, the accura…
Open article record in new tab ↗Esophageal cancer (EC) remains one of the most lethal malignancies worldwide, with poor survival outcomes largely attributable to late-stage diagnosis and limited treatment effectiveness. Early detection and accurate risk stratification are therefore essential for improving clinical management. In this study, we investigate the predictive value of socio-demographic, dietary, behavioral, environmental, and clinical…
BackgroundOpioid Use Disorder (OUD) continues to pose a pressing public health challenge across the United States, highlighting the critical need for early and accurate risk assessment tools that facilitate prompt prevention and intervention efforts. Machine learning methods have emerged as valuable tools for parsing complex medical datasets and aiding in clinical decisions. However, their effectiveness and interp…
Open article record in new tab ↗High-dimensional medical datasets present challenges in feature selection, where traditional methods often prioritize spurious correlations over causally relevant variables, compromising model interpretability and clinical utility. We introduce CausalDRIFT, a causal feature selection algorithm grounded in the Frisch-Waugh-Lovell theorem and Double Machine Learning, which estimates the Average Treatment Effect (ATE…
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