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Publications

Modelling the impact of rapid tests, tracing and distancing in lower-income countries suggest optimal policies varies with rural-urban settings

Jiang X. et al, (2021)

Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels

Willetts M. et al, (2020), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, 5286 - 5295

Epidemiological changes on the Isle of Wight after the launch of the NHS Test and Trace programme: a preliminary analysis.

Kendall M. et al, (2020), The Lancet. Digital health, 2, e658 - e666

Correction to: Graphing and reporting heterogeneous treatment effects through reference classes.

Watson JA. and Holmes CC., (2020), Trials, 21

A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices.

Watson JA. et al, (2020), PLoS genetics, 16

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera S. et al, (2020), The Lancet Digital Health, 2, e549 - e560

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Liu X. et al, (2020), The Lancet Digital Health, 2, e537 - e548

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera S. et al, (2020), Nature Medicine, 26, 1351 - 1363

NRF2 metagene signature is a novel prognostic biomarker in colorectal cancer.

O'Cathail SM. et al, (2020), Cancer genetics, 248-249, 1 - 10

Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis.

Benedetto U. et al, (2020), The Journal of thoracic and cardiovascular surgery

On the marginal likelihood and cross-validation

Fong E. and Holmes CC., (2020), Biometrika, 107, 489 - 496

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.

Banerjee A. et al, (2020), Lancet (London, England), 395, 1715 - 1725

Graphing and reporting heterogeneous treatment effects through reference classes.

Watson JA. and Holmes CC., (2020), Trials, 21

Testing for dependence on tree structures.

Behr M. et al, (2020), Proceedings of the National Academy of Sciences of the United States of America

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.

Vollmer S. et al, (2020), BMJ (Clinical research ed.), 368

Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error.

Watson JA. and Holmes CC., (2020), Trials, 21

A framework for adaptive mcmc targeting multimodal distributions

Pompe E. et al, (2020), Annals of Statistics, 48, 2930 - 2952

Improving the quality of machine learning in health applications and clinical research

Mateen BA. et al, (2020), Nature Machine Intelligence

Author Correction: Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed.

CONSORT-AI and SPIRIT-AI Steering Group None., (2019), Nature medicine, 25

Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed.

CONSORT-AI and SPIRIT-AI Steering Group None., (2019), Nature medicine, 25, 1467 - 1468

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