• Fatima Palani
Aim: The purpose of this study is to create a quality mapping to MedDRA, by applying text-mining to detect possible adverse events (AE) and thereby be able to map the terms into an international medical dictionary.
Introduction: Polypharmacy is the most common treatment strategy in diabetic patients, which is associated with various complications; however, the most important is drug-drug interactions (DDI). Pharmacovigilance (PV) is significant in drug development after an approved drug. The Medical Dictionary of Regulatory Activity (MedDRA) was developed to share findings and retrieve the latest information on drugs. A five level hierarchy, very general to very specific, structures it. Every term is identified by an 8-digit number, which describes the term of interest and remains the same number regarding of language. This kind of communications allows researcher to share and understand new findings across different languages, leading to better health globally.
Methods: An automatic computational text mining technique was conducted in 12,073 unstructured electronic patient records (EPR). Natural language processing (NLP) algorithm was executed to structure, understand and validate relevant information. Detected unique terms were mapped into MedDRA in combination with the in-house dictionary, established by International Classification of disease (ICH) to achieve an internationally comparable dataset.
Results: Application of text mining in 12,073 EPRs with NLP gave us 3,830 unique terms, which may be possible AEs. All unique terms were feasible to be mapped into MedDRA. Simple words were easier to be mapped compared to terms that were more complex. Moreover, extraction of the phenotypic profiles, as well as the most common prescribed drugs with associated AEs were able to be extracted.
Conclusion: Based on our results, we conclude that the use of MedDRA as our dictionary in combination with the in-house NER-tagger were possible to use and can give a sophisticated analysis on a Scandinavian as well as international level. Moreover, application of text mining detect possible AE, and is a great tool when dealing with large databases, which is the case in most safety trails.
Publication date30 Sep 2019
Number of pages38
External collaboratorNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
Søren Brunak soren.brunak@cpr.ku.dk
ID: 311896284