Machine learning‐enabled multitrust audit of stroke comorbidities using natural language processing-Reference-Cited by-同舟云学术

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Machine learning‐enabled multitrust audit of stroke comorbidities using natural language processing

Published:2021-08-29 Issue:12 Volume:28 Page:4090-4097
ISSN:1351-5101
Container-title:European Journal of Neurology
language:en
Short-container-title:Eur J Neurol

Author:

Shek Anthony¹,Jiang Zhilin²,Teo James¹²^ORCID,Au Yeung Joshua³,Bhalla Ajay³,Richardson Mark P.¹²⁴,Mah Yee¹⁵^ORCID

Affiliation:

1. School of Neuroscience Institute of Psychiatry, Psychology, & Neuroscience King's College London London UK

2. King's College Hospital NHS Foundation Trust London UK

3. Guy's & St Thomas' Hospital NHS Foundation Trust London UK

4. National Institute for Health Research Maudsley Biomedical Research Centre South London & Maudsley NHS Foundation Trust London UK

5. School of Biomedical Engineering and Imaging Sciences King's College London London UK

Funder

Medical Research Council

Publisher

Wiley

Subject

Neurology (clinical),Neurology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ene.15071

Reference25 articles.

1. Using population-based routinely collected data from the Sentinel Stroke National Audit Programme to investigate factors associated with discharge to care home after rehabilitation

2. The economic burden of stroke care in England, Wales and Northern Ireland: Using a national stroke register to estimate and report patient-level health economic outcomes in stroke

3. Great Britain National Audit Office Morse A.Department of Health: progress in improving stroke care. London: The Stationery Office;2010.

4. Data-driven discovery of seasonally linked diseases from an Electronic Health Records system

5. Angiotensin‐converting enzyme inhibitors and angiotensin II receptor blockers are not associated with severe COVID‐19 infection in a multi‐site UK acute hospital trust

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