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Drug Safety
Published in: Drug Safety 11/2017

01-11-2017 | Review Article

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review

Authors: Yuan Luo, William K. Thompson, Timothy M. Herr, Zexian Zeng, Mark A. Berendsen, Siddhartha R. Jonnalagadda, Matthew B. Carson, Justin Starren

Published in: Drug Safety | Issue 11/2017

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Abstract

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. The ability to accurately capture both semantic and syntactic structures in clinical narratives becomes increasingly critical to enable efficient and accurate ADE detection. Significant progress has been made in algorithm development and resource construction since 2000. Since 2012, statistical analysis and machine learning methods have gained traction in automation of ADE mining from EHR narratives. Current state-of-the-art methods for NLP-based ADE detection from EHRs show promise regarding their integration into production pharmacovigilance systems. In addition, integrating multifaceted, heterogeneous data sources has shown promise in improving ADE detection and has become increasingly adopted. On the other hand, challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-use ADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.
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Literature
1.
Onder G, et al. Adverse drug reactions as cause of hospital admissions: results from the Italian Group of Pharmacoepidemiology in the Elderly (GIFA) (in English). J Am Geriatr Soc. 2002;50(12):1962–8.CrossRefPubMed
2.
3.
4.
Pouliot Y, Chiang AP, Butte AJ. Predicting adverse drug reactions using publicly available PubChem BioAssay data (in English). Clin Pharmacol Ther. 2011;90(1):90–9.CrossRefPubMedPubMedCentral
5.
Zheng HR, Wang HY, Xu H, Wu YH, Zhao ZM, Azuaje F. Linking biochemical pathways and networks to adverse drug reactions (in English). IEEE Trans Nanobiosci. 2014;13(2):131–7.CrossRef
6.
Liu M, et al. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs (in English). J Am Med Inform Assoc. 2012;19(E1):E28–35.CrossRefPubMedPubMedCentral
7.
Harpaz R, et al. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. J Am Med Inform Assoc. 2013;20(3):413–9.CrossRefPubMed
8.
Boland MR, Tatonetti NP. Are all vaccines created equal? Using electronic health records to discover vaccines associated with clinician-coded adverse events. AMIA Summits Transl Sci Proc. 2015;2015:196.PubMedPubMedCentral
9.
10.
Classen DC, et al. ‘Global trigger tool’ shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff. 2011;30(4):581–9.CrossRef
11.
Coloma PM, Trifirò G, Patadia V, Sturkenboom M. Postmarketing safety surveillance. Drug Saf. 2013;36(3):183–97.CrossRefPubMed
12.
Doupi P. Using EHR data for monitoring and promoting patient safety: reviewing the evidence on trigger tools. Stud Health Technol Inform. 2011;180:786–90.
13.
Gonzalez GH, Tahsin T, Goodale BC, Greene AC, Greene CS. Recent advances and emerging applications in text and data mining for biomedical discovery. Brief Bioinform. 2016;17(1):33–42.CrossRefPubMed
14.
Luo Y, Uzuner Ö, Szolovits P. Bridging semantics and syntax with graph algorithms—state-of-the-art of extracting biomedical relations. Brief Bioinform. 2016;18(1):160–78.CrossRefPubMed
15.
Cohen KB, Demner-Fushman D. Biomedical natural language processing. Amsterdam: John Benjamins Publishing Company; 2014.CrossRef
16.
17.
Luo Y, Riedlinger G, Szolovits P. Text mining in cancer gene and pathway prioritization. Cancer informatics. 2014;(Suppl. 1):69–79.
18.
Thomson Reuters. EndNote, X7.5 ed. New York: Thomson Reuters; 2016.
19.
Veritas Health Innovation. Covidence systematic review software, ed. Melbourne: Veritas Health Innovation; 2016.
20.
Manning CD, Raghavan P, Schütze H. Introduction to information retrieval, vol. 1. Cambridge: Cambridge University Press; 2008.CrossRef
21.
22.
Honigman B, Light P, Pulling RM, Bates DW. A computerized method for identifying incidents associated with adverse drug events in outpatients. Int J Med Inform. 2001;61(1):21–32.CrossRefPubMed
23.
Gurwitz JH, et al. Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA. 2003;289(9):1107–16.CrossRefPubMed
24.
