Literature DB >> 28840504

From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources.

Gianluca Trifirò1,2, Janet Sultana3,4, Andrew Bate5,6.   

Abstract

In the last decade 'big data' has become a buzzword used in several industrial sectors, including but not limited to telephony, finance and healthcare. Despite its popularity, it is not always clear what big data refers to exactly. Big data has become a very popular topic in healthcare, where the term primarily refers to the vast and growing volumes of computerized medical information available in the form of electronic health records, administrative or health claims data, disease and drug monitoring registries and so on. This kind of data is generally collected routinely during administrative processes and clinical practice by different healthcare professionals: from doctors recording their patients' medical history, drug prescriptions or medical claims to pharmacists registering dispensed prescriptions. For a long time, this data accumulated without its value being fully recognized and leveraged. Today big data has an important place in healthcare, including in pharmacovigilance. The expanding role of big data in pharmacovigilance includes signal detection, substantiation and validation of drug or vaccine safety signals, and increasingly new sources of information such as social media are also being considered. The aim of the present paper is to discuss the uses of big data for drug safety post-marketing assessment.

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Year:  2018        PMID: 28840504     DOI: 10.1007/s40264-017-0592-4

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  37 in total

1.  Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug-drug interaction extraction and classification.

Authors:  Asma Ben Abacha; Md Faisal Mahbub Chowdhury; Aikaterini Karanasiou; Yassine Mrabet; Alberto Lavelli; Pierre Zweigenbaum
Journal:  J Biomed Inform       Date:  2015-09-30       Impact factor: 6.317

Review 2.  Utilizing social media data for pharmacovigilance: A review.

Authors:  Abeed Sarker; Rachel Ginn; Azadeh Nikfarjam; Karen O'Connor; Karen Smith; Swetha Jayaraman; Tejaswi Upadhaya; Graciela Gonzalez
Journal:  J Biomed Inform       Date:  2015-02-23       Impact factor: 6.317

3.  The EU-ADR project: preliminary results and perspective.

Authors:  Gianluca Trifiro; Annie Fourrier-Reglat; Miriam C J M Sturkenboom; Carlos Díaz Acedo; Johan Van Der Lei
Journal:  Stud Health Technol Inform       Date:  2009

Review 4.  Using real-world healthcare data for pharmacovigilance signal detection - the experience of the EU-ADR project.

Authors:  Vaishali K Patadia; Preciosa Coloma; Martijn J Schuemie; Ron Herings; Rosa Gini; Giampiero Mazzaglia; Gino Picelli; Carla Fornari; Lars Pedersen; Johan van der Lei; Miriam Sturkenboom; Gianluca Trifirò
Journal:  Expert Rev Clin Pharmacol       Date:  2015-01       Impact factor: 5.045

5.  Identifying plausible adverse drug reactions using knowledge extracted from the literature.

Authors:  Ning Shang; Hua Xu; Thomas C Rindflesch; Trevor Cohen
Journal:  J Biomed Inform       Date:  2014-07-19       Impact factor: 6.317

Review 6.  Big data in medicine is driving big changes.

Authors:  F Martin-Sanchez; K Verspoor
Journal:  Yearb Med Inform       Date:  2014-08-15

Review 7.  Novel data-mining methodologies for adverse drug event discovery and analysis.

Authors:  R Harpaz; W DuMouchel; N H Shah; D Madigan; P Ryan; C Friedman
Journal:  Clin Pharmacol Ther       Date:  2012-06       Impact factor: 6.875

8.  Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

Authors:  Ziad Obermeyer; Ezekiel J Emanuel
Journal:  N Engl J Med       Date:  2016-09-29       Impact factor: 91.245

9.  Harmonization process for the identification of medical events in eight European healthcare databases: the experience from the EU-ADR project.

Authors:  Paul Avillach; Preciosa M Coloma; Rosa Gini; Martijn Schuemie; Fleur Mougin; Jean-Charles Dufour; Giampiero Mazzaglia; Carlo Giaquinto; Carla Fornari; Ron Herings; Mariam Molokhia; Lars Pedersen; Annie Fourrier-Réglat; Marius Fieschi; Miriam Sturkenboom; Johan van der Lei; Antoine Pariente; Gianluca Trifirò
Journal:  J Am Med Inform Assoc       Date:  2012-09-06       Impact factor: 4.497

10.  Mining FDA drug labels using an unsupervised learning technique--topic modeling.

Authors:  Halil Bisgin; Zhichao Liu; Hong Fang; Xiaowei Xu; Weida Tong
Journal:  BMC Bioinformatics       Date:  2011-10-18       Impact factor: 3.169

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  21 in total

1.  From Data Silos to Standardized, Linked, and FAIR Data for Pharmacovigilance: Current Advances and Challenges with Observational Healthcare Data.

