Using text‐mining techniques in electronic patient records to identify ADRs from medicine use

This literature review included studies that use text‐mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical s without restrictions from origin until July 2011. We included...

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Bibliographic Details
Published in:British journal of clinical pharmacology 2012-05, Vol.73 (5), p.674-684
Main Authors: Warrer, Pernille, Hansen, Ebba Holme, Juhl‐Jensen, Lars, Aagaard, Lise
Format: Article
Language:English
Subjects:
Adverse Drug Reaction Reporting Systems - statistics & numerical data
adverse drug reactions
Algorithms
Biological and medical sciences
Data Mining - methods
Drug-Related Side Effects and Adverse Reactions
electronic patient records
Humans
Medical Records Systems, Computerized - statistics & numerical data
Medical sciences
Natural Language Processing
Pharmacology. Drug treatments
Pharmacovigilance
Reviews
text mining
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Summary:This literature review included studies that use text‐mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical s without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text‐mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text‐mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs.
ISSN:0306-5251
1365-2125
DOI:10.1111/j.1365-2125.2011.04153.x