Murff HJ, Forster AJ, Peterson JF, Fiskio JM, Heiman HL, Bates DW. Electronically screening discharge summaries for adverse medical events. J Am Med Inform Assoc. 2003;10(4):339–50.CrossRefPubMedPubMedCentral
25.
Cantor MN, Feldman HJ, Triola MM. Using trigger phrases to detect adverse drug reactions in ambulatory care notes. Qual Saf Health Care. 2007;16(2):132–4.CrossRefPubMedPubMedCentral
26.
Chazard E, Baceanu A, Ferret L, Ficheur G. The ADE scorecards: a tool for adverse drug event detection in electronic health records. Stud Health Technol Inform. 2011;166:169–79.PubMed
27.
Chazard E, Ficheur G, Bernonville S, Luyckx M, Beuscart R. Data mining to generate adverse drug events detection rules. IEEE Trans Inf Technol Biomed. 2011;15(6):823–30.CrossRefPubMed
28.
Ballard J, Rosenman M, Weiner M. Harnessing a health information exchange to identify surgical device adverse events for urogynecologic mesh. AMIA Annu Symp Proc. 2012;2012:1109–18.PubMedPubMedCentral
29.
Ferrajolo C, et al. Idiopathic acute liver injury in paediatric outpatients: incidence and signal detection in two European countries. Drug Saf. 2013;36(10):1007–16.CrossRefPubMed
30.
Ferrajolo C, et al. Signal detection of potentially drug-induced acute liver injury in children using a multi-country healthcare database network. Drug Saf. 2014;37(2):99–108.CrossRefPubMed
31.
Pathak J, Kiefer RC, Chute CG. Using linked data for mining drug–drug interactions in electronic health records. Stud Health Technol Inform. 2013;192:682–6.PubMedPubMedCentral
32.
Pathak J, Kiefer RC, Chute CG. Mining drug–drug interaction patterns from linked data: a case study for warfarin, clopidogrel, and simvastatin. 2013 IEEE International Conference on Bioinformatics and Biomedicine, 2013.
33.
Haber P et al. Post-Licensure surveillance of trivalent live-attenuated influenza vaccine in children aged 2–18 years, Vaccine Adverse Event Reporting System, United States, July 2005–June 2012. J Pediatr Infect Dis Soc. 2015;4(3):205–13.CrossRef
34.
International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). (Aug 31). Medical dictionary for regulatory activities. http://​www.​meddra.​org/​.
35.
Bates DW, Evans RS, Murff H, Stetson PD, Pizziferri L, Hripcsak G. Detecting adverse events using information technology. J Am Med Inform Assoc JAMIA. 2003;10(2):115–28.CrossRefPubMed
36.
Hazlehurst B, Naleway A, Mullooly J. Detecting possible vaccine adverse events in clinical notes of the electronic medical record. Vaccine. 2009;27(14):2077–83.CrossRefPubMed
37.
Hazlehurst B, Mullooly J, Naleway A, Crane B. Detecting possible vaccination reactions in clinical notes. AMIA Annu Symp Proc, vol. Annual Symposium Proceedings/AMIA Symposium, p. 306–10, 2005.
38.
Hazlehurst B, Frost HR, Sittig DF, Stevens VJ. MediClass: a system for detecting and classifying encounter-based clinical events in any electronic medical record. J Am Med Inform Assoc. 2005;12(5):517–29.CrossRefPubMedPubMedCentral
39.
Sohn S, Kocher JP, Chute CG, Savova GK. Drug side effect extraction from clinical narratives of psychiatry and psychology patients. J Am Med Inform Assoc JAMIA. 2011;18(Suppl 1):i144–9.CrossRefPubMed
40.
41.
Epstein RH, St Jacques P, Stockin M, Rothman B, Ehrenfeld JM, Denny JC. Automated identification of drug and food allergies entered using non-standard terminology. J Am Med Inform Assoc JAMIA. 2013;20(5):962–8.CrossRefPubMed
42.
Eriksson R, Jensen PB, Frankild S, Jensen LJ, Brunak S. Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text. J Am Med Inform Assoc. 2013;20(5):947–53.CrossRefPubMedPubMedCentral
43.
