Application of Natural Language Processing and Network Analysis Techniques to Post-market Reports for the Evaluation of Dose-related Anti-Thymocyte Globulin Safety Patterns
06 October 2016
15 February 2017
21 December 2017 (online)
Objective: To evaluate the feasibility of automated dose and adverse event information retrieval in supporting the identification of safety patterns.
Methods: We extracted all rabbit Anti-Thymocyte Globulin (rATG) reports submitted to the United States Food and Drug Administration Adverse Event Reporting System (FAERS) from the product’s initial licensure in April 16, 1984 through February 8, 2016. We processed the narratives using the Medication Extraction (MedEx) and the Event-based Text-mining of Health Electronic Records (ETHER) systems and retrieved the appropriate medication, clinical, and temporal information. When necessary, the extracted information was manually curated. This process resulted in a high quality dataset that was analyzed with the Pattern-based and Advanced Network Analyzer for Clinical Evaluation and Assessment (PANACEA) to explore the association of rATG dosing with post-transplant lymphoproliferative disorder (PTLD).
Results: Although manual curation was necessary to improve the data quality, MedEx and ETHER supported the extraction of the appropriate information. We created a final dataset of 1,380 cases with complete information for rATG dosing and date of administration. Analysis in PANACEA found that PTLD was associated with cumulative doses of rATG >8 mg/kg, even in periods where most of the submissions to FAERS reported low doses of rATG.
Conclusion: We demonstrated the feasibility of investigating a dose-related safety pattern for a particular product in FAERS using a set of automated tools.
Citation: Botsis T, Foster M, Arya N, Kreimeyer K, Pandey A, Arya D. Application of natural language processing and network analysis techniques to post-market reports for the evaluation of dose-related anti-thymocyte globulin safety patterns. Appl Clin Inform 2017; 8: 396–411 https://doi.org/10.4338/ACI-2016-10-RA-0169
KeywordsNatural language processing - network analysis - postmarketing product surveillance - information retrieval - data visualization
Clinical Relevance Statement
• The automated retrieval of medication and other clinical information from the United States Food and Drug Administration Adverse Event Reporting System (FAERS) is critical for pharma-coepidemiological analysis.
• Natural language processing can be combined with other approaches, such as network analysis, to support the evaluation of safety patterns associated with medical product administration.
• The use of advanced techniques in the decision making process may assist medical experts and epidemiologists in performing their routine safety surveillance tasks.
Protection of Human and Animal Subjects
Human and/or animal subjects were not included in the project.
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