output
20082026
most citedCommon pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

1k citations

5 papers

stat.ME2026

Introducing precision-weighted bias as a performance measure to inform the inclusion of adaptive designs in meta-analysis

Martin Law, David S. Robertson, Sofia S. Villar +4

We propose a novel, intuitive measure of statistical performance: precision-weighted bias. Precision-weighted bias is defined as the unconditional bias of an estimator weighted by…

stat.AP2024★ 2 cited

Implementing Response-Adaptive Randomisation in Stratified Rare-disease Trials: Design Challenges and Practical Solutions

Rajenki Das, Nina Deliu, Mark Toshner +1

Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implement…

cs.SE2022★ 11 cited

Navigating the challenges in creating complex data systems: a development philosophy

Sören Dittmer, Michael Roberts, Julian Gilbey +6

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy a…

cs.LG2020★ 1k cited

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Michael Roberts, Derek Driggs, Matthew Thorpe +13

Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (…

astro-ph2008★ 152 cited

On disc protoplanet interactions in a non-barotropic disc with thermal diffusion

S. -J. Paardekooper, J. C. B. Papaloizou

We study the disc planet interactions of low-mass protoplanets embedded in a circumstellar disc. We extend the standard theory of planet migration from the usual locally isothermal…