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Knowledge Discovery and Visualization of Clusters for Erythromycin Related Adverse Events in the FDA Drug Adverse Event Reporting System

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8401)

Abstract

In this paper, a research study to discover hidden knowledge in the reports of the public release of the Food and Drug Administration (FDA)’s Adverse Event Reporting System (FAERS) for erythromycin is presented. Erythromycin is an antibiotic used to treat certain infections caused by bacteria. Bacterial infections can cause significant morbidity, mortality, and the costs of treatment are known to be detrimental to health institutions around the world. Since erythromycin is of great interest in medical research, the relationships between patient demographics, adverse event outcomes, and the adverse events of this drug were analyzed. The FDA’s FAERS database was used to create a dataset for cluster analysis in order to gain some statistical insights. The reports contained within the dataset consist of 3792 (44.1%) female and 4798 (55.8%) male patients. The mean age of each patient is 41.759. The most frequent adverse event reported is oligohtdramnios and the most frequent adverse event outcome is OT(Other). Cluster analysis was used for the analysis of the dataset using the DBSCAN algorithm, and according to the results, a number of clusters and associations were obtained, which are reported here. It is believed medical researchers and pharmaceutical companies can utilize these results and test these relationships within their clinical studies.

Keywords

  • Open medical data
  • knowledge discovery
  • biomedical data mining
  • bacteria
  • drug adverse event
  • erythromycin
  • cluster analysis
  • clustering algorithms

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References

  1. Shadbolt, N., O’Hara, K., Berners-Lee, T., Gibbins, N., Glaser, H.: Linked Open Government Data: Lessons from Data.gov.uk. IEEE Intelligent Systems, 1541–1672 (2012)

    Google Scholar 

  2. Boulton, G., Rawlins, M., Vallance, P., Walport, M.: Science as a public enterprise: The case for open data. The Lancet 377, 1633–1635 (2011)

    CrossRef  Google Scholar 

  3. Rowen, L., Wong, G.K.S., Lane, R.P., Hood, L.: Intellectual property - Publication rights in the era of open data release policies. Science 289, 1881 (2000)

    CrossRef  Google Scholar 

  4. Thompson, M., Heneghan, C.: BMJ OPEN DATA CAMPAIGN We need to move the debate on open clinical trial data forward. British Medical Journal, 345 (2012)

    Google Scholar 

  5. Sakaeda, T., Tamon, A., Kadoyama, K., Okuno, Y.: Data mining of the Public Version of the FDA Adverse Event Reporting System. International Journal of Medical Sciences 10(7), 796–803 (2013)

    CrossRef  Google Scholar 

  6. Rodriguez, E.M., Staffa, J.A., Graham, D.J.: The role of databases in drug postmarketing surveillance. Pharmacoepidemiol Drug Saf. 10, 407–410 (2001)

    CrossRef  Google Scholar 

  7. Wysowski, D.K., Swartz, L.: Adverse drug event surveillance and drug withdrawals in the United States, 1969-2002: The importance of reporting suspected reactions. Arch Intern Med. 165, 1363–1369 (2005)

    CrossRef  Google Scholar 

  8. (Internet) U.S. Food and Drug Administration (FDA), http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/default.htm

  9. (Internet) MedDRA MSSO, http://www.meddramsso.com/index.asp

  10. Moore, T.J., Cohen, M.R., Furberg, C.D.: Serious adverse drug events reported to the Food and Drug Administration, 1998-2005. Arch. Intern. Med. 167, 1752–1759 (2007)

    CrossRef  Google Scholar 

  11. Weiss-Smith, S., Deshpande, G., Chung, S., et al.: The FDA drug safety surveillance program: Adverse event reporting trends. Arch. Intern. Med. 171, 591–593 (2011)

    CrossRef  Google Scholar 

  12. (Internet) Bacterial Infections, http://www.who.int/vaccine_research/diseases/soa_bacterial/en/index4.html

  13. Manchia, M., Alda, M., Calkin, C.V.: Repeated erythromycin/codeine-induced psychotic mania. Clin. Neuropharmacol. 36(5), 177–178 (2013)

