Supervised signal detection for adverse drug reactions in medication dispensing data

Computer Methods and Programs in Biomedicine
Tao HoangJiuyong Li

Abstract

Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality and thus should be detected early to reduce consequences on health outcomes. Medication dispensing data are comprehensive sources of information about medicine uses that can be utilized for the signal detection of ADRs. Sequence symmetry analysis (SSA) has been employed in previous studies to detect signals of ADRs from medication dispensing data, but it has a moderate sensitivity and tends to miss some ADR signals. With successful applications in various areas, supervised machine learning (SML) methods are promising in detecting ADR signals. Gold standards of known ADRs and non- ADRs from previous studies create opportunities to take into account additional domain knowledge to improve ADR signal detection with SML. We assess the utility of SML as a signal detection tool for ADRs in medication dispensing data with the consideration of domain knowledge from DrugBank and MedDRA. We compare the best performing SML method with SSA. We model the ADR signal detection problem as a supervised machine learning problem by linking medication dispensing data with domain knowledge bases. Suspected ADR signals are extracted from the Australian Pharmaceutic...Continue Reading

Citations

Jan 1, 2019·Current Epidemiology Reports·Nicole Pratt, Elizabeth Roughead
Jun 25, 2020·Drug Safety : an International Journal of Medical Toxicology and Drug Experience·Clare E KingEirene C Behm
Jan 11, 2019·BioMed Research International·Minhui WangJiajia Chen
Aug 12, 2021·Research in Social & Administrative Pharmacy : RSAP·Earl J MorrisScott M Vouri
Jan 7, 2022·Journal of the American Medical Informatics Association : JAMIA·Siru LiuSamir Abdelrahman

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