Imperial College London
Alternate

DrVasaCurcin

Faculty of Medicine, School of Public Health

Honorary Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 0716vasa.curcin Website

 
 
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Location

 

320Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chapman:2021:10.3233/SHTI210233,
author = {Chapman, M and Domínguez, J and Fairweather, E and Delaney, BC and Curcin, V},
doi = {10.3233/SHTI210233},
journal = {Stud Health Technol Inform},
pages = {560--564},
title = {Using Computable Phenotypes in Point-of-Care Clinical Trial Recruitment.},
url = {http://dx.doi.org/10.3233/SHTI210233},
volume = {281},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A key challenge in point-of-care clinical trial recruitment is to autonomously identify eligible patients on presentation. Similarly, the aim of computable phenotyping is to identify those individuals within a population that exhibit a certain condition. This synergy creates an opportunity to leverage phenotypes in identifying eligible patients for clinical trials. To investigate the feasibility of this approach, we use the Transform clinical trial platform and replace its archetype-based eligibility criteria mechanism with a computable phenotype execution microservice. Utilising a phenotype for acute otitis media with discharge (AOMd) created with the Phenoflow platform, we compare the performance of Transform with and without the use of phenotype-based eligibility criteria when recruiting AOMd patients. The parameters of the trial simulated are based on those of the REST clinical trial, conducted in UK primary care.
AU  - Chapman,M
AU  - Domínguez,J
AU  - Fairweather,E
AU  - Delaney,BC
AU  - Curcin,V
DO  - 10.3233/SHTI210233
EP  - 564
PY  - 2021///
SP  - 560
TI  - Using Computable Phenotypes in Point-of-Care Clinical Trial Recruitment.
T2  - Stud Health Technol Inform
UR  - http://dx.doi.org/10.3233/SHTI210233
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34042638
VL  - 281
ER  -
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