    CrossRef  Google Scholar 

  14. Varughese, C.A., Vakil, N.H., Phillips, K.M.: Antibiotic-associated diarrhea: A refresher on causes and possible prevention with probiotics–continuing education article. J. Pharm. Pract. 26(5), 476–482 (2013)

    CrossRef  Google Scholar 

  15. Chen, E.S., Hripcsak, G., Xu, H., Markatou, M., Friedman, C.: Automated Acquisition of Disease–Drug Knowledge from Biomedical and Clinical Documents: An Initial Study. J. Am. Med. Inf. Assoc. 15, 87–98 (2008)

    CrossRef  Google Scholar 

  16. 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. Biological & Pharmaceutical Bulletin 35(6), 967–970 (2012)

    CrossRef  Google Scholar 

  17. Malla, S., Banda, S., Bansal, D., Gudala, K.: Trabectedin related muscular and other adverse effects; data from public version of the FDA Adverse Event Reporting System. Internatial Journal of Medical and Pharmaceutical Sciences 03(07), 11–17 (2013)

    Google Scholar 

  18. Raschi, E., Poluzzi, E., Koci, A., Moretti, U., Sturkenboom, M., De Ponti, F.: Macrolides and Torsadogenic Risk: Emerging Issues from the FDA Pharmacovigilance Database. Journal of Pharmacovigilance 1(104), 1–4 (2013)

    Google Scholar 

  19. Harpaz, R., DuMouchel, W., Shah, N.H., Madigan, D., Ryan, P., Friedman, C.: Novel data-mining methodologies for adverse drug event discovery and analysis. Clin. Pharmacol. Ther. 91(6), 1010–1021 (2012)

    CrossRef  Google Scholar 

  20. Harpaz, R., Chase, H.S., Friedman, C.: Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics 11(suppl. 9), S7 (2010)

    Google Scholar 

  21. Harpaz, R., Perez, H., Chase, H.S., Rabadan, R., Hripcsak, G., Friedman, C.: Biclustering of adverse drug events in the FDA’s spontaneous reporting system. Clin. Pharmacol. Ther. 89(2), 243–250 (2011)

    CrossRef  Google Scholar 

  22. Vilar, S., Harpaz, R., Chase, H.S., Costanzi, S., Rabadan, R., Friedman, C.: Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: Application to rhabdomyolysis. J. Am. Med. Inform. Assoc. 18(suppl. 1) (December 2011)

    Google Scholar 

  23. Wang, X., Chase, H.S., Li, J., Hripcsak, G., Friedman, C.: Integrating heterogeneous knowledge sources to acquire executable drug-related knowledge. In: AMIA Annu. Symp. Proc. 2010, pp. 852–856 (2010)

    Google Scholar 

  24. Kadoyama, K., Miki, I., Tamura, T., Brown, J.B., Sakaeda, T., Okuno, Y.: Adverse Event Profiles of 5-Fluorouracil and Capecitabine: Data Mining of the Public Version of the FDA Adverse Event Reporting System, AERS, and Reproducibility of Clinical Observations. Int. J. Med. Sci. 9(1), 33–39 (2012)

    CrossRef  Google Scholar 

  25. Yildirim, P., Ekmekci, I.O., Holzinger, A.: On Knowledge Discovery in Open Medical Data on the Example of the FDA Drug Adverse Event Reporting System for Alendronate (Fosamax). In: Holzinger, A., Pasi, G. (eds.) HCI-KDD 2013. LNCS, vol. 7947, pp. 195–206. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  26. Belciug, S., Gorunescu, F., Salem, A.B., Gorunescu, M.: Clustering-based approach for detecting breast cancer recurrence. Intelligent Systems Design and Applications (ISDA), 533–538 (2010)

    Google Scholar 

  27. Belciug, S., Gorunescu, F., Gorunescu, M., Salem, A.: Assessing performances of unsupervised and supervised neural networks in breast cancer detection. In: 7th International Conference on Informatics and Systems (INFOS), pp. 1–8 (2010)

    Google Scholar 

  28. Emmert-Streib, F., de Matos Simoes, R., Glazko, G., McDade, S., Haibe-Kains, B., Holzinger, A., Dehmer, M., Campbell, F.: Functional and genetic analysis of the colon cancer network. BMC Bioinformatics 15(suppl. 6), S6 (2014)

