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An Automated Approach to Calculating the Daily Dose of Tacrolimus in Electronic Health Records

Clinical research often requires extracting detailed drug information, such as medication names and dosages, from Electronic Health Records (EHR). Since medication information is often recorded as both structured and unstructured formats in the EHR, extracting all the relevant drug mentions and dete...

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Detalles Bibliográficos
Autores principales: Xu, Hua, Doan, Son, Birdwell, Kelly A., Cowan, James D., Vincz, Andrew J., Haas, David W., Basford, Melissa A., Denny, Joshua C.
Formato: Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041548/
https://www.ncbi.nlm.nih.gov/pubmed/21347153
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author Xu, Hua
Doan, Son
Birdwell, Kelly A.
Cowan, James D.
Vincz, Andrew J.
Haas, David W.
Basford, Melissa A.
Denny, Joshua C.
author_facet Xu, Hua
Doan, Son
Birdwell, Kelly A.
Cowan, James D.
Vincz, Andrew J.
Haas, David W.
Basford, Melissa A.
Denny, Joshua C.
author_sort Xu, Hua
collection PubMed
description Clinical research often requires extracting detailed drug information, such as medication names and dosages, from Electronic Health Records (EHR). Since medication information is often recorded as both structured and unstructured formats in the EHR, extracting all the relevant drug mentions and determining the daily dose of a medication for a selected patient at a given date can be a challenging and time-consuming task. In this paper, we present an automated approach using natural language processing to calculate daily doses of medications mentioned in clinical text, using tacrolimus as a test case. We evaluated this method using data sets from four different types of unstructured clinical data. Our results showed that the system achieved precisions of 0.90–1.00 and recalls of 0.81–1.00.
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spelling pubmed-30415482011-02-23 An Automated Approach to Calculating the Daily Dose of Tacrolimus in Electronic Health Records Xu, Hua Doan, Son Birdwell, Kelly A. Cowan, James D. Vincz, Andrew J. Haas, David W. Basford, Melissa A. Denny, Joshua C. Summit on Translat Bioinforma Articles Clinical research often requires extracting detailed drug information, such as medication names and dosages, from Electronic Health Records (EHR). Since medication information is often recorded as both structured and unstructured formats in the EHR, extracting all the relevant drug mentions and determining the daily dose of a medication for a selected patient at a given date can be a challenging and time-consuming task. In this paper, we present an automated approach using natural language processing to calculate daily doses of medications mentioned in clinical text, using tacrolimus as a test case. We evaluated this method using data sets from four different types of unstructured clinical data. Our results showed that the system achieved precisions of 0.90–1.00 and recalls of 0.81–1.00. American Medical Informatics Association 2010-03-01 /pmc/articles/PMC3041548/ /pubmed/21347153 Text en ©2010 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Xu, Hua
Doan, Son
Birdwell, Kelly A.
Cowan, James D.
Vincz, Andrew J.
Haas, David W.
Basford, Melissa A.
Denny, Joshua C.
An Automated Approach to Calculating the Daily Dose of Tacrolimus in Electronic Health Records
title An Automated Approach to Calculating the Daily Dose of Tacrolimus in Electronic Health Records
title_full An Automated Approach to Calculating the Daily Dose of Tacrolimus in Electronic Health Records
title_fullStr An Automated Approach to Calculating the Daily Dose of Tacrolimus in Electronic Health Records
title_full_unstemmed An Automated Approach to Calculating the Daily Dose of Tacrolimus in Electronic Health Records
title_short An Automated Approach to Calculating the Daily Dose of Tacrolimus in Electronic Health Records
title_sort automated approach to calculating the daily dose of tacrolimus in electronic health records
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041548/
https://www.ncbi.nlm.nih.gov/pubmed/21347153
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