Connections of Reduced Performance Health Data for Severe Persistent Uncontrolled Allergic Asthma Treated by Omazulimab

Stefanos Matsopoulos, Valentina Plekhanova

2014

Abstract

An application of association rules mining method for the discovery of associations in abnormal quantitative health data for inadequately controlled severe allergic Asthma treated by Omazulimab is presented. To the best of authors’ knowledge, no formal approaches have ever been used for extraction of association rules among dysfunctional elements in Spirometry datasets. Initially is provided an explanation of the procedures used for diagnosing inadequately controlled severe allergic Asthma. Following this, it is conducted critical evaluation of well-known ‘association rule’ mining techniques, in order to identify the one with the best utility for discovery of associations among abnormal elements of Spirometry datasets. Apriori Algorithm is applied to real-life Spirometry datasets to illustrate the contribution of application of association rule mining techniques. This revealed the existence of association rules among dysfunctional Spirometry elements for this disease. Moreover it has been identified that this disease is provoked by association of Spirometry elements that do not function properly as these are provided by Spirometer. This is translated in human factors as a dysfunction of small and medium airways of patients’. Furthermore Spirometry element FEV1, is not as valuable parameter as the European Medical Agency supports. Finally it has been observed that Omazulimab treatment improves respiratory function and makes the connection among associated elements weaker.

References

  1. Ackan, H., Astashyn, A. and Bronnimann, H., 2008. Deterministic algorithms for sampling count data. In Data & Knowledge Engineering, Volume 64, pp 405- 418.
  2. Amato, F., Fasolino, A. R., Mazzeo, A., Moscato, V., Picariello, A., Romano, S., and Tramontana, P., 2011. Ensuring semantic interoperability for e-health applications, In Complex, Intelligent and Software Intensive Systems (CISIS), International Conference on IEEE, ISBN: 978-1-61284-709-2, June 30 - July 2, Seoul, Korea, pp 315-320.
  3. Bousquet, J., Rabe, K., Humbert, M., Chung, K.F., Berger, W., Fox, H., Ayre, G., Chen, H., Thomas, K., Blogg, M. and Holgate, S., .2007. Predicting and evaluating response to omalizumab in patients with severe allergic asthma. In Respiratory Medicine, Volume 101, pp 1483-1492.
  4. Chen, C., Horng, S.J. and Huang, C. P., 2011. Locality sensitive hashing for Sampling-based algorithms in association rule mining. In Expert Systems with Applications, Volume 38, pp 12388-12397.
  5. Cokpinar, S. and Gundem, T.I., 2012. Positive and negative association rule mining on XML data streams in database as a service concept. In Expert Systems with Applications, Volume 39, pp 7503-7511.
  6. Duneja, E. and Sachan, A. K., 2012. A Survey on Frequent Itemset Mining with Association Rules. In Computer Applications, Volume 46, pp 18-24.
  7. Guang-Yuana, L., Dan-Yanga, C. and Jian-Wei, G., 2011. Association Rules Mining with Multiple Constraints. In Procedia Engineering, Volume 15, pp 1678 - 1683.
  8. Holgate, S., Buhl, R., Bousquet, J., Smith, N., Panahloo, Z. and Jimenez, P., 2009. The use of omalizumab in the treatment of severe allergic asthma: A clinical experience update. In Respiratory Medicine, Volume 103, pp 1098-1113.
  9. Ke, J., Zhan, Y., Chen, X., and Wang, M., 2013. The retrieval of motion event by associations of temporal Frequent Pattern growth. In Future Generation Computer Systems, Volume 29, pp 442-450.
  10. Korn, S., Thielen, A., Seyfried, S., Taube, C., Kornmann, O. and Buhl, R., 2009. Omalizumab in patients with databases. In Computer and Information Science, Volume 23, pp 1-6.
  11. Lee, Y. C., Hong, T. P. and Lin, W. Y., 2005. Mining association rules with multiple minimum supports using maximum constraints. In Approximate Reasoning, Volume 40, pp 44-54.
  12. Lin, K. C., Lia, I.E. and Chen, Z. S., 2011. An improved frequent pattern growth method for mining association rules. In Expert Systems with Applications, Volume 38, pp 5154-5161.
  13. Liu, X., Zhai, K. and Pedrycz, W., 2012. An improved association rules mining method. In Expert Systems with Applications, Volume 39, pp 1362-1374.
  14. National Heart, Lung, and Blood Institute, 2007. Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma. In National Asthma Education and Prevention Program, 28 August.
  15. Nahar, J., Imama, T., Tickle, K. S. and Chen, Y. P. P., 2012. Association rule mining to detect factors which contribute to heart disease in males and females Expert. In Systems with Applications, in press.
  16. Nowak, D., 2006. Management of asthma with antiimmunoglobulin E: A review of clinical trials of omalizumab. In Respiratory Medicine, Volume 100, pp 1907-1917.
  17. Slavin, R., Ferioli, C., Tannenbaum, S., Martin, C., Blogg, M. and Lowe, P., 2009. Asthma symptom reemergence after omalizumab withdrawal correlates well with increasing IgE and decreasing pharmacokinetic concentrations. In Allergy and Clinical Immunology, Volume 123, pp 107-113.
  18. Stout, J. W., Smith, K., Zhou, C., Solomon, C., Dozor, A. J., Garrison, M. M. and Mangione-Smith, R., 2012. Learning from a Distance: Effectiveness of Online Spirometry Training in Improving Asthma Care. In Academic Pediatrics, Volume 12, pp 88-95.
  19. Tzortzaki, E., Georgiou, A., Kampas, D., Lemessios, M., Markatos, M., Adamidi, T., Samara, K., Skoula, G., Damianaki, A., Schiza, S., Tzanakis, N., and Siafakas, N., 2012. Long-term omalizumab treatment in severe allergic asthma: The South-Eastern Mediterranean “real-life” experience. In Pulmonary Pharmacology & Therapeutics, Volume 25, pp 77-82.
  20. Umarani, V. and Punithavalli, M., 2010. Sampling based Association Rules Mining- A Recent Overview. In Computer Science and Engineering, Volume 2, pp 314-318.
  21. Umarani, V. and Punithavalli, M., 2011. An Empirical Analysis over the Four Different Methods of Progressive Sampling-Based Association Rule Mining. In Scientific Research, Volume 66, pp 620- 630.
  22. Ykhlef, M., 2011. A Quantum Swarm Evolutionary Algorithm for mining association rules in large.
  23. Yu, K.M., Zhou, J., Hong,T.P. and Zhou, J.L., 2010. A load-balanced distributed parallel mining algorithm. In Expert Systems with Applications, Volume 37, pp 2459-2464.
Download


Paper Citation


in Harvard Style

Matsopoulos S. and Plekhanova V. (2014). Connections of Reduced Performance Health Data for Severe Persistent Uncontrolled Allergic Asthma Treated by Omazulimab . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014) ISBN 978-989-758-010-9, pages 276-286. DOI: 10.5220/0004763802760286


in Bibtex Style

@conference{healthinf14,
author={Stefanos Matsopoulos and Valentina Plekhanova},
title={Connections of Reduced Performance Health Data for Severe Persistent Uncontrolled Allergic Asthma Treated by Omazulimab},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)},
year={2014},
pages={276-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004763802760286},
isbn={978-989-758-010-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)
TI - Connections of Reduced Performance Health Data for Severe Persistent Uncontrolled Allergic Asthma Treated by Omazulimab
SN - 978-989-758-010-9
AU - Matsopoulos S.
AU - Plekhanova V.
PY - 2014
SP - 276
EP - 286
DO - 10.5220/0004763802760286