    Google Scholar 

  29. Yildirim, P., Majnaric, L., Ekmekci, O., Holzinger, A.: Knowledge discovery of drug data on the example of adverse reaction prediction. BMC Bioinformatics 15(suppl. 6), S7 (2014)

    Google Scholar 

  30. Reynolds, A.P., Richards, G., de la Iglesia, B., Rayward-Smith, V.J.: Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms 5, 475–504 (2006)

    MathSciNet  CrossRef  MATH  Google Scholar 

  31. Yıldırım, P., Ceken, K., Saka, O.: Discovering similarities fort he treatments of liver specific parasites. In: Proceedings of the Federated Conference on Computer Science and Information Systems, FedCSIS 2011, Szczecin, Poland, September 18-21, pp. 165–168. IEEE Xplore (2011) ISBN 978-83-60810-22-4

    Google Scholar 

  32. Holland, S.M.: Cluster Analysis, Department of Geology, University of Georgia, Athens, GA 30602-2501 (2006)

    Google Scholar 

  33. Hammouda, K., Kamel, M.: Data Mining using Conceptual Clustering, SYDE 622: Machine Intelligence, Course Project (2000)

    Google Scholar 

  34. Beckstead, J.W.: Using Hierarchical Cluster Analysis in Nursing Research. Western Journal of Nursing Research 24, 307–319 (2002)

    CrossRef  Google Scholar 

  35. Yıldırım, P., Ceken, C., Hassanpour, R., Tolun, M.R.: Prediction of Similarities among Rheumatic Diseases. Journal of Medical Systems 36(3), 1485–1490 (2012)

    CrossRef  Google Scholar 

  36. Han, J., Micheline, K.: Data mining: concepts and techniques. Morgan Kaufmann (2001)

    Google Scholar 

  37. Yang, C., Wang, F., Huang, B.: Internet Traffic Classification Using DBSCAN. In: 2009 WASE International Conference on Information Engineering, pp. 163–166 (2009)

    Google Scholar 

  38. Hall, M., Frank, E., Holmes, G., Pfahringe, B., Reutemann, P., Witten, I.E.: The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11, 1 (2009)

    CrossRef  Google Scholar 

  39. (internet) Drugbank, http://www.drugbank.ca

  40. (internet) Erythromycin, http://www.drugs.com

  41. Wang, X., Hripcsak, G., Markatou, M., Friedman, C.: Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study. Journal of the American Medical Informatics Association 16(3), 328–337 (2009)

    CrossRef  Google Scholar 

  42. (internet) Bacterial infections, http://www.nlm.nih.gov/medlineplus/bacterialinfections.html

  43. Edwards, I.R., Aronson, J.K.: Adverse drug reactions: Definitions, diagnosis, and management. Lancet 356(9237), 1255–1259 (2000)

    CrossRef  Google Scholar 

  44. (internet) Open Knowledge Foundation,Open data introduction, http://okfn.org/opendata/

  45. (internet) http://www.chemoprofiling.org/AERS/t1.html

  46. Poluzzi, E., Raschi, E., Piccinni, C., De Ponti, F.: Data mining techniques in Pharmacovigilance: Intech, Open Science (2012)

    Google Scholar 

  47. Holzinger, A., Dehmer, M., Jurisica, I.: Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions. BMC Bioinformatics 15(suppl. 6), I1 (2014)

    Google Scholar 

  48. Otasek, D., Pastrello, C., Holzinger, A., Jurisica, I.: Visual Data Mining: Effective Exploration ofthe Biological Universe. In: Holzinger, A., Jurisica, I. (eds.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges. LNCS, vol. 8401, pp. 19–33. Springer, Heidelberg (2014)

    Google Scholar 

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Yildirim, P., Bloice, M., Holzinger, A. (2014). Knowledge Discovery and Visualization of Clusters for Erythromycin Related Adverse Events in the FDA Drug Adverse Event Reporting System. In: Holzinger, A., Jurisica, I. (eds) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. Lecture Notes in Computer Science, vol 8401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43968-5_6

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  • DOI: https://doi.org/10.1007/978-3-662-43968-5_6

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