Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events

References
1 Hrynaszkiewicz I. The need and drive for open data in biomedical publishing. Serials 2011; 24 (01) 31-37.
2 Boulton G, Rawlins M, Vallance P, Walport M. Science as a public enterprise: the case for open data. Lancet 2011; 377: 1633-1635.
3 Poluzzi E, Raschi E, Piccinni C, De Ponti F. Analysis of the Publicly Accessible FDA Adverse Event Reporting System(AERS). Data Mining Applications in Engineering and Medicine 2012. http://www.intechopen.com/books/data-mining-applications-in-engineering-and-medicine/data-mining-techniques-in-pharmacovigilance-analysis-of-the-publicly-accessible-fda-adverse-event-re
4 Johnson KB, Lehmann CU. Council on Clinical Information Technology of the American Academy of Pediatrics. Electronic prescribing in pediatrics: toward safer and more effective medication management. Pediatrics 2013; 131 (04) 824-826.
5 Institute of Medicine, Committee on Quality in Healthcare in America.. To Err is Human: building a Safer Health System. Washington DC: National Academies Press; 1999
6 Walport M, Brest P. Sharing research data to improve public health. Lancet 2011; 377: 537-539.
7 U. S Food and Drug Administration.. Postmarketing surveillance programs, 2009. http://www.fda.gov/ Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/ucm090385.htm .
8 Sakaeda T, Tamon A, Kadoyama K, Okuno Y. Data Mining of the Public Version of the FDA Adverse Event Reporting System. Int J Med Sci 2013; 10 (07) 796-803.
9 Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah NH. Performance of Pharmacovigi-lance Signal-Detection Algorithms for the FDA Adverse Event Reporting System. Clin Pharmacol Ther 2013; 93 (06) 539-546.
10 O’Neill RT, Szarfman A. Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System. American Statistician 1999; 53 (03) 190-196.
11 Harpaz R, Chase HS, Friedman C. Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics 2010; 11 (Suppl. 09) S7.
12 Hauben M, Horn S, Reich L, Younus M. Association between gastric acid suppressants and clostridium difficile colitis and community-acquired pneumonia: analysis using pharmacovigilance tools. Int J Infect Dis 2007; 11 (05) 417-422.
13 Tamura T, Sakaeda T, Kadoyama K, Okuno Y. Omeprazole and Esomeprazole-associated Hypomagnesaemia: Data Mining of the Public Version of the FDA Adverse Event Reporting System. Int J Med Sci 2012; 9 (05) 322-326.
14 Alvarez SA. Chi-squared computation for association rules: preliminary results. Technical Report, BCCS-2003–01 July 2003.
15 Silverstein C, Brin S, Motwani R. Beyond Market Baskets: Generalizing Association Rules to Dependence rules. Data Mining and Knowledge Discovery 1998; 2: 39-68.
16 Ali AK. Pharmacovigilance analysis of adverse event reports for aliskiren hemifumarate, a first-in-class direct renin inhibitor. Ther Clin Risk Manag 2011; 7: 337-344.
17 Hauben M, Bate A. Decision support methods for the detection of adverse events in post marketing data. Drug Discov Today 2009; 14 7–8 343-357.
18 Wang C, Guo XJ, Xu JF, Wu C, Sun YL, Ye XF, Qian W, Ma XQ, Du WM, He J. Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systems. PLoS One 2012; 7 (07) e40561.
19 Hoog SL, Cheng Y, Elpers J. Duloxetine and Pregnancy Outcomes: Safety Surveillance Findings. Int J Med Sci 2013; 10 (04) 413-419.
20 Kadoyama K, Sakaeda T, Tamon A, Okuno Y. Adverse Event Profile of Tigecycline: Data Mining of the Public Version of the U. S. Food and Drug Administration Adverse Event Reporting System. Biol Pharm Bull 2012; 35 (06) 967-970.
21 Haring B, Bauer W. Ciprofloxacin and the risk for cardiac arrhythmias: culprit delicti or watching bystander?. Acta Cardiol 2012; 67 (03) 351-354.
22 Moffett BS, Valdes SO, Kim JJ. Possible digoxin toxicity associated with concomitant ciprofloxacin therapy. Int J Clin Pharm 2013; 35 (05) 673-676.
23 Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah NH. Performance of Pharmacovigi-lance signal detection algorithms for the FDA Adverse Event Reporting System. Clin Pharmacol Ther 2013; 93 (06) 1-20.
24 Han J, Kamber M, Pei J. Data mining: concepts and techniques. Oxford: Elsevier Ltd; 2011
25 Zhu AL, Li J, Leong TY. Automated Knowledge Extraction for Decision Model Construction. A Data Mining Approach. AMIA Annu Symp Proc 2003; 2003: 758-776.
26 Deora CS, Arora S, Makani Z. Comparison of interestingness measures: support-confidence framework versus lift-irule framework. IJERA 2013; 3 (02) 208-215.
27 Drugbank.. http://www.drugbank.ca
28 Hall M, Frank E, Holmes G, Pfahringe B, Reutemann P, Witten IE. The WEKA data mining software: an update. SIGKDD Explorations 2009; 11 (01) 10-18.
29 WEKA: Weka 3: Data Mining Software in Java.. http://www.cs.waikato.ac.nz/ml/weka
30 Medication guide Cipro® 2008.. http://www.accessdata.fda.gov/drugsatfda_docslabel/2009/019537s701984744198575120780282147325L.pdf
31 Kim GK. The risk of Fluoroquinolone-induced Tendinopathy and Tendon Rupture: What Does The Clinician Need to Know?. J Clin Aesthet Dermatol 2010; 3 (04) 49-54.
32 McGarry K. A survey of interestingness measures for knowledge discovery. The Knowledge Engineering Review 2005; 20 (01) 39-61.
33 Lee DG, Ryu KS, Bashir M, Bae JW, Ryu KH. Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction. J Med Syst 2013; 37 (02) 9896.
34 Tang J, Chuang LY, Hsi E, Lin YD, Yang CH, Chang HW. Identifying the association rules between clinicopathologic factors and higher survival performance in operation centric oral cancer patients using the apriori algorithm. BioMed Res Int 2013; 2013: 359634.
35 Sheikh LM, Tanveer B, Hamdani MA. Interesting Measures for Mining Association Rules. Multitopic Conference, Proceedings of INMIC 2004. 8th International 2004: 641-644.
36 Kaimal LB, Metkar AR, Rakesh G. Self Learning Real Time Expert System. IJSCAI 2014; 3 (02) 13-25.
37 Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. ACM-SIGMOD Record 2000; 29 (02) 1-12.
38 Said AM, Dominic D, Abdullah AB. A Comparative Study of FP-growth Variations. IJCSNS 2009; 9 (05) 266-272.
39 Borgelt A. An Implementation of the FP-Growth Algorithm. OSDM’05, August 21, 2005 Chicago, IL: United States.
40 Wu B, Zhang D, Lan Q, Zheng J. An Efficient Frequent Patterns Mining Algorithm Based on Apriori Algorithm and the FP-tree Structure. Third International Conference on convergence and Hybrid Information Technology 2008, Washington DC: United States.
41 Pearson RK. The problem of disguised missing data. SIGKDD Explorations 2006; 8 (01) 83-92.
42 Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health information science and systems 2014; 2 (03) 1-10.
43 Yildirim P, Ekmekci OI, Holzinger A. On Knowledge Discovery in Open Medical Data on the Example of the FDA Drug Adverse Event Reporting System for Alendronate (Fosamax). Lecture Notes in Computer Science 2013; 7947: 195-206.