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Publications

Development and assessment of a machine learning tool for predicting emergency admission in Scotland.

Liley J. et al, (2024), NPJ digital medicine, 7

Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland.

Liley J. et al, (2024), NPJ digital medicine, 7

Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID-19.

Pigoli D. et al, (2024), Stat Med

Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records.

Venkatesh SS. et al, (2024), Nature communications, 15

Identification and validation of a machine learning model of complete response to radiation in rectal cancer reveals immune infiltrate and TGFβ as key predictors.

Domingo E. et al, (2024), EBioMedicine, 106

A large-scale and PCR-referenced vocal audio dataset for COVID-19

Budd J. et al, (2024), Scientific Data, 11

Equity in medical devices: trainers and educators play a vital role.

Whitehead M. et al, (2024), BMJ (Clinical research ed.), 385

To do no harm - and the most good - with AI in health care.

Goldberg CB. et al, (2024), Nature medicine

[Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI].

Cruz Rivera S. et al, (2024), Revista panamericana de salud publica = Pan American journal of public health, 48

[Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionDiretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI].

Liu X. et al, (2024), Revista panamericana de salud publica = Pan American journal of public health, 48

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers

Coppock H. et al, (2024), Nature Machine Intelligence

Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective

Falck F. et al, (2024), Proceedings of Machine Learning Research, 235, 12784 - 12805

Where Medical Statistics Meets Artificial Intelligence. Reply.

Hunter DJ. and Holmes C., (2023), N Engl J Med, 389, 2403 - 2404

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk

Jiang X. et al, (2023), Nature Genetics, 55, 1854 - 1865

Authors’ reply to the Discussion of ‘Martingale Posterior Distributions’

Fong E. et al, (2023), Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85, 1413 - 1416

Martingale posterior distributions

Fong E. et al, (2023), Journal of the Royal Statistical Society. Series B: Statistical Methodology, 85, 1357 - 1391

Where Medical Statistics Meets Artificial Intelligence.

Hunter DJ. and Holmes C., (2023), The New England journal of medicine, 389, 1211 - 1219

Making the invisible visible: what can we do about biased AI in medical devices?

Whitehead M. et al, (2023), BMJ (Clinical research ed.), 382

Optimal strategies for learning multi-ancestry polygenic scores vary across traits

Lehmann B. et al, (2023), Nature Communications, 14

Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database.

Sinha S. et al, (2023), European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery, 63

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