Authors:  Vassilis Koutkias
Journal:  Drug Saf       Date:  2019-05       Impact factor: 5.606

Review 2.  First Conference on Big Data for Pharmacovigilance.

Authors:  Jae Min; Vicki Osborne; Elizabeth Lynn; Saad A W Shakir
Journal:  Drug Saf       Date:  2018-12       Impact factor: 5.606

Review 3.  The Role of European Healthcare Databases for Post-Marketing Drug Effectiveness, Safety and Value Evaluation: Where Does Italy Stand?

Authors:  Gianluca Trifirò; Rosa Gini; Francesco Barone-Adesi; Ettore Beghi; Anna Cantarutti; Annalisa Capuano; Carla Carnovale; Antonio Clavenna; Mirosa Dellagiovanna; Carmen Ferrajolo; Matteo Franchi; Ylenia Ingrasciotta; Ursula Kirchmayer; Francesco Lapi; Roberto Leone; Olivia Leoni; Ersilia Lucenteforte; Ugo Moretti; Alessandro Mugelli; Luigi Naldi; Elisabetta Poluzzi; Concita Rafaniello; Federico Rea; Janet Sultana; Mauro Tettamanti; Giuseppe Traversa; Alfredo Vannacci; Lorenzo Mantovani; Giovanni Corrao
Journal:  Drug Saf       Date:  2019-03       Impact factor: 5.606

4.  Workshop on the Italian Pharmacovigilance System in the International Context: Critical Issues and Perspectives.

Authors:  Janet Sultana; Ugo Moretti; Antonio Addis; Pia Caduff; Annalisa Capuano; Agnes Kant; Joan-Ramon Laporte; Marie Lindquist; June Raine; Daniele Sartori; Gianluca Trifirò; Marco Tuccori; Mauro Venegoni; Eugene van Puijenbroek; Roberto Leone
Journal:  Drug Saf       Date:  2019-05       Impact factor: 5.606

5.  Toxicities with Immune Checkpoint Inhibitors: Emerging Priorities From Disproportionality Analysis of the FDA Adverse Event Reporting System.

Authors:  Emanuel Raschi; Alessandra Mazzarella; Ippazio Cosimo Antonazzo; Nicolò Bendinelli; Emanuele Forcesi; Marco Tuccori; Ugo Moretti; Elisabetta Poluzzi; Fabrizio De Ponti
Journal:  Target Oncol       Date:  2019-04       Impact factor: 4.493

Review 6.  Healthcare Databases for Drug Safety Research: Data Validity Assessment Remains Crucial.

Authors:  Nigel S B Rawson; Carl D'Arcy
Journal:  Drug Saf       Date:  2018-09       Impact factor: 5.606

7.  Hospitalizations and deaths related to adverse drug events worldwide: Systematic review of studies with national coverage.

Authors:  Lunara Teles Silva; Ana Carolina Figueiredo Modesto; Rita Goreti Amaral; Flavio Marques Lopes
Journal:  Eur J Clin Pharmacol       Date:  2021-10-30       Impact factor: 2.953

8.  Use of Linked Databases for Improved Confounding Control: Considerations for Potential Selection Bias.

Authors:  Jenny W Sun; Rui Wang; Dongdong Li; Sengwee Toh
Journal:  Am J Epidemiol       Date:  2022-03-24       Impact factor: 5.363

9.  Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.

Authors:  Scott A Malec; Peng Wei; Elmer V Bernstam; Richard D Boyce; Trevor Cohen
Journal:  J Biomed Inform       Date:  2021-03-11       Impact factor: 6.317

10.  Using machine learning to investigate self-medication purchasing in England via high street retailer loyalty card data.

Authors:  Alec Davies; Mark A Green; Alex D Singleton
Journal:  PLoS One       Date:  2018-11-19       Impact factor: 3.240

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