Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population. - Bilal A. Mateen; Matthias Bussas; Catherine Doogan; Denise Waller; Alessia Saverino; Franz J. Király; E. Diane Playford (CoRR, 2016)

Probabilistic supervised learning. - Frithjof Gressmann; Franz J. Király; Bilal A. Mateen; Harald Oberhauser (CoRR, 2018)

NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature. - Franz J. Király; Bilal A. Mateen; Raphael Sonabend (CoRR, 2018)

Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness. - Sebastian J. Vollmer; Bilal A. Mateen; Gergo Bohner; Franz J. Király; Rayid Ghani; Pall Jonsson; Sarah Cumbers; Adrian Jonas; Katherine S. L. McAllister; Puja Myles; David Granger; Mark Birse; et al. (CoRR, 2018)

Debiasing classifiers: is reality at variance with expectation? - Ashrya Agrawal; Florian Pfisterer; Bernd Bischl; Jiahao Chen 001; Srijan Sood; Sameena Shah; Francois Buet-Golfouse; Bilal A. Mateen; Sebastian J. Vollmer (CoRR, 2020)

Improving the quality of machine learning in health applications and clinical research. - Bilal A. Mateen; James Liley; Alastair K. Denniston; Chris C. Holmes; Sebastian J. Vollmer (Nat. Mach. Intell., 2020)

Model updating after interventions paradoxically introduces bias. - James Liley; Samuel R. Emerson; Bilal A. Mateen; Catalina A. Vallejos; Louis J. M. Aslett; Sebastian J. Vollmer (AISTATS, 2021)

We need to talk about the lack of investment in digital research infrastructure. - Rebecca Knowles; Bilal A. Mateen; Yo Yehudi (Nat. Comput. Sci., 2021)

Faking feature importance: A cautionary tale on the use of differentially-private synthetic data. - Oscar Giles; Kasra Hosseini; Grigorios Mingas; Oliver Strickson; Louise Bowler; Camila Rangel Smith; Harrison Wilde; Jen Ning Lim; Bilal A. Mateen; Kasun Amarasinghe; Rayid Ghani; Alison J. Heppenstall; et al. (CoRR, 2022)

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine. - Ashwini Venkatasubramaniam; Bilal A. Mateen; Beverley M. Shields; Andrew T. Hattersley; Angus G. Jones; Sebastian J. Vollmer; John M. Dennis (BMC Medical Informatics Decis. Mak., 2023)

Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland. - James Liley; Gergo Bohner; Samuel R. Emerson; Bilal A. Mateen; Katie Borland; David Carr; Scott Heald; Samuel D. Oduro; Jill Ireland; Keith Moffat; Rachel Porteous; Stephen Riddell; et al. (npj Digit. Medicine, 2024)

Development and assessment of a machine learning tool for predicting emergency admission in Scotland. - James Liley; Gergo Bohner; Samuel R. Emerson; Bilal A. Mateen; Katie Borland; David Carr; Scott Heald; Samuel D. Oduro; Jill Ireland; Keith Moffat; Rachel Porteous; Stephen Riddell; et al. (npj Digit. Medicine, 2024)