Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Dietrich, Georg, Krebs, Jonathan, Liman, Leon, Fette, Georg, Ertl, Maximilian, Kaspar, Mathias, Störk, Stefan, Puppe, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
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
_version_ 1783388610645458944
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
work_keys_str_mv AT dietrichgeorg replicatingmedicationtrendstudiesusingadhocinformationextractioninaclinicaldatawarehouse
AT krebsjonathan replicatingmedicationtrendstudiesusingadhocinformationextractioninaclinicaldatawarehouse
AT limanleon replicatingmedicationtrendstudiesusingadhocinformationextractioninaclinicaldatawarehouse
AT fettegeorg replicatingmedicationtrendstudiesusingadhocinformationextractioninaclinicaldatawarehouse
AT ertlmaximilian replicatingmedicationtrendstudiesusingadhocinformationextractioninaclinicaldatawarehouse
AT kasparmathias replicatingmedicationtrendstudiesusingadhocinformationextractioninaclinicaldatawarehouse
AT storkstefan replicatingmedicationtrendstudiesusingadhocinformationextractioninaclinicaldatawarehouse
AT puppefrank replicatingmedicationtrendstudiesusingadhocinformationextractioninaclinicaldatawarehouse