Publications

2023

Pearn, K., Allen, M., Laws, A., Monks, T., Everson, R., & James, M. (2023). What would other emergency stroke teams do? Using explainable machine learning to understand variation in thrombolysis practice. MedRxiv. https://doi.org/10.1101/2023.04.24.23289017

2022

Allen, M., James, C., Frost, J., Liabo, K., Pearn, K., Monks, T., Everson, R., Stein, K., & Martin James. (2022). Use of Clinical Pathway Simulation and Machine Learning to Identify Key Levers for Maximizing the Benefit of Intravenous Thrombolysis in Acute Stroke. Stroke, 53(9), 2758–2767. https://doi.org/10.1161/STROKEAHA.121.038454

2021

Allen, M., Pearn, K., Ford, G., White, P., Rudd, A., Mcmeekin, P., Stein, K., & James, M. (2021). National implementation of reperfusion for acute ischaemic stroke in England: How should services be configured? A modelling study. European Stroke Journal, 7, 239698732110633. https://doi.org/10.1177/23969873211063323

McMeekin, P., Flynn, D., James, M., Price, C. I., Ford, G. A., & White, P. (2021). Updating estimates of the number of UK stroke patients eligible for endovascular thrombectomy: incorporating recent evidence to facilitate service planning. European Stroke Journal, 6(4), 349–356. https://doi.org/10.1177/23969873211059471

Mcmeekin, P., Flynn, D., James, M., Price, C., Ford, G., & White, P. (2021). Updating estimates of the number of UK stroke patients eligible for endovascular thrombectomy: incorporating recent evidence to facilitate service planning. European Stroke Journal, 6, 239698732110594. https://doi.org/10.1177/23969873211059471

2019

Allen, M., Pearn, K., Monks, T., Bray, B. D., Everson, R., Salmon, A., James, M., & Stein, K. (2019). Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway. BMJ Open, 9(9). https://doi.org/10.1136/bmjopen-2018-028296

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