Eriksson R, Werge T, Jensen LJ, Brunak S. Dose-specific adverse drug reaction identification in electronic patient records: temporal data mining in an inpatient psychiatric population. [Erratum appears in Drug Saf. 2014 May; 37(5):379]. Drug Saf. 2014;37(4):237–47.CrossRefPubMedPubMedCentral
44.
Roitmann E, Eriksson R, Brunak S. Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events. Front Physiol. 2014;5:332.CrossRefPubMedPubMedCentral
45.
Nadeau D, Sekine S. A survey of named entity recognition and classification. Lingvisticae Investigationes. 2007;30(1):3–26.CrossRef
46.
Uzuner Ö, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc. 2011;18(5):552–6.CrossRefPubMedPubMedCentral
47.
Friedman C, Shagina L, Lussier Y, Hripcsak G. Automated encoding of clinical documents based on natural language processing. J Am Med Inform Assoc. 2004;11(5):392–402.CrossRefPubMedPubMedCentral
48.
Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of the AMIA Symposium, 2001, p. 17, American Medical Informatics Association.
49.
Savova GK, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010;17(5):507–13.CrossRefPubMedPubMedCentral
50.
Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc. 2010;17(1):19–24.CrossRefPubMedPubMedCentral
51.
Cunningham H. GATE, a general architecture for text engineering. Comput Humanit. 2002;36(2):223–54.CrossRef
52.
Melton G, Hripcsak G. Automated detection of adverse events using natural language processing of discharge summaries. J Am Med Inform Assoc. 2005;12(4):448–57.CrossRefPubMedPubMedCentral
53.
Penz JFE, Wilcox AB, Hurdle JF. Automated identification of adverse events related to central venous catheters. J Biomed Inform. 2007;40(2):174–82.CrossRefPubMed
54.
Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc. 2009;16(3):328–37.CrossRefPubMedPubMedCentral
55.
Wang X, Chase H, Markatou M, Hripcsak G, Friedman C. Selecting information in electronic health records for knowledge acquisition. J Biomed Inform. 2010;43(4):595–601.CrossRefPubMedPubMedCentral
56.
Friedman C. Discovering novel adverse drug events using natural language processing and mining of the electronic health record. Artif Intell Med Proc. 2009;5651:1–5.CrossRef
57.
Haerian K, Varn D, Vaidya S, Ena L, Chase HS, Friedman C. Detection of pharmacovigilance-related adverse events using electronic health records and automated methods. Clin Pharmacol Ther. 2012;92(2):228–34.CrossRefPubMedPubMedCentral
58.
Gysbers M et al. Natural language processing to identify adverse drug events. AMIA Annu Symp Proc, vol. Annual Symposium Proceedings/AMIA Symposium, p. 961, 2008.
59.
LePendu P, Iyer SV, Fairon C, Shah NH. Annotation analysis for testing drug safety signals using unstructured clinical notes. J Biomed Semant. 2012;3(Suppl 1):S5.
60.
Shah NH, Bhatia N, Jonquet C, Rubin D, Chiang AP, Musen MA. Comparison of concept recognizers for building the Open Biomedical Annotator. BMC Bioinform. 2009;10(Suppl 9):S14.CrossRef
61.
Banerjee R, Ramakrishnan IV, Henry M, Perciavalle M. Patient centered identification, attribution, and ranking of adverse drug events. In: International Conference on Healthcare Informatics 2015, p.18–27.
62.
Tsuruoka Y, Tsujii JI. Bidirectional inference with the easiest-first strategy for tagging sequence data. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, 2005, p. 467–74, Association for Computational Linguistics.
63.
Liu Y, LePendu P, Iyer S, Shah NH. Using temporal patterns in medical records to discern adverse drug events from indications. AMIA Summits Transl Sci Proc. 2012;2012:47–56.PubMedPubMedCentral
64.
Whetzel PL, et al. BioPortal: enhanced functionality via new web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res. 2011;39(suppl 2):W541–5.CrossRefPubMedPubMedCentral
65.
Gerdes LU, Hardahl C. Text mining electronic health records to identify hospital adverse events. Stud Health Technol Inform. 2013;192:1145.PubMed
66.
Wei WQ, et al. Creation and validation of an EMR-based algorithm for identifying major adverse cardiac events while on statins. AMIA Summits Transl Sci Proc. 2014;2014:112–9.PubMedPubMedCentral
67.
