Advertisement

Association Analysis of Medical Opinions About the Non-realization of Autopsies in a Mexican Hospital

  • Elayne Rubio Delgado
  • Lisbeth Rodríguez-Mazahua
  • Silvestre Gustavo Peláez-Camarena
  • José Antonio Palet Guzmán
  • Asdrúbal López-Chau
Chapter
Part of the Management and Industrial Engineering book series (MINEN)

Abstract

Hospital autopsy rates around the world have dramatically decreased in frequency in the past years. In that sense, as physicians are very close to that kind of practice, the opinions of doctors might help to clarify the reasons and characteristics of the decline of this important medical procedure. This chapter explains how, to the effects of this study, data mining techniques were applied to perform an analysis of medical opinions about the practice of autopsies in a hospital of Veracruz, Mexico. These opinions were obtained from a survey, applied to 85 doctors of the hospital. The application of data mining techniques allowed the construction of a model, which is represented by a set of rules. The rules suggest some factors that are related to the decrease of the realization of autopsies in the hospital. All this was achieved in a framework where support and confidence thresholds were applied. Likewise, the results were refined by the addition of an objective statistic measure, named Lift, which helps filter out uninteresting association rules.

Keywords

Association rules Autopsy Data mining Mexican hospital 

Notes

Acknowledgements

The authors are very grateful to the National Technological of Mexico for supporting this work. Also, this chapter was sponsored by the National Council of Science and Technology (CONACYT), as well as by the Public Education Secretary (SEP) through PRODEP.

References

  1. Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: ACM sigmod record. ACM, pp 207–216Google Scholar
  2. Aher SB, Lobo L (2012) A comparative study of association rule algorithms for course recommender system in e-learning. Int J Comput Appl 39:48–52Google Scholar
  3. Antonelli D, Baralis E, Bruno G, et al (2015) Meta: characterization of medical treatments at different abstraction levels. ACM Trans Intell Syst Technol 6:57. doi: 10.1145/2700479
  4. Bathla H, Kathuria K (2015) Apriori algorithm and filtered association in association rule mining. Int J Comput Sci Mob Comput 4:299–306Google Scholar
  5. Cao B, Kong X, Kettering C et al (2015) Determinants of HIV-induced brain changes in three different periods of the early clinical course: a data mining analysis. NeuroImage Clin 9:75–82. doi: 10.1016/j.nicl.2015.07.012 CrossRefGoogle Scholar
  6. Cheng C-W, Chanani N, Maher K, Wang MD (2014) icuARM-II: improving the reliability of personalized risk prediction in pediatric intensive care units. In: Proceedings of the 5th ACM conference on bioinformatics, computational biology, and health informatics, ACM, pp 211–219Google Scholar
  7. Dange MAA, Siddiqui S (2016) Survey on assess co-morbid risk of diabetes mellitus by using split and merge association rule summarization techniques. Int J 1(6)Google Scholar
  8. Flach PA, Lachiche N (2001) Confirmation-guided discovery of first-order rules with Tertius. Mach Learn 42:61–95. doi: 10.1023/A:1007656703224
  9. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: ACM Sigmod Record. ACM, pp 1–12Google Scholar
  10. Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques, 3rd ed. Morgan KaufmannGoogle Scholar
  11. Hayashi Y, Yukita S (2016) Rule extraction using recursive-rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset. Inf Med Unlocked 2:92–104. doi: 10.1016/j.imu.2016.02.001
  12. Hwang S, Kwak D, Kim H, et al (2015) Association rule mining in Korean herbal prescriptions of the early 20th century. Integr Med Res 4:107. doi: 10.1016/j.imr.2015.04.186
  13. Idoudi R, Ettabaa KS, Solaiman B, Hamrouni K (2016) Ontology knowledge mining based association rules ranking. Procedia Comput Sci 96:345–354. doi: 10.1016/j.procs.2016.08.147 CrossRefGoogle Scholar
  14. Kira K, Rendell LA (1992) A practical approach to feature selection. In: International conference on machine learning. Proceedings of the ninth international workshop on machine learning, pp 249–256Google Scholar
  15. Lee BJ, Kim JY (2015) Indicators of hypertriglyceridemia from anthropometric measures based on data mining. Comput Biol Med 57:201–211CrossRefGoogle Scholar
  16. Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Proceedings of the fourth international conference on knowledge discovery and data mining (KDD), American Association for Artificial IntelligenceGoogle Scholar
  17. Muangprathub J, Jareonsuk Y, Sealiw A (2016) A web-based medical diagnostic system using data mining technique. J Telecommun Electron Comput Eng 8:37–41Google Scholar
  18. Oviedo Carrascal EA, Oviedo Carrascal AI, Vélez Saldarriaga GL (2015) Minería de datos: aportes y tendencias en el servicio de salud de ciuda-des inteligentes. Rev Politécnica 11(20):111–120Google Scholar
  19. Pérez AMF, Guzmán EL (2012) An approach to the risk analysis of diabetes mellitus type 2 in a health care provider entity of Colombia using business intelligence. In: 2012 6th Euro American conference on Telematics and Information Systems (EATIS), IEEE, pp 1–8Google Scholar
  20. Ramakrishnan S, Rakesh A (2005) Mining sequential patterns: generalizations and performance improvement. In: Proceedings of the 5th international conference on extending database technology. Springer, Avignon, France, pp 1–17Google Scholar
  21. Rubio Delgado E, Rodríguez-Mazahua L, Peláez-Camarena SG, Abud-Figueroa MA, Palet Guzman JA, López-Chau A, Alor-Hernández G (2017) Preliminary results of an analysis using association rules to find relations between medical opinions about the non-realization of autopsies in a Mexican hospital. Second international workshop on intelligent decision support system for industry, Research in computing science (in press)Google Scholar
  22. Sanz-Ortiz J, Mayorga M, Martín A (2011) Autopsy in clinical oncology: is it in crisis? Med Clin (Barc) 137:317–320CrossRefGoogle Scholar
  23. Scheffer T (2001) Finding association rules that trade support optimally against confidence. In: Proceedings of the 5th European conference (PKDD). Springer, Berlin, pp 424–435Google Scholar
  24. Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-Wesley, BostonGoogle Scholar
  25. Tang V, Cheng SWY, Choy KL et al (2016) An intelligent medical replenishment system for managing the medical resources in the healthcare industry. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), IEEE, pp 154–161Google Scholar
  26. Timarán Pereira R, Yépez Chamorro MC (2012) La minería de datos aplicada al descubrimiento de patrones de supervivencia en mujeres con cáncer invasivo de cuello uterino. Univ y salud 14:117–129Google Scholar
  27. Vemulapalli V, Qu J, Garren JM et al (2016) Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data. Artif Intell Med 74:1–8. doi: 10.1016/j.artmed.2016.11.001
  28. Wang C, Guo X-J, Xu J-F, et al (2012) Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systems. PLoS One 7:e40561. doi: 10.1371/journal.pone.0040561

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Elayne Rubio Delgado
    • 1
  • Lisbeth Rodríguez-Mazahua
    • 1
  • Silvestre Gustavo Peláez-Camarena
    • 1
  • José Antonio Palet Guzmán
    • 2
  • Asdrúbal López-Chau
    • 3
  1. 1.División de Estudios de Posgrado e InvestigaciónInstituto Tecnológico de OrizabaOrizabaMexico
  2. 2.Hospital Regional de Rio Blanco, H.R.R.BRío BlancoMexico
  3. 3.Universidad Autónoma del Estado de MéxicoZumpangoMexico

Personalised recommendations