Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations-Reference-Cited by-同舟云学术

Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations

Published:2022-05 Issue:5 Volume:45 Page:493-510
ISSN:0114-5916
Container-title:Drug Safety
language:en
Short-container-title:Drug Saf

Author:

Wong Jenna,Prieto-Alhambra Daniel,Rijnbeek Peter R.,Desai Rishi J.,Reps Jenna M.,Toh Sengwee^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Pharmacology (medical),Pharmacology,Toxicology

Link

https://link.springer.com/content/pdf/10.1007/s40264-022-01158-3.pdf

Reference114 articles.

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3. Toh S, Pratt N, Klungel O, Gagne JJ, Platt RW. Distributed networks of databases analyzed using common protocols and/or common data models. Pharmacoepidemiology. John Wiley & Sons, Ltd; 2019: p. 617–38. Available from: https://onlinelibrary.wiley.com/doi/abs/https://doi.org/10.1002/9781119413431.ch25. [Accessed 1 Aug 2021].

4. Burn E, You SC, Sena A, Kostka K, Abedtash H, Abrahao MTF, et al. Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study. MedRxiv Prepr Serv Health Sci. 2020;2020.04.22.20074336.

5. Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R. Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care. Med Care. 2010;48:S45-51.

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