Iqbal E, et al. Identification of adverse drug events from free text electronic patient records and information in a large mental health case register. PLoS One. 2015;10(8):e0134208.CrossRefPubMedPubMedCentral
68.
69.
Bui Q-C, Sloot PM, Van Mulligen EM, Kors JA. A novel feature-based approach to extract drug–drug interactions from biomedical text. Bioinformatics. 2014;30(23):3365–71.CrossRefPubMed
70.
Wang G, Jung K, Winnenburg R, Shah NH. A method for systematic discovery of adverse drug events from clinical notes. J Am Med Inform Assoc. 2015;22(6):1196–204.CrossRefPubMedPubMedCentral
71.
Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform. 2001;34(5):301–10.CrossRefPubMed
72.
Chapman WW, Chu D, Dowling JN. ConText: an algorithm for identifying contextual features from clinical text. In: Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, 2007, p. 81–88, Association for Computational Linguistics.
73.
Cao F, Sun X, Wang X, Li B, Li J, Pan Y. Ontology-based knowledge management for personalized adverse drug events detection. Stud Health Technol Inform. 2010;169:699–703.
74.
Avillach P, et al. Harmonization process for the identification of medical events in eight European healthcare databases: the experience from the EU-ADR project. J Am Med Inform Assoc JAMIA. 2013;20(1):184–92.CrossRefPubMed
75.
Iyer SV, Harpaz R, LePendu P, Bauer-Mehren A, Shah NH. Mining clinical text for signals of adverse drug–drug interactions. J Am Med Inform Assoc. 2014;21(2):353–62.CrossRefPubMed
76.
Kamdar MR, Tudorache T, Musen MA. A systematic analysis of term reuse and term overlap across biomedical ontologies. Semantic Web, no. Preprint, p. 1–19, 2016.
77.
Wasserman L. All of statistics: a concise course in statistical inference. New York: Springer; 2013.
78.
Bishop CM. Pattern recognition. Mach Learn. 2006;128:1–58.
79.
Visweswaran S, Hanbury P, Saul M, Cooper GF. Detecting adverse drug events in discharge summaries using variations on the simple Bayes model. AMIA Annu Symp Proc, vol. Annual Symposium Proceedings/AMIA Symposium, p. 689–93, 2003.
80.
Wang X, Hripcsak G, Friedman C. Characterizing environmental and phenotypic associations using information theory and electronic health records. BMC Bioinform. 2009;10(Suppl 9):S13.CrossRef
81.
LePendu P, et al. Pharmacovigilance using clinical notes. Clin Pharmacol Ther. 2013;93(6):547–55.CrossRefPubMed
82.
83.
Schuemie MJ. Methods for drug safety signal detection in longitudinal observational databases: LGPS and LEOPARD. Pharmacoepidemiol Drug Saf. 2011;20(3):292–9.CrossRefPubMed
84.
Banda JM, et al. Feasibility of prioritizing drug–drug-event associations found in electronic health records. Drug Saf. 2016;39(1):45–57.CrossRefPubMed
85.
Aramaki E, et al. Extraction of adverse drug effects from clinical records. Stud Health Technol Inform. 2010;160(Pt 1):739–43.PubMed
86.
Baeza-Yates R, Ribeiro-Neto B. Modern information retrieval. New York: ACM Press; 1999.
87.
Henriksson A, Kvist M, Dalianis H, Duneld M. Identifying adverse drug event information in clinical notes with distributional semantic representations of context. J Biomed Inform. 2015;57:333–49.CrossRefPubMed
88.
Henriksson A, Zhao J, Bostrom H, Dalianis H. Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015, p. 343–50.
89.
90.
Wang X, et al. SMDM: enhancing enterprise-wide master data management using semantic web technologies. Proc VLDB Endow. 2009;2(2):1594–7.CrossRef
91.
Knox C, et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res. 2011;39(suppl 1):D1035–41.CrossRefPubMed
92.
Luo Y, Sohani AR, Hochberg EP, Szolovits P. Automatic lymphoma classification with sentence subgraph mining from pathology reports. J Am Med Inform Assoc. 2014;21(5):824–32.CrossRefPubMedPubMedCentral
93.
Boland MR, Hripcsak G, Shen Y, Chung WK, Weng C. Defining a comprehensive verotype using electronic health records for personalized medicine. J Am Med Inform Assoc. 2013;20(e2):e232–8.CrossRefPubMedPubMedCentral
94.
