Enabling Data-Driven Clinical Quality Assurance: Predicting Adverse Event Reporting in Clinical Trials Using Machine Learning-Reference-Cited by-同舟云学术

Enabling Data-Driven Clinical Quality Assurance: Predicting Adverse Event Reporting in Clinical Trials Using Machine Learning

Published:2019-05-23 Issue:9 Volume:42 Page:1045-1053
ISSN:0114-5916
Container-title:Drug Safety
language:en
Short-container-title:Drug Saf

Author:

Ménard Timothé^ORCID,Barmaz Yves,Koneswarakantha Björn,Bowling Rich,Popko Leszek

Publisher

Springer Science and Business Media LLC

Subject

Pharmacology (medical),Pharmacology,Toxicology

Link

http://link.springer.com/content/pdf/10.1007/s40264-019-00831-4.pdf

Reference25 articles.

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2. Medicine and Healthcare products Regulatory Agency. GCP inspection metrics report. 2018. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/706356/GCP_INSPECTIONS_METRICS_2016-2017__final_11-05-18_.pdf . Accessed 10 Dec 2018.

3. Food and Drug Administration. 2018 inspection data set. https://www.fda.gov/downloads/ICECI/Inspections/UCM628362.xlsx . Accessed 10 Dec 2018.

4. Food and Drug Administration. Guidance for Industry: Investigator Responsibilities — Protecting the Rights, Safety, and Welfare of Study Subjects. 2009. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM187772.pdf . Accessed 10 Dec 2018.

5. Pitrou I, Boutron I, Ahmad N, Ravaud P. Reporting of safety results in published reports of randomized controlled trials. Arch Intern Med. 2009;169(19):1756–61.

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