Exploiting heterogeneous publicly available data sources for drug safety surveillance: computational framework and case studies

Koutkias, Vassilis; Louët, Agnés Lillo‐Le; Jaulent, Marie-Christine

doi:10.1080/14740338.2017.1257604

Cited by 18 publications

(21 citation statements)

References 31 publications

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“…The recent focus has been on various aspects of causal assessment based on heterogeneous evidence. Some examples include work on aggregating human and animal data (European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), 2009), aggregation of spontaneous reports (Caster et al, 2017;Watson et al, 2018), Bayesian aggregation of safety trial data (Price et al, 2014) and data sets (Landes and Williamson, 2016), bringing together toxicology and epidemiology (Adami et al, 2011), retrieving but not assessing evidence Knowledge Base workgroup of the Observational Health Data Sciences and Informatics, 2017; Koutkias et al, 2017), assessing the evidential force of data in terms of reproducibility and replicability of the research (LeBel et al, 2018), grading certainty of evidence of effects in studies (Alonso-Coello et al, 2016), grading observational studies based on study design (Sanderson et al, 2007;Sterne et al, 2016;Wells et al, 2018), thematic synthesis of qualitative research, decision making (Thomas and Harden, 2008;Landes, 2018), providing probability bounds for an adverse event being drug induced in an individual (Murtas et al, 2017) in Pearl's formal framework for causality (Pearl, 2000) and work on aggregating evidence generated by computational tools (Koutkias and Jaulent, 2015).…”

Section: Introductionmentioning

confidence: 99%

E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance

2019

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Background: Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm.Methods: In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and side effects. This framework arose out of a philosophical analysis of the Bradford Hill Guidelines. In this article, we expand the Bayesian framework and add "evidential modulators," which bear on the assessment of the reliability of incoming study results. The overall framework for evidence synthesis, "E-Synthesis", is then applied to a case study.Results: Theoretically and computationally, E-Synthesis exploits coherence of partly or fully independent evidence converging towards the hypothesis of interest (or of conflicting evidence with respect to it), in order to update its posterior probability. With respect to other frameworks for evidence synthesis, our Bayesian model has the unique feature of grounding its inferential machinery on a consolidated theory of hypothesis confirmation (Bayesian epistemology), and in allowing any data from heterogeneous sources (cell-data, clinical trials, epidemiological studies), and methods (e.g., frequentist hypothesis testing, Bayesian adaptive trials, etc.) to be quantitatively integrated into the same inferential framework.Conclusions: E-Synthesis is highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, that is philosophically and statistically grounded. Furthermore, by introducing evidential modulators, and thereby breaking up the different dimensions of evidence (strength, relevance, reliability), E-Synthesis allows them to be explicitly tracked in updating causal hypotheses.

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Section: Introductionmentioning

confidence: 99%

E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance

2019

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“…Under-reporting of ADRs by patients in spontaneous reporting systems like the FDA Adverse Event Reporting System (FAERS) has also been observed, with only 20–33% of the minimum number of expected serious events being reported (Alatawi and Hansen, 2017 ). Consequently, several authors have reached to the conclusion that social media listening is an important tool to augment post-marketing safety surveillance (Powell et al, 2016 ; Koutkias et al, 2017 ). However, these authors consider that much work is needed to determine the best methods for using this data source.…”

Section: Introductionmentioning

confidence: 99%

Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate

Chen

Faviez²,

Schück³

et al. 2018

Front. Pharmacol.

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Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety.Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus.Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics.Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation, but also Side effects. Cases of misuse were also identified in this corpus, including recreational use and abuse.Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.

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“…Social media platforms (mostly Twitter, DailyStrength.com, and dedicated patient forums) attracted recently major interest for DS. Exploiting KE activities like knowledge extraction in social media can add a valuable new data source in the DS ecosystem, as they are characterized by three interesting aspects (Koutkias et al, 2017): (a) they provide vast amounts of data, (b) posts could be monitored across time and trends could be identified in relation with triggering events (e.g., new safety issues reported by regulatory authorities or announced in the media), and (c) user interconnections (e.g., mentions, responses, followership, etc.) could create a “social graph” which could provide useful insights through graph-based Social Network Analysis (SNA).…”

Section: Resultsmentioning

confidence: 99%

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

et al. 2019

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Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.

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Exploiting heterogeneous publicly available data sources for drug safety surveillance: computational framework and case studies

Abstract: This work contributes in establishing a continuous learning system for drug safety surveillance by exploiting heterogeneous publicly available data sources via appropriate support tools.

Cited by 18 publications

References 31 publications

E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance

E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance

Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

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