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Natural language processing for automated annotation of medication mentions in primary care visit conversations
OBJECTIVES: The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. MATERIALS AND METHODS: Eight clinicians contributed to a data set of 85 clinic visit transcript...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374372/ https://www.ncbi.nlm.nih.gov/pubmed/34423262 http://dx.doi.org/10.1093/jamiaopen/ooab071 |
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author | Ganoe, Craig H Wu, Weiyi Barr, Paul J Haslett, William Dannenberg, Michelle D Bonasia, Kyra L Finora, James C Schoonmaker, Jesse A Onsando, Wambui M Ryan, James Elwyn, Glyn Bruce, Martha L Das, Amar K Hassanpour, Saeed |
author_facet | Ganoe, Craig H Wu, Weiyi Barr, Paul J Haslett, William Dannenberg, Michelle D Bonasia, Kyra L Finora, James C Schoonmaker, Jesse A Onsando, Wambui M Ryan, James Elwyn, Glyn Bruce, Martha L Das, Amar K Hassanpour, Saeed |
author_sort | Ganoe, Craig H |
collection | PubMed |
description | OBJECTIVES: The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. MATERIALS AND METHODS: Eight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations. RESULTS: Sixty-five transcripts with 1121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9% on the same test set. DISCUSSION: Our medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software. CONCLUSION: Integration of our annotation system with clinical recording applications has the potential to improve patients’ understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes. |
format | Online Article Text |
id | pubmed-8374372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-83743722021-08-20 Natural language processing for automated annotation of medication mentions in primary care visit conversations Ganoe, Craig H Wu, Weiyi Barr, Paul J Haslett, William Dannenberg, Michelle D Bonasia, Kyra L Finora, James C Schoonmaker, Jesse A Onsando, Wambui M Ryan, James Elwyn, Glyn Bruce, Martha L Das, Amar K Hassanpour, Saeed JAMIA Open Research and Applications OBJECTIVES: The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. MATERIALS AND METHODS: Eight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations. RESULTS: Sixty-five transcripts with 1121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9% on the same test set. DISCUSSION: Our medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software. CONCLUSION: Integration of our annotation system with clinical recording applications has the potential to improve patients’ understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes. Oxford University Press 2021-08-19 /pmc/articles/PMC8374372/ /pubmed/34423262 http://dx.doi.org/10.1093/jamiaopen/ooab071 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research and Applications Ganoe, Craig H Wu, Weiyi Barr, Paul J Haslett, William Dannenberg, Michelle D Bonasia, Kyra L Finora, James C Schoonmaker, Jesse A Onsando, Wambui M Ryan, James Elwyn, Glyn Bruce, Martha L Das, Amar K Hassanpour, Saeed Natural language processing for automated annotation of medication mentions in primary care visit conversations |
title | Natural language processing for automated annotation of medication mentions in primary care visit conversations |
title_full | Natural language processing for automated annotation of medication mentions in primary care visit conversations |
title_fullStr | Natural language processing for automated annotation of medication mentions in primary care visit conversations |
title_full_unstemmed | Natural language processing for automated annotation of medication mentions in primary care visit conversations |
title_short | Natural language processing for automated annotation of medication mentions in primary care visit conversations |
title_sort | natural language processing for automated annotation of medication mentions in primary care visit conversations |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374372/ https://www.ncbi.nlm.nih.gov/pubmed/34423262 http://dx.doi.org/10.1093/jamiaopen/ooab071 |
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