TY - JOUR

T1 - Using text-mining techniques in electronic patient records to identify ADRs from medicine use

AU - Warrer, Pernille

AU - Hansen, Ebba Holme

AU - Jensen, Lars Juhl

AU - Aagaard, Lise

N1 - © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

PY - 2012/5

Y1 - 2012/5

N2 - 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 Abstracts 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.

AB - 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 Abstracts 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.

KW - Adverse Drug Reaction Reporting Systems

KW - Algorithms

KW - Data Mining

KW - Humans

KW - Medical Records Systems, Computerized

KW - Natural Language Processing

KW - Pharmaceutical Preparations

KW - Pharmacovigilance

U2 - 10.1111/j.1365-2125.2011.04153.x

DO - 10.1111/j.1365-2125.2011.04153.x

M3 - Journal article

C2 - 22122057

VL - 73

SP - 674

EP - 684

JO - British Journal of Clinical Pharmacology, Supplement

JF - British Journal of Clinical Pharmacology, Supplement

SN - 0264-3774

IS - 5

ER -