Stephen Eglen
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University Teaching Officer
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About
Dr. Stephen Eglen is a University Teaching Officer at the University of Cambridge, UK. His research focuses on reproducibility in scientific computing, causal inference in time series data, and open science initiatives. He has contributed to various publications, including those on sepsis criteria implementation and best practices for writing Dockerfiles in data science.
Recent Grants
Grant: Close
Novel analytical and datasharing tools for rich neuronal activity datasets obtained with a 4096 electrodes array
Open Date: 2010-12-14
Close Date: 2012-06-12
Grant: Close
Measuring and modelling the dynamics of retinotopic map formation.
Open Date: 2008-11-10
Close Date: 2016-03-09
Grant: Close
Measuring and modelling the dynamics of retinotopic map formation.
Open Date: 2008-09-10
Close Date: 2013-09-09
Grant: Close
Measuring and modelling the dynamics of retinotopic map formation.
Open Date: 2008-02-01
Close Date: 2013-11-30
Grant: Close
CARMEN: Code analysis, repository, and modelling for e-Neuroscience
Open Date: 2006-09-30
Close Date: 2011-03-30
Articles (6)
Sepsis is a major healthcare problem with substantial mortality and a common reason for admission to the intensive care unit (ICU). For this reason, the management of sepsis is an important area of ICU research. A number of large-scale, freely-accessible ICU databases are available for observational research and the robust identification of septic patients in such data sets is crucial for research purposes, particularly for comparative studies between critical care sub-populations which may vary around the world. However, data structures are poorly standardised due to inevitable variances in clinical electronic health record system vendor and implementation as well as research database design choices. Robust and well-documented cohort selection (such as patients with sepsis) is crucial for reproducible research. In this work, we operationalise the Sepsis-3 definition on the AmsterdamUMCdb, a recently published large European ICU database, publishing open-access code for wider use by critical care researchers.
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2022
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2021
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2021
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2021
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2020
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2020
Collaborators (3)
Benjamin Evans
University of Bristol
Ari Ercole
University of Cambridge
Tom Edinburgh
University of Oxford
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