Denaxas, Spiros

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 4,718

Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1.25 million people

Rapsomaniki, E., Timmis, A., George, J., Pujades-Rodriguez, M., Shah, A. D., Denaxas, S., White, I. R., Caulfield, M. J., Deanfield, J. E., Smeeth, L., et al. (2014) · The Lancet

Blood Pressure and Hypertension StudiesCardiovascular Health and Disease Prevention

Cited by 1,643

Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1.9 million people

Shah, A. D., Langenberg, C., Rapsomaniki, E., Denaxas, S., Pujades-Rodriguez, M., Gale, C. P., Deanfield, J., Smeeth, L., Timmis, A., & Hemingway, H. (2015) · The Lancet Diabetes & Endocrinology

Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Treatment and Management

Cited by 1,225

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,069

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 1,039

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · BMJ

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 665

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

Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., Noursadeghi, M., Pillay, D., Sebire, N., Holmes, C., et al. (2020) · The Lancet

COVID-19 epidemiological studiesCOVID-19 Clinical Research Studies

Cited by 518

Estimated impact of the COVID-19 pandemic on cancer services and excess 1-year mortality in people with cancer and multimorbidity: near real-time data on cancer care, cancer deaths and a population-based cohort study

Lai, A. G., Pasea, L., Banerjee, A., Hall, G., Denaxas, S., Chang, W. H., Katsoulis, M., Williams, B., Pillay, D., Noursadeghi, M., et al. (2020) · BMJ Open

COVID-19 and healthcare impactsGlobal Cancer Incidence and Screening

Cited by 447

A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service

Kuan, V., Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Bhatti, O., Husain, S., Sutaria, S., Hingorani, M., Nitsch, D., Parisinos, C. A., et al. (2019) · The Lancet Digital Health

Chronic Disease Management StrategiesMedical Coding and Health Information

Cited by 305

The Digital Health Competencies in Medical Education Framework

Car, J., Ong, Q. C., Erlikh Fox, T., Leightley, D., Kemp, S. J., Švab, I., Tsoi, K. K. F., Sam, A. H., Kent, F. M., Hertelendy, A. J., et al. (2025) · JAMA Network Open

Innovations in Medical EducationHealth and Medical Education

Cited by 155

Associations between measures of sarcopenic obesity and risk of cardiovascular disease and mortality: a cohort study and mendelian randomization analysis using the UK biobank

Farmer, R. E., Mathur, R., Schmidt, A. F., Bhaskaran, K., Fatemifar, G., Eastwood, S. V., Finan, C., Denaxas, S., Smeeth, L., & Chaturvedi, N. (2019) · Journal of the American Heart Association

Nutrition and Health in AgingDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 144

Time spent at blood pressure target and the risk of death and cardiovascular diseases

Chung, S.-C., Pujades-Rodriguez, M., Duyx, B., Denaxas, S. C., Pasea, L., Hingorani, A., Timmis, A., Williams, B., & Hemingway, H. (2018) · PLOS ONE

Blood Pressure and Hypertension StudiesSodium Intake and Health

Cited by 51

Unlocking electronic health records: a hybrid graph RAG approach to safe clinical AI for patient QA

Thio, S., Lewis, M., Denaxas, S., & Dobson, R. J. B. (2026) · Frontiers in Digital Health

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 6

Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines

Lewis, M., Thio, S., Roberts, A., Siju, C., Mukit, W., Kuruvilla, R., Jiang, Z. J., Möller-Grell, N., Borakati, A., Dobson, R. J., et al. (2025) · arXiv

Artificial Intelligence in Healthcare and EducationInformation Retrieval and Search Behavior

Cited by 0