Causality assessment of adverse drug reaction reports using an expert-defined Bayesian network

Publication date: Available online 2 August 2018Source: Artificial Intelligence in MedicineAuthor(s): Pedro Pereira Rodrigues, Daniela Ferreira-Santos, Ana Silva, Jorge Polónia, Inês Ribeiro-VazAbstractIn pharmacovigilance, reported cases are considered suspected adverse drug reactions (ADR). Health authorities have thus adopted structured causality assessment methods, allowing the evaluation of the likelihood that a drug was the causal agent of an adverse reaction. The aim of this work was to develop and validate a new causality assessment support system used in a regional pharmacovigilance centre. A Bayesian network was developed, for which the structure was defined by experts while the parameters were learnt from 593 completely filled ADR reports evaluated by the Portuguese Northern Pharmacovigilance Centre medical expert between 2000 and 2012. Precision, recall and time to causality assessment (TTA) was evaluated, according to the WHO causality assessment guidelines, in a retrospective cohort of 466 reports (April–September 2014) and a prospective cohort of 1041 reports (January–December 2015). Additionally, a simplified assessment matrix was derived from the model, enabling its preliminary direct use by notifiers. Results show that the network was able to easily identify the higher levels of causality (recall above 80%), although struggling to assess reports with a lower level of causality. Nonetheless, the median (Q1:Q3) TTA was 4 (2:8) days using the network and ...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research