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

Koutkias VG, Jaulent MC - Drug Saf (2015)

Bottom Line: These findings reinforce the arguments for exploiting all possible information sources for drug safety and the parallel use of multiple signal detection methods.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.

View Article: PubMed Central - PubMed

Affiliation: INSERM, U1142, LIMICS, Campus des Cordeliers, 15 rue de l' École de Médecine, 75006, Paris, France, vasileios.koutkias@inserm.fr.

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.

Show MeSH
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
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Fig1: 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

Mentions: As illustrated in Fig. 1, combinatorial signal detection scales up from (a) studies where one data source is being explored by a single computational method (in a kind of ‘coupled’ setting); (b) benchmarking studies (in which one data source is being explored by various methods to enable comparisons in the methods’ performance); and (c) studies focusing on replication of outcomes (i.e. one method applied to various data sources, typically of the same type), to settings where multiple data sources are explored by diverse methods. This may be seen as a natural evolution thanks to the increase in data availability for analysis, the development of more efficient computational methods, and the need for more accurate and evidence-based assessments.Fig. 1


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

Koutkias VG, Jaulent MC - Drug Saf (2015)

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
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4374117&req=5

Fig1: 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
Mentions: As illustrated in Fig. 1, combinatorial signal detection scales up from (a) studies where one data source is being explored by a single computational method (in a kind of ‘coupled’ setting); (b) benchmarking studies (in which one data source is being explored by various methods to enable comparisons in the methods’ performance); and (c) studies focusing on replication of outcomes (i.e. one method applied to various data sources, typically of the same type), to settings where multiple data sources are explored by diverse methods. This may be seen as a natural evolution thanks to the increase in data availability for analysis, the development of more efficient computational methods, and the need for more accurate and evidence-based assessments.Fig. 1

Bottom Line: These findings reinforce the arguments for exploiting all possible information sources for drug safety and the parallel use of multiple signal detection methods.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.

View Article: PubMed Central - PubMed

Affiliation: INSERM, U1142, LIMICS, Campus des Cordeliers, 15 rue de l' École de Médecine, 75006, Paris, France, vasileios.koutkias@inserm.fr.

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.

Show MeSH