Computational approaches for pharmacovigilance signal detection: toward integrated and semantically-enriched frameworks

Abstract

Computational signal detection constitutes a key element of postmarketing drug monitoring and surveillance. Diverse data sources are considered within the 'search space' of pharmacovigilance scientists, and respective data analysis methods are employed, all with their qualities and shortcomings, towards more timely and accurate signal detection. Recent systematic comparative studies highlighted not only event-based and data-source-based differential performance across methods but also their complementarity. These findings reinforce the arguments for exploiting all possible information sources for drug safety and the parallel use of multiple signal detection methods. Combinatorial signal detection has been pursued in few studies up to now, employing a rather limited number of methods and data sources but illustrating well-promising outcomes. However, the large-scale realization of this approach requires systematic frameworks to address the challenges of the concurrent analysis setting. In this paper, we argue that semantic technologies provide the means to address some of these challenges, and we particularly highlight their contribution in (a) annotating data sources and analysis methods with quality attributes to facilitate their selection given the analysis scope; (b) consistently defining study parameters such as health outcomes and drugs of interest, and providing guidance for study setup; (c) expressing analysis outcomes in a common format enabling data sharing and systematic comparisons; and (d) assessing/supporting the novelty of the aggregated outcomes through access to reference knowledge sources related to drug safety. A semantically-enriched framework can facilitate seamless access and use of different data sources and computational methods in an integrated fashion, bringing a new perspective for large-scale, knowledge-intensive signal detection.

Fig. 1

Scaling-up computational signal detection towards combinatorial-integrated approaches: a the quite typical approach of one data source being explored by a single method in a ‘coupled’ fashion; b the benchmarking setting, i.e. one data source explored by various methods to enable the methods’ comparison; c studies assessing replication of outcomes, i.e. one method applied to various data sources (typically of the same type); and d the integrated perspective, i.e. various data sources of different types explored by diverse methods in parallel

Fig. 2

Overview of the computational signal detection process in an integrated perspective. a Diverse data sources; b relevant computational signal detection methods per data source type; c the signal detection workflow; d stakeholders involved in signal detection for whom uniform-combined access to data and computational methods for signal detection shall be provided; e proposed add-ons for semantically-enriched, large-scale signal detection. ATC Anatomical Therapeutic Chemical classification system, EHR electronic health record, MedDRA Medical Dictionary for Regulatory Activities, NLP Natural Language Processing, OMOP Observational Medical Outcomes Partnership, SRS spontaneous reporting system