Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach

키워드

참고문헌 (21)

1. Liu Y, "Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database" (2014)[DOI]
2. Montastruc JL, "Fatal adverse drug reactions : a worldwide perspective in the World Health Organization pharmacovigilance database", Br J Clin Pharmacol, 87(11), pp.4334~4340 (2021)[DOI]
3. Sonawane KB, "Serious adverse drug events reported to the FDA : analysis of the FDA adverse event reporting system 2006-2014 database", J Manag Care Spec Pharm, 24(7), pp.682~690 (2018)[DOI]
4. Jaganathan K, "Prediction of druginduced liver toxicity using SVM and optimal descriptor sets", Int J Mol Sci, 22(15), pp.8073~ (2021)[DOI]
5. Liu A, "Prediction and mechanistic analysis of drug-induced liver injury(DILI)based on chemical structure", Biol Direct, 16(1), pp.6~ (2021)[DOI]
6. Chalasani N, "Risk factors for idiosyncratic drug-induced liver injury", Gastroenterology, 138(7), pp.2246~2259 (2010)[DOI]
7. Chen Z, "The prediction approach of drug-induced liver injury:response to the issues of reproducible science of artificial intelligence in real-world applications", Brief Bioinform, 23(4), pp.bbac196~ (2022)[DOI]
8. Fleurence RL, "Launching PCORnet, a national patientcentered clinical research network", J Am Med Inform Assoc, 21(4), pp.578~582 (2014)[DOI]
9. Huser V, "Analyzing real-world use of research common data elements", AMIA Annu Symp Proc, 2018, pp.602~608 (2018)
10. Voss EA, "Feasibility and utility of applications of the common data model to multiple, disparate observational health databases", J Am Med Inform Assoc, 22(3), pp.553~564 (2015)[DOI]
11. Ryu B, "Development of prediction models for unplanned hospital readmission within 30days based on common data model : a feasibility study", Methods Inf Med, 60(S 02), pp.e65~e75 (2021)[DOI]
12. Ball R, "The FDA’s sentinel initiative : a comprehensive approach to medical product surveillance", Clin Pharmacol Ther, 99(3), pp.265~268 (2016)[DOI]
13. Drug-induced liver injury (DILI):current status and future directions for drug development and the post-market setting, "The Council for International Organizations of Medical Sciences (CIOMS)", CIOMS, ~ (2020)
14. Goldstein A, "Peeking inside the black box : Visualizing statistical learning with plots of individual conditional expectation", J Comput Graph Stat, 24(1), pp.44~65 (2015)[DOI]
15. Lundberg SM, "A unified approach to interpreting model predictions", Adv Neural Inf Process Syst, 30, pp.4765~4774 (2017)
16. Selvaraju RR, "Grad-CAM: visual explanations from deep networks via gradient-based localization", Proceedings of the IEEE International Conference on Computer Vision, pp.618~626 (2017)
17. Guo T, "Exploring interpretable LSTM neural networks over multi-variable data", Proceedings of the 36th International Conference on Machine Learning(ICML), pp.2494~2504 (2019)
18. Barreras A, "Angiotensin II receptor blockers", Proc(Bayl Univ Med Cent), 16(1), pp.123~126 (2003)[DOI]
19. Hill RD, "Angiotensin II receptor blockers (ARB)" (2019), StatPearls Publishing;
20. "MOACDM" (2023)[URL]
21. Wang SV, "Development and evaluation of a global propensity score for data mining with tree-based scan statistics (Sentinel Methods Protocol)" (2018), The Sentinel System

관련 논문AI추천