References
- Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA. 1998;279(15):1200–1205.
- Montastruc JL, Bagheri H, Durrieu G, et al. Adverse drug reactions, iatrogenic diseases, drug safety, and pharmacovigilance: importance and interest for patients and their physicians. In: Cousty S, Laurencin-Dalicieux S, editors. Drug-induced oral complications. Cham: Switzerland: Springer International; 2021. p. 1–6.
- Laroche M-L, Gautier S, Polard E, et al. Incidence and preventability of hospital admissions for adverse drug reactions in France: a prospective observational study (IATROSTAT). Br J Clin Pharmacol. 2022 Aug 24 2022; published online ahead of print. available at https://www.rfcrpv.fr/wp-content/uploads/2022/05/rapport-IATROSTAT-version-defintiive-02-mai-2022.pdf. Last accessed .
- de Filippis R, De Fazio P, Kane JM, et al. Pharmacovigilance approaches to study of rare and very rare side-effects: example of clozapine-related DiHS/DRESS syndrome. Expert Opin Drug Saf. 2022;21(5):585–587.
- Onakpoya IJ. Rare adverse events in clinical trials: understanding the rule of three. BMJ Evid Based Med. 2018;23(1):6.
- FDA Adverse Event Reporting Systems (FAERS) Public Dashboard. Last accessed 2022 Sep 27 Available from: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard
- About Vigibase. updated 2022 Sep 27 Available from: https://who-umc.org/vigibase/
- Liu X, Zhao X, He Y, et al. Dropped head syndrome: a rare adverse drug reaction identified in the FDA adverse event reporting system and review of case reports in the literature. Expert Opin Drug Saf. published online ahead of print 2022 Mar 22 2022; 1–8. DOI:10.1080/14740338.2022.2054986.
- Faillie JL. Case-noncase studies: principle, methods, bias, and interpretation. Therapie. 2019;74(2):225–232.
- Strandell J, Caster O, Hopstadius J, et al. The development and evaluation of triage algorithms for early discovery of adverse drug interactions. Drug Saf. 2013;36(5):371–388.
- Caster O, Juhlin K, Watson S, et al. Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank. Drug Saf. 2014;37(8):617–628.
- Henry J, Pylypchuk Y, Searcy T, et al. Adoption of electronic health record systems among U.S. non-federal acute care hospitals: 2008–2015. ONC Data Brief, no. 35. Office of the National Coordinator for Health Information Technology: Washington DC (2016). updated 2022 Sep 27 Available from: https://www.healthit.gov/data/data-briefs/adoption-electronic-health-record-systems-among-us-non-federal-acute-care-1
- Hanlon JT, Sloane RJ, Pieper CF, et al. Association of adverse drug reactions with drug-drug and drug-disease interactions in frail older outpatients. Age Ageing. 2011;40(2):274–277.
- Chiang AP, Butte AJ. Data-driven methods to discover molecular determinants of serious adverse drug events. Clin Pharmacol Ther. 2009;85(3):259–268.
- Harpaz R, DuMouchel W, Shah NH, et al. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012;91(6):1010–1021.
- Shailaja K, Seetharamulu B, Jabbar MA “Machine learning in healthcare: review.” 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA);2018. p. 910–914. Last accessed 2022 Sep 27.https://ieeexplore.ieee.org/document/8474918
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444.
- Hill AB. The environment and disease: association or causation. J R Soc Med. 1965;58(5):295–300.