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Abstract: High-dimensional gene expression data pose substantial challenges for machine-learning–based diagnostic modelling due to extreme dimensionality, noise, heterogeneous measurement conditions, ...

This repository contains the mini project for the ECS7020P Principles of Machine Learning course at Queen Mary University of London. This project develops an automated song recognition system capable ...

We developed a classifier to infer acute ischemic stroke severity from Medicare claims using the modified Rankin Scale at discharge. The classifier can be used to improve stroke outcomes research and ...

Abstract Wed136: Integration of Mechanistic Fontan Circulatory Models with Interpretable Machine Learning Classifiers Noah Schenk, BS, Alexander Egbe, MD, MPH, Brian Carlson, PhD, and Daniel Beard, ...

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...

Tumor subtyping based on morphological grade is used in cancer treatment and management decision-making and to determine a patient’s prognosis. While low- and high-grade tumors are predictive of ...

Real-world quality of life (QOL) in patients (pts) with metastatic renal cell carcinoma (mRCC) on active surveillance (AS) in the ODYSSEY prospective observational study. This is an ASCO Meeting ...

Abstract: Machine Learning (ML) models are increasingly used by domain experts to tackle classification tasks, aiming for high predictive accuracy. However, classifiers are inherently prone to ...