Early Detection of Adverse Drug Reactions in Postmarket Monitoring
Published Online:9 Jul 2025https://doi.org/10.1287/ijoc.2024.0585
References
- (1998) A Bayesian neural network method for adverse drug reaction signal generation. Eur. J. Clinical Pharmacology 54(4):315–321.Crossref, Google Scholar
- (1997) Dynamic itemset counting and implication rules for market basket data. Peckman JM, Ram S, Franklin M, eds. Proc. 1997 ACM SIGMOD Internat. Conf. Management Data (Association for Computing Machinery, New York), 255–264.Google Scholar
- (2020) Renal adverse effects following the use of different immune checkpoint inhibitor regimens: A real-world pharmacoepidemiology study of post-marketing surveillance data. Cancer Medicine 9(18):6576–6585.Crossref, Google Scholar
- (2012) Adverse drug effect detection. IEEE J. Biomedical Health Inform. 17(2):305–311.Crossref, Google Scholar
- (2014) Selecting the right correlation measure for binary data. ACM Trans. Knowledge Discovery Data 9(2):1–28.Crossref, Google Scholar
- (2025) Early detection of adverse drug reactions in post-market monitoring. https://doi.org/10.1287/ijoc.2024.0585.cd, https://github.com/INFORMSJoC/2024.0585.Google Scholar
- (2019) Myocarditis following the use of different immune checkpoint inhibitor regimens: A real-world analysis of post-marketing surveillance data. Internat. Immunopharmacology 76:105866.Crossref, Google Scholar
- (2006) Interestingness measures for data mining: A survey. ACM Comput. Surveys 38(3):9–es.Crossref, Google Scholar
- (2013) Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clinical Pharmacology Therapeutics 93(6):539–546.Crossref, Google Scholar
- (2022) Signaling COVID-19 vaccine adverse events. Drug Safety 45(7):765–780.Crossref, Google Scholar
- (2007) Predicting adequacy of vancomycin regimens: A learning-based classification approach to improving clinical decision making. Decision Support Systems 43(4):1226–1241.Crossref, Google Scholar
- (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans. Inform. Systems 20(4):422–446.Crossref, Google Scholar
- (2002) Drug-related deaths in a university central hospital. Eur. J. Clinical Pharmacology 58(7):479–482.Crossref, Google Scholar
- (2012) KDIGO clinical practice guidelines for acute kidney injury. Nephron Clinical Practice 120(4):c179–c184.Crossref, Google Scholar
- (2013) Causal inference with rare events in large-scale time-series data. IJCAI’13 Proc. 23rd Internat. Joint Conf. Artificial Intelligence (AAAI Press, Washington, DC), 1444–1450.Google Scholar
- (2019) Towards early detection of adverse drug reactions: Combining pre-clinical drug structures and post-market safety reports. BMC Medical Informatics Decision Making 19(1):1–9.Crossref, Google Scholar
- (2013) Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records. J. Amer. Medical Inform. Assoc. 20(3):420–426.Crossref, Google Scholar
- (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486(7403):361–367.Crossref, Google Scholar
- (2016) Causal explanation under indeterminism: A sampling approach. Proc. 30th AAAI Conf. Artificial Intelligence (AAAI Press, Washington, DC), 1037–1043.Google Scholar
- (1988) Statistics in medicine: Calculating confidence intervals for relative risks (odds ratios) and standardised ratios and rates. British Medical J. 296(6632):1313–1316.Crossref, Google Scholar
- (1968) Association and estimation in contingency tables. J. Amer. Statist. Assoc. 63(321):1–28.Crossref, Google Scholar
- (2019) Early detection of adverse drug reactions in social health networks: A natural language processing pipeline for signal detection. JMIR Public Health Surveillance 5(2):e11264.Crossref, Google Scholar
- (2013) Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery. Statist. Methods Medical Res. 22(1):57–69.Crossref, Google Scholar
- (2008) A statistical methodology for drug–drug interaction surveillance. Statist. Medicine 27(16):3057–3070.Crossref, Google Scholar
- (1991) Knowledge Discovery in Databases (MIT Press, Cambridge, MA).Google Scholar
- (2019) Conformalized quantile regression. Proc. 33rd Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Inc., Red Hook, NY), 3543–3553.Google Scholar
- (2020) Risk factor considerations in statistical signal detection: Using subgroup disproportionality to uncover risk groups for adverse drug reactions in VigiBase. Drug Safety 43(10):999–1009.Crossref, Google Scholar
- (2017) Paying for prescription drugs around the world: Why is the US an outlier? Commonwealth Fund Issue Brief (October), 1–14.Google Scholar
- (2016) Detecting adverse drug reactions following long-term exposure in longitudinal observational data: The exposure-adjusted self-controlled case series. Statist. Methods Medical Res. 25(6):2577–2592.Crossref, Google Scholar
- (2016) Performance of stratified and subgrouped disproportionality analyses in spontaneous databases. Drug Safety 39:355–364.Crossref, Google Scholar
- (2023) Real-world safety profile of riluzole: A systematic analysis of data from the FAERS database and case reports. Expert Opinion Drug Safety 22(10):967–974.Crossref, Google Scholar
- (2023) Sensitivity and specificity in signal detection with the reporting odds ratio and the information component. Pharmacoepidemiology Drug Safety 32(8):910–917.Crossref, Google Scholar
- (2011) Expressing observations from electronic medical record flowsheets in an i2b2 based clinical data repository to support research and quality improvement. AMIA Annual Sympos. Proc., vol. 2011 (American Medical Informatics Association, Bethesda, MD), 1454–1463.Google Scholar
- (2019) Detecting potential adverse drug reactions using a deep neural network model. J. Medical Internet Res. 21(2):e11016.Crossref, Google Scholar
- (2020) Predicting drug risk level from adverse drug reactions using SMOTE and machine learning approaches. IEEE Access 8:185761–185775.Crossref, Google Scholar
- World Health Organization (1969) International drug monitoring: The role of the hospital: Report of a WHO meeting (World Health Organization, Geneva).Google Scholar
- (2006) TAPER: A two-step approach for all-strong-pairs correlation query in large databases. IEEE Trans. Knowledge Data Engrg. 18(4):493–508.Crossref, Google Scholar
- (2008) Top-k φ correlation computation. INFORMS J. Comput. 20(4):539–552.Link, Google Scholar
- (2022) Machine learning in causal inference: Application in pharmacovigilance. Drug Safety 45(5):459–476.Crossref, Google Scholar
- (2003) Peculiarity oriented multidatabase mining. IEEE Trans. Knowledge Data Engrg. 15(4):952–960.Crossref, Google Scholar
- (2018) Paradoxical correlation pattern mining. IEEE Trans. Knowledge Data Engrg. 30(8):1561–1574.Crossref, Google Scholar