Radley DC, Finkelstein SN, Stafford RS. Off-label prescribing among office-based physicians. Arch Intern Med. 2006;166(9):1021–6.CrossRefPubMed
95.
Flowers CM, Racoosin JA, Kortepeter C. Seizure activity and off-label use of tiagabine. N Engl J Med. 2006;354(7):773–4.CrossRefPubMed
96.
Carmona L, Descalzo MA, Ruiz-Montesinos D, Manero-Ruiz FJ, Perez-Pampin E, Gomez-Reino JJ. Safety and retention rate of off-label uses of TNF antagonists in rheumatic conditions: data from the Spanish registry BIOBADASER 2.0. Rheumatology. 2011;50(1):85–92.CrossRefPubMed
97.
Dal Pan GJ. Monitoring the safety of medicines used off-label. Clin Pharmacol Ther. 2012;91(5):787–95.CrossRefPubMed
98.
Epstein RS, Huang SM. The many sides of off-label prescribing (in English). Clin Pharmacol Ther. 2012;91(5):755–8.CrossRefPubMed
99.
Stafford RS. Off-label use of drugs and medical devices: a review of policy implications. Clin Pharmacol Ther. 2012;91(5):920–25.CrossRefPubMed
100.
Kimland E, Odlind V. Off-label drug use in pediatric patients. Clin Pharmacol Ther. 2012;91(5):796–801.CrossRefPubMed
101.
Morris J. The use of observational health-care data to identify and report on off-label use of biopharmaceutical products. Clin Pharmacol Ther. 2012;91(5):937–42.CrossRefPubMed
102.
Leong R, et al. Regulatory experience with physiologically based pharmacokinetic modeling for pediatric drug trials. Clin Pharmacol Ther. 2012;91(5):926–31.CrossRefPubMed
103.
Teagarden JR, Dreitlein WB, Kourlas H, Nichols L. Influence of pharmacy benefit practices on off-label dispensing of drugs in the United States (in English). Clin Pharmacol Ther. 2012;91(5):943–5.CrossRefPubMed
104.
105.
LePendu P, Liu Y, Iyer S, Udell MR, Shah NH. Analyzing patterns of drug use in clinical notes for patient safety. AMIA Summits Transl Sci Proc. 2012;2012:63–70.PubMedPubMedCentral
106.
107.
Huang C-C, Lu Z. Community challenges in biomedical text mining over 10 years: success, failure and the future. Brief Bioinform. 2016;17(1):132–44.CrossRefPubMed
108.
Segura Bedmar I, Martínez P, Herrero Zazo M. SemEval-2013 task 9: extraction of drug–drug interactions from biomedical texts (DDIExtraction 2013). Association for Computational Linguistics; 2013.
109.
Segura Bedmar I, Martinez P, Sánchez Cisneros D. The 1st DDIExtraction-2011 challenge task: extraction of drug–drug interactions from biomedical texts. In: proceedings of the 1st challenge task on Drug-Drug Interaction Extraction (DDIExtraction 2011), p. 1–9, Huelva, Spain, 2011.
110.
111.
Qato DM, Wilder J, Schumm LP, Gillet V, Alexander GC. Changes in prescription and over-the-counter medication and dietary supplement use among older adults in the United States, 2005 vs 2011. JAMA Intern Med. 2016;176(4):473–82.CrossRefPubMedPubMedCentral
112.
Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005;294(6):716–24.CrossRefPubMed
113.
Steinman MA, Miao Y, Boscardin WJ, Komaiko KD, Schwartz JB. Prescribing quality in older veterans: a multifocal approach. J Gen Intern Med. 2014;29(10):1379–86.CrossRefPubMedPubMedCentral
Metadata
Title
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review
Authors
Yuan Luo
William K. Thompson
Timothy M. Herr
Zexian Zeng
Mark A. Berendsen
Siddhartha R. Jonnalagadda
Matthew B. Carson
Justin Starren
Publication date
01-11-2017
Publisher
Springer International Publishing
Published in
Drug Safety / Issue 11/2017
Print ISSN: 0114-5916
Electronic ISSN: 1179-1942
DOI
https://doi.org/10.1007/s40264-017-0558-6

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