Detection and Visualization of Variants in Typical Medical Treatment Sequences

  • Yuichi Honda
  • Muneo Kushima
  • Tomoyoshi Yamazaki
  • Kenji Araki
  • Haruo Yokota
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10494)

Abstract

Electronic Medical Records (EMRs) are widely used in many large hospitals. EMRs can reduce the cost of managing medical histories, and can also improve medical processes by the secondary use of these records. Medical workers including doctors, nurses, and technicians generally use clinical pathways as their guidelines for typical sequences of medical treatments. The medical workers traditionally generate the clinical pathways themselves based on their experiences. It is helpful for the medical workers to verify the correctness of existing clinical pathways or modify them by comparing the frequent sequential patterns in medical orders computationally extracted from EMR logs. Thinking that the EMR is a database and a typical clinical pathway is a frequent sequential pattern in the database in our previous work, we proposed a method to extract typical clinical pathways as frequent sequential patterns with treatment time information from EMR logs. These patterns tend to contain variants that are influential in verification and modification. In this paper, we propose an approach for detecting the variants in frequent sequential patterns of medical orders while considering time information. Since it is important to provide visual views of these variants so the results can be used effectively by the medical workers, we also develop an interactive graphical interface system for visualizing the results of variants in clinical pathways. The results of applying the approach to actual EMR logs in an university hospital are reported.

Keywords

Sequential pattern mining Electronic Medical Records Clinical pathways variant Visualization of clinical pathways 

References

  1. 1.
    Uragaki., K., Hosaka., T., Arahori., Y., Kushima., M., Yamazaki., T., Araki., K., Yokota., H.: Sequential pattern mining on electronic medical records with handling time intervals and the efficacy of medicines. In: First IEEE Workshop on ICT Solutions for Health. Proceedings of 21st IEEE International Symposium on Computers and Communications, pp. 20–25 (2016)Google Scholar
  2. 2.
    Wakamiya, S., Yamauchi, K.: What are the standard functions of electronic clinical pathways? Int. J. Med. Inform. 78, 543–550 (2009)CrossRefGoogle Scholar
  3. 3.
    Hirano., S., Tsumoto., S.: Clustering of order sequences based on the typicalness index for finding clinical pathway candidates. In: IEEE International Conference on Data Mining ICDM Workshops (2013)Google Scholar
  4. 4.
    Agrawal., R., Srikant., R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  5. 5.
    Chen, Y.-L., Chiang, M.-C., Ko, M.-T.: Discovering time-interval sequential patterns in sequence databases. Expert Syst. Appl. 25, 343–354 (2003)CrossRefGoogle Scholar
  6. 6.
    Pei., J., Han., J., Mortazavi-Asl., B., Pinto., H., Chen., Q., Dayal., U., Hsu., M-C.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of 2001 International Conference on Data Engineering, pp. 215–224 (2001)Google Scholar
  7. 7.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Disc. 1, 259–289 (1997)CrossRefGoogle Scholar
  8. 8.
    Achar., A., Laxman., S., Raajay., V., Sastry., P,S.: Discovering general partial orders from event streams. Technical report. arXiv:0902.1227v2 [cs.AI]. http://arxiv.org
  9. 9.
    Denshi Karte System WATATUMI (EMRs “WATATUMI"). http://www.corecreate.com/02_01_izanami.html
  10. 10.
    Miyazaki Daigaku Igaku Bu Fuzoku Byouin Iryo Jyoho Bu (Medical Informatics Division, Faculty of Medicine, University of Miyazaki Hospital). http://www.med.miyazaki-u.ac.jp/home/jyoho/
  11. 11.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuichi Honda
    • 1
  • Muneo Kushima
    • 2
  • Tomoyoshi Yamazaki
    • 2
  • Kenji Araki
    • 2
  • Haruo Yokota
    • 1
  1. 1.Department of Computer ScienceTokyo Institute of TechnologyTokyoJapan
  2. 2.Faculty of MedicineUniversity of Miyazaki HospitalMiyazakiJapan

Personalised recommendations