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Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse
BACKGROUND: Medication trend studies show the changes of medication over the years and may be replicated using a clinical Data Warehouse (CDW). Even nowadays, a lot of the patient information, like medication data, in the EHR is stored in the format of free text. As the conventional approach of info...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339317/ https://www.ncbi.nlm.nih.gov/pubmed/30658633 http://dx.doi.org/10.1186/s12911-018-0729-0 |
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author | Dietrich, Georg Krebs, Jonathan Liman, Leon Fette, Georg Ertl, Maximilian Kaspar, Mathias Störk, Stefan Puppe, Frank |
author_facet | Dietrich, Georg Krebs, Jonathan Liman, Leon Fette, Georg Ertl, Maximilian Kaspar, Mathias Störk, Stefan Puppe, Frank |
author_sort | Dietrich, Georg |
collection | PubMed |
description | BACKGROUND: Medication trend studies show the changes of medication over the years and may be replicated using a clinical Data Warehouse (CDW). Even nowadays, a lot of the patient information, like medication data, in the EHR is stored in the format of free text. As the conventional approach of information extraction (IE) demands a high developmental effort, we used ad hoc IE instead. This technique queries information and extracts it on the fly from texts contained in the CDW. METHODS: We present a generalizable approach of ad hoc IE for pharmacotherapy (medications and their daily dosage) presented in hospital discharge letters. We added import and query features to the CDW system, like error tolerant queries to deal with misspellings and proximity search for the extraction of the daily dosage. During the data integration process in the CDW, negated, historical and non-patient context data are filtered. For the replication studies, we used a drug list grouped by ATC (Anatomical Therapeutic Chemical Classification System) codes as input for queries to the CDW. RESULTS: We achieve an F1 score of 0.983 (precision 0.997, recall 0.970) for extracting medication from discharge letters and an F1 score of 0.974 (precision 0.977, recall 0.972) for extracting the dosage. We replicated three published medical trend studies for hypertension, atrial fibrillation and chronic kidney disease. Overall, 93% of the main findings could be replicated, 68% of sub-findings, and 75% of all findings. One study could be completely replicated with all main and sub-findings. CONCLUSION: A novel approach for ad hoc IE is presented. It is very suitable for basic medical texts like discharge letters and finding reports. Ad hoc IE is by definition more limited than conventional IE and does not claim to replace it, but it substantially exceeds the search capabilities of many CDWs and it is convenient to conduct replication studies fast and with high quality. |
format | Online Article Text |
id | pubmed-6339317 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63393172019-01-23 Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse Dietrich, Georg Krebs, Jonathan Liman, Leon Fette, Georg Ertl, Maximilian Kaspar, Mathias Störk, Stefan Puppe, Frank BMC Med Inform Decis Mak Research Article BACKGROUND: Medication trend studies show the changes of medication over the years and may be replicated using a clinical Data Warehouse (CDW). Even nowadays, a lot of the patient information, like medication data, in the EHR is stored in the format of free text. As the conventional approach of information extraction (IE) demands a high developmental effort, we used ad hoc IE instead. This technique queries information and extracts it on the fly from texts contained in the CDW. METHODS: We present a generalizable approach of ad hoc IE for pharmacotherapy (medications and their daily dosage) presented in hospital discharge letters. We added import and query features to the CDW system, like error tolerant queries to deal with misspellings and proximity search for the extraction of the daily dosage. During the data integration process in the CDW, negated, historical and non-patient context data are filtered. For the replication studies, we used a drug list grouped by ATC (Anatomical Therapeutic Chemical Classification System) codes as input for queries to the CDW. RESULTS: We achieve an F1 score of 0.983 (precision 0.997, recall 0.970) for extracting medication from discharge letters and an F1 score of 0.974 (precision 0.977, recall 0.972) for extracting the dosage. We replicated three published medical trend studies for hypertension, atrial fibrillation and chronic kidney disease. Overall, 93% of the main findings could be replicated, 68% of sub-findings, and 75% of all findings. One study could be completely replicated with all main and sub-findings. CONCLUSION: A novel approach for ad hoc IE is presented. It is very suitable for basic medical texts like discharge letters and finding reports. Ad hoc IE is by definition more limited than conventional IE and does not claim to replace it, but it substantially exceeds the search capabilities of many CDWs and it is convenient to conduct replication studies fast and with high quality. BioMed Central 2019-01-18 /pmc/articles/PMC6339317/ /pubmed/30658633 http://dx.doi.org/10.1186/s12911-018-0729-0 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Dietrich, Georg Krebs, Jonathan Liman, Leon Fette, Georg Ertl, Maximilian Kaspar, Mathias Störk, Stefan Puppe, Frank Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse |
title | Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse |
title_full | Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse |
title_fullStr | Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse |
title_full_unstemmed | Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse |
title_short | Replicating medication trend studies using ad hoc information extraction in a clinical data warehouse |
title_sort | replicating medication trend studies using ad hoc information extraction in a clinical data warehouse |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339317/ https://www.ncbi.nlm.nih.gov/pubmed/30658633 http://dx.doi.org/10.1186/s12911-018-0729-0 |
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