Literature DB >> 33026641

Artificial Intelligence, Real-World Automation and the Safety of Medicines.

Andrew Bate1,2, Steve F Hobbiger3.   

Abstract

Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.

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Mesh:

Year:  2020        PMID: 33026641     DOI: 10.1007/s40264-020-01001-7

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


  61 in total

1.  A decade of data mining and still counting.

Authors:  Manfred Hauben; G Niklas Norén
Journal:  Drug Saf       Date:  2010-07-01       Impact factor: 5.606

Review 2.  Quantitative signal detection using spontaneous ADR reporting.

Authors:  A Bate; S J W Evans
Journal:  Pharmacoepidemiol Drug Saf       Date:  2009-06       Impact factor: 2.890

3.  Pattern signalling in health information monitoring systems.

Authors:  A Levine; S P Mandel; A Santamaria
Journal:  Methods Inf Med       Date:  1977-07       Impact factor: 2.176

4.  Real-World Evidence, Causal Inference, and Machine Learning.

Authors:  William H Crown
Journal:  Value Health       Date:  2019-05       Impact factor: 5.725

5.  Systemic signalling of adverse reactions to drugs.

Authors:  D J Finney
Journal:  Methods Inf Med       Date:  1974-01       Impact factor: 2.176

6.  Comprehensive drug surveillance.

Authors:  H Jick; O S Miettinen; S Shapiro; G P Lewis; V Siskind; D Slone
Journal:  JAMA       Date:  1970-08-31       Impact factor: 56.272

7.  Monitoring of adverse reactions to drugs in the United Kingdom.

Authors:  W H Inman
Journal:  Proc R Soc Med       Date:  1970-12

8.  The hope, hype and reality of Big Data for pharmacovigilance.

Authors:  Andrew Bate; Robert F Reynolds; Patrick Caubel
Journal:  Ther Adv Drug Saf       Date:  2017-10-31

9.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Authors:  Daniel Shu Wei Ting; Carol Yim-Lui Cheung; Gilbert Lim; Gavin Siew Wei Tan; Nguyen D Quang; Alfred Gan; Haslina Hamzah; Renata Garcia-Franco; Ian Yew San Yeo; Shu Yen Lee; Edmund Yick Mun Wong; Charumathi Sabanayagam; Mani Baskaran; Farah Ibrahim; Ngiap Chuan Tan; Eric A Finkelstein; Ecosse L Lamoureux; Ian Y Wong; Neil M Bressler; Sobha Sivaprasad; Rohit Varma; Jost B Jonas; Ming Guang He; Ching-Yu Cheng; Gemmy Chui Ming Cheung; Tin Aung; Wynne Hsu; Mong Li Lee; Tien Yin Wong
Journal:  JAMA       Date:  2017-12-12       Impact factor: 56.272

10.  Artificial intelligence and medicine: discussion paper.

Authors:  D J Hand
Journal:  J R Soc Med       Date:  1987-09       Impact factor: 18.000

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  9 in total

1.  Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.

Authors:  Jeffrey K Aronson
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.606

Review 2.  Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.

Authors:  Benjamin Kompa; Joe B Hakim; Anil Palepu; Kathryn Grace Kompa; Michael Smith; Paul A Bain; Stephen Woloszynek; Jeffery L Painter; Andrew Bate; Andrew L Beam
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.606

3.  Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions.

Authors:  Yauheniya Cherkas; Joshua Ide; John van Stekelenborg
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.606

4.  Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance.

Authors:  Raymond Kassekert; Neal Grabowski; Denny Lorenz; Claudia Schaffer; Dieter Kempf; Promit Roy; Oeystein Kjoersvik; Griselda Saldana; Sarah ElShal
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

5.  Black Swan Events and Intelligent Automation for Routine Safety Surveillance.

Authors:  Oeystein Kjoersvik; Andrew Bate
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

6.  Artificial Intelligence and Machine Learning for Safe Medicines.

Authors:  Andrew Bate; Yuan Luo
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

7.  Supervised Machine Learning-Based Decision Support for Signal Validation Classification.

Authors:  Muhammad Imran; Aasia Bhatti; David M King; Magnus Lerch; Jürgen Dietrich; Guy Doron; Katrin Manlik
Journal:  Drug Saf       Date:  2022-05-17       Impact factor: 5.228

8.  Identifying Actionability as a Key Factor for the Adoption of 'Intelligent' Systems for Drug Safety: Lessons Learned from a User-Centred Design Approach.

Authors:  George I Gavriilidis; Vlasios K Dimitriadis; Marie-Christine Jaulent; Pantelis Natsiavas
Journal:  Drug Saf       Date:  2021-10-21       Impact factor: 5.606

9.  Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov.

Authors:  Claus Zippel; Sabine Bohnet-Joschko
Journal:  Int J Environ Res Public Health       Date:  2021-05-11       Impact factor: 3.390

  9 in total

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