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[bioontology-support] CFP on "Computational Approaches for Signal Detection and Management in Pharmacovigilance", Frontiers in Pharmacology

Vassilis Koutkias vkoutkias at
Thu Jun 8 06:08:08 PDT 2017

/[Apologies if you receive multiple copies of this message]/
*Call for Papers - Research Topic: "**Computational Approaches for 
Signal Detection and Management in Pharmacovigilance**"
*Frontiers in Pharmacology 
<> (*Impact factor 

Historically, the main knowledge source for pharmacovigilance consists 
of spontaneous case reports issued by healthcare professionals and 
patients and these case reports are usually manually reviewed by 
evaluators in a qualitative way. Pharmacovigilance has for a long time 
been confined to a role of passive surveillance of adverse drug 
reactions (ADRs), which means that measures to look for ADRs are limited 
to the encouragement of health professionals and others to report safety 
issues in order to alert the health authorities. This role is 
indispensable but should now be associated to a “pro-active” approach, 
in order to identify potential problems and safety risks before they 
emerge into crises. Additionally, under declaration is a major drawback 
limiting the ability of pharmacovigilance to process the evaluation of 
case reports and to detect early signals.

In this context, the development of new data analytic methods and the 
availability of more efficient computing resources allows considering 
new data sources for pharmacovigilance, such as lectronic health 
records, administrative claims systems, case-mix databases, social 
media, search log data and the medical literature. New data mining 
techniques are being implemented in order to detect safety signals in 
dominant pharmacovigilance data sources as well as in emerging data 
sources. Notable systematic efforts relevant with the Research Topic 
have already been conducted in projects such as the Observational 
Medical Outcomes Partnership (OMOP) <>, 
the Pharmacoepidemiological Research on Outcomes of Therapeutics by a 
European Consortium (PROTECT) 
<>, the EU-ADR European 
project , the U.S. Food 
and Drug Administration’s Mini-Sentinel 
<>, and the European Web-RADR 
project <>.

The objective of this Research Topic is to describe the state of 
advancement of innovative research that relies heavily on data 
processing for analyzing all possible (traditional and emerging) sources 
of knowledge for pharmacovigilance.

This includes but is not limited to:
•Analyzing big data such as patients’ posts extracted from forums on the 
internet as well as large observational databases for signal detection
•Innovative systems considering other data sources than case reports, 
e.g. medical literature
•Combining multiple healthcare databases
•Novel computational signal detection methods for spontaneous reporting 
•Influence of the MedDRA hierarchy on case retrieval and signal detection
•Knowledge engineering techniques for modelling and extracting knowledge 
on ADRs
•Joint signal detection through integrative analysis of multiple, 
heterogeneous data sources
•Computational methods for signal verification / causality assessment
•Evaluation / comparative studies of computational signal detection methods
•Novel tools (e.g. mobile apps and applications at the point-of-care) 
aiming to reinforce/facilitate ADR reporting
•Extraction of ADR knowledge from clinical notes in the electronic 
health record
•Substantiation of drug safety signals, i.e. providing a biological 
explanation by exploring mechanistic connections that might explain why 
a drug produces a specific ADR
•Public resources intended to support computational process in 
•Successful case studies concerning the application of computational 
methods in pharmacovigilance
•Opinion and review papers in the domain

*Keywords*: Pharmacovigilance, Signal Detection, Computational Methods, 
Data Analytics, Knowledge Engineering

*Submission Deadlines:*
July 15, 2017:Abstract
October 02, 2017:Manuscript

*Guest Editors:*
     Cedric Bousquet, Centre Hospitalier Universitaire de Saint-Étienne, 
Saint-Étienne, France
     Marie-Christine Jaulent, INSERM, Paris, France
     Vassilis Koutkias, Institute of Applied Biosciences, Centre for 
Research & Technology Hellas, Thessaloniki, Greece

*C**FP Online:*

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