de Filippis, R. and Al Foysal, A. (2026) Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data.
Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...
Discover a smarter way to grow with Learn with Jay, your trusted source for mastering valuable skills and unlocking your full potential. Whether you're aiming to advance your career, build better ...
Background: Acute ST-segment elevation myocardial infarction (STEMI) is a cardiovascular emergency that is associated with a high risk of death. In this study, we developed explainable machine ...
Abstract: Global agriculture is facing major challenges such as food security, sustainable water management, and the preservation of natural resources. Water scarcity, exacerbated by climate change, ...
SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes means ...
1 Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2 Cardiovascular Surgery Research and Development Committee, Iran University of Medical ...
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