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

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


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Shadbolt, N., O’Hara, K., Berners-Lee, T., Gibbins, N., Glaser, H.: Linked Open Government Data: Lessons from IEEE Intelligent Systems, 1541–1672 (2012)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    (Internet) MedDRA MSSO,
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Manchia, M., Alda, M., Calkin, C.V.: Repeated erythromycin/codeine-induced psychotic mania. Clin. Neuropharmacol. 36(5), 177–178 (2013)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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. 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. 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)CrossRefGoogle Scholar
  20. 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. 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)CrossRefGoogle Scholar
  22. 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. 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. 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)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 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. 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. 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. 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. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 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 978-83-60810-22-4Google Scholar
  32. 32.
    Holland, S.M.: Cluster Analysis, Department of Geology, University of Georgia, Athens, GA 30602-2501 (2006)Google Scholar
  33. 33.
    Hammouda, K., Kamel, M.: Data Mining using Conceptual Clustering, SYDE 622: Machine Intelligence, Course Project (2000)Google Scholar
  34. 34.
    Beckstead, J.W.: Using Hierarchical Cluster Analysis in Nursing Research. Western Journal of Nursing Research 24, 307–319 (2002)CrossRefGoogle Scholar
  35. 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)CrossRefGoogle Scholar
  36. 36.
    Han, J., Micheline, K.: Data mining: concepts and techniques. Morgan Kaufmann (2001)Google Scholar
  37. 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. 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)CrossRefGoogle Scholar
  39. 39.
    (internet) Drugbank,
  40. 40.
    (internet) Erythromycin,
  41. 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)CrossRefGoogle Scholar
  42. 42.
  43. 43.
    Edwards, I.R., Aronson, J.K.: Adverse drug reactions: Definitions, diagnosis, and management. Lancet 356(9237), 1255–1259 (2000)CrossRefGoogle Scholar
  44. 44.
    (internet) Open Knowledge Foundation,Open data introduction,
  45. 45.
  46. 46.
    Poluzzi, E., Raschi, E., Piccinni, C., De Ponti, F.: Data mining techniques in Pharmacovigilance: Intech, Open Science (2012)Google Scholar
  47. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of Engineering & ArchitectureOkan UniversityIstanbulTurkey
  2. 2.Research Unit HCI, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria

Personalised recommendations