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Verfasst von:Kusch, Marcel [VerfasserIn]   i
 Zien, Alexander [VerfasserIn]   i
 Hachenberg, Christian [VerfasserIn]   i
 Haefeli, Walter E. [VerfasserIn]   i
 Seidling, Hanna [VerfasserIn]   i
Titel:Information on adverse drug reactions
Titelzusatz:proof of principle for a structured database that allows customization of drug information
Verf.angabe:Marcel K. P. Kusch, Alexander Zien, Christian Hachenberg, Walter E. Haefeli, Hanna M. Seidling
Jahr:2020
Jahr des Originals:2019
Fussnoten:Available online 16 September 2019 ; Gesehen am 10.01.2020
Titel Quelle:Enthalten in: International journal of medical informatics
Ort Quelle:Amsterdam [u.a.] : Elsevier, 1997
Jahr Quelle:2020
Band/Heft Quelle:133(2020) Artikel-Nummer 103970, 9 Seiten
ISSN Quelle:1872-8243
Abstract:Background - The drug information most commonly requested by patients is to learn more about potential adverse drug reactions (ADRs) of their drugs. Such information should be customizable to individual information needs. While approaches to automatically aggregate ADRs by text-mining processes and establishment of respective databases are well known, further efforts to map additional ADR information are sparse, yet crucial for customization. In a proof-of-principle (PoP) study, we developed a database format demonstrating that natural language processing can further structure ADR information in a way that facilitates customization. - Methods - We developed the database in a 3-step process: (1) initial ADR extraction, (2) mapping of additional ADR information, and (3) review process. ADRs of 10 frequently prescribed active ingredients were initially extracted from their Summary of Product Characteristics (SmPC) by text-mining processes and mapped to Medical Dictionary for Regulatory Activities (MedDRA) terms. To further structure ADR information, we mapped 7 additional ADR characteristics (i.e. frequency, organ class, seriousness, lay perceptibility, onset, duration, and management strategies) to individual ADRs. In a PoP study, the process steps were assessed and tested. Initial ADR extraction was assessed by measuring precision, recall, and F1-scores (i.e. harmonic mean of precision and recall). Mapping of additional ADR information was assessed considering pre-defined parameters (i.e. correctness, errors, and misses) regarding the mapped ADR characteristics. - Results - Overall the SmPCs listed 393 ADRs with an average of 39.3±18.1 ADRs per SmPC. For initial ADR extraction precision was 97.9% and recall was 93.2% leading to an F1-score of 95.5%. Regarding mapping of additional ADR information, the frequency information of 28.6±18.4 ADRs for each SmPC was correctly mapped (72.8%). Overall 77 ADRs (20.6%) of the correctly extracted ADRs did not have a concise frequency stated in the SmPC and were consequently mapped with ‘frequency not known’. Mapping of remaining ADR characteristics did not result in noteworthy errors or misses. - Conclusion - ADR information can be automatically extracted and mapped to corresponding MedDRA terms. Additionally, ADR information can be further structured considering additional ADR characteristics to facilitate customization to individual patient needs.
DOI:doi:10.1016/j.ijmedinf.2019.103970
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1016/j.ijmedinf.2019.103970
 Volltext: http://www.sciencedirect.com/science/article/pii/S1386505619307026
 DOI: https://doi.org/10.1016/j.ijmedinf.2019.103970
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Adverse drug reactions
 MedDRA
 Natural language processing
 Patient empowerment
 Structured drug information
 Summary of product characteristics
K10plus-PPN:1687139849
Verknüpfungen:→ Zeitschrift

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