Kerry Pearn

Kerry Pearn#

Research Fellow

Publications#

2024#

SAMueL-2 Preprints:

Allen, M., Pearn, K., Jarvie, R., Laws, A., Frost, J., Farmer, L., McMeekin, P., Pope, C., Lang, I., Pratt-Boyden, K., Everson, R., & James, M. (2024). Stroke Audit Machine Learning (SAMueL-2). Zenodo. https://doi.org/10.5281/zenodo.12798409

Pearn, K., Allen, M., Laws, A., & James, M. (2024). Are the patients who would benefit from thrombolysis the same ones as those receiving it? A machine learning study of the UK stroke registry. Zenodo. https://doi.org/10.5281/zenodo.12798299

Pearn, K., Allen, M., Laws, A., & James, M. (2024). Thrombolysis: Are the results from the clinical trial meta-analysis seen in real life outcomes? A machine learning study of the UK stroke registry. Zenodo. https://doi.org/10.5281/zenodo.12798319

Pearn, K., Allen, M., Laws, A., McMeekin, P., & James, M. (2024). Identifying levers for improving thrombolysis use and outcomes – combining clinical pathway simulation and machine learning applied to the UK stroke registry. Zenodo. https://doi.org/10.5281/zenodo.13252978

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. SAGE Publications. dx.doi.org/10.1177/23969873231189040

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. SAGE Publications. dx.doi.org/10.1177/23969873211063323

Allen, M., Pearn, K., Ford, G., White, P., Rudd, A., McMeekin, P., Stein, K., James, M. (2021). Implementing the NHS England Long Term Plan for stroke: how should reperfusion services be configured? A modelling study. Center for Open Science. dx.doi.org/10.31219/osf.io/yg3x8

2022#

Allen, M., James, C., Frost, J., Liabo, K., Pearn, K., Monks, T., Zhelev, Z., Logan, S., Everson, R., James, M., Stein, K. (2022). Using simulation and machine learning to maximise the benefit of intravenous thrombolysis in acute stroke in England and Wales: the SAMueL modelling and qualitative study. National Institute for Health and Care Research. dx.doi.org/10.3310/gvzl5699

Allen, M., James, C., Frost, J., Liabo, K., Pearn, K., Monks, T., Everson, R., Stein, K., James, M. (2022). Use of Clinical Pathway Simulation and Machine Learning to Identify Key Levers for Maximizing the Benefit of Intravenous Thrombolysis in Acute Stroke. Ovid Technologies (Wolters Kluwer Health). dx.doi.org/10.1161/strokeaha.121.038454

2019#

Allen, M., Pearn, K., Monks, T., Bray, B., 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. dx.doi.org/10.1136/bmjopen-2018-028296

Allen, M., Pearn, K., Villeneuve, E., James, M., Stein, K. (2019). Planning and Providing Acute Stroke Care in England: The Effect of Planning Footprint Size. Frontiers Media SA. dx.doi.org/10.3389/fneur.2019.00150

2018#

Allen, M., Pearn, K., James, M., Ford, G., White, P., Rudd, A., McMeekin, P., Stein, K. (2018). Maximising access to thrombectomy services for stroke in England: A modelling study. SAGE Publications. dx.doi.org/10.1177/2396987318785421

2017#

Monks, T., van der Zee, D., Lahr, M., Allen, M., Pearn, K., James, M., Buskens, E., Luijckx, G. (2017). A framework to accelerate simulation studies of hyperacute stroke systems. Elsevier BV. dx.doi.org/10.1016/j.orhc.2017.09.002

2015#

Monks, T., Pearn, K., Allen, M. (2015). Simulation of stroke care systems. IEEE. dx.doi.org/10.1109/WSC.2015.7408262