Abstract
One of the most important tasks of data mining in a hospital is to discover structured knowledge about decision-making, which is useful for the management of clinical processes. However, most of the data in a hospital information system are stored without classification labels or the meaning of clinical actions. Thus, unsupervised learning techniques are required for analysis. This paper proposes a method which induces a clinical pathway using sample and attribute clustering of the histories of nursing orders stored in a hospital information system. The method consists of the following ve steps: first, frequencies of nursing orders are extracted from a hospital information system as a dataset in which each row and column represents nursing orders and days of the week. Second, orders are classified into several groups using sample clustering. Then, attributes clustering is applied to the data for feature selection. Fourth, for each sample and attribute clustering, the number of clusters is obtained from the sequence of the height values, and following the results of attribute clustering, the original dataset is decomposed into sub-tables. Then, the second-to-fourth steps are repeated in a recursive way until the grouping of attributes (days) are stable. Finally, a new pathway will be constructed from all the induced results. The proposed method was evaluated on datasets extracted from a hospital information system. The experiment results show that the method is useful for the construction of a clinical pathway when the distribution of length of stay is uni-modular.
Similar content being viewed by others
References
Benaglia, T., Chauveau, D., Hunter, D. R., & Young, D. (2009). mixtools: An r package for analyzing finite mixture models. Journal of Statistical Software, 32(6), 1–29.
Bichindaritz, I. (2006). Memoire: A framework for semantic interoperability of case-based reasoning systems in biology and medicine. Artificial Intelligence in Medicine, 36(2), 177–192.
Binti Omar, N., Supriyanto, E., Al-Ashwal, R.H., Binti Abdul Wahab, A. (2018). Personalized clinical pathway for heart failure management. In 2018 International Conference on Applied Engineering (ICAE), pp. 1–5.
Cox, T., & Cox, M. (2000). Multidimensional scaling (2nd ed.). Boca Raton: Chapman & Hall/CRC.
Dagliati, A., Sacchi, L., Cerra, C., Leporati, P., De Cata, P., Chiovato, L., Holmes, J.H., & Bellazzi, R. (2014) . Temporal data mining and process mining techniques to identify cardiovascular risk-associated clinical pathways in type 2 diabetes patients. In IEEE-EMBS international conference on biomedical and health informatics (BHI), pp. 240–243.
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Hoboken: Wiley.
Hanada, E., Tsumoto, S., & Kobayashi, S. (2010). A “ubiquitous environment” through wireless voice/data communication and a fully computerized hospital information system in a university hospital. In H. Takeda (ed.) E-Health, IFIP Advances in Information and Communication Technology, vol. 335, pp. 160–168. Springer, Boston.
Hyde, E., & Murphy, B. (2012). Computerized clinical pathways (care plans): Piloting a strategy to enhance quality patient care. Clinical Nurse Specialist, 26(4), 277–282.
Iakovidis, D., & Smailis, C. (2012). A semantic model for multimodal data mining in healthcare information systems. Studies in Health Technology and Informatics, 180, 574–578.
Igakutsushinsha (ed.). (2020). Quick Reference of DPC points (in Japanese). Igakutsushinsha, Tokyo.
Iwata, H., Hirano, S., & Tsumoto, S. (2015). Maintenance and discovery of domain knowledge for nursing care using data in hospital information system. Fundamenta Informaticae, 137(2), 237–252. https://doi.org/10.3233/FI-2015-1177.
Le, H.H., Edman, H., Honda, Y., Kushima, M., Yamazaki, T., Araki, K., & Yokota, H. (2017). Fast generation of clinical pathways including time intervals in sequential pattern mining on electronic medical record systems. In 2017 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1726–1731.
Lee, S., & McElmurry, B. (2010). Capturing nursing care workflow disruptions: Comparison between nursing and physician workflows. Computers, Informatics, Nursing, 28(3), 151–159.
Leisch, F. (2004). Flexmix: A general framework for finite mixture models and latent class regression in r. Journal of Statistical Software, 11(8), 1–18.
McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.
Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., & Hsu, M.C. (2004). Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Transactions on Knowledge and Data Engineering 16(11), 1424–1440 . https://doi.org/10.1109/TKDE.2004.77, http://portal.acm.org/citation.cfm?id=1024872.1025077&coll=ACM&dl=ACM [Member-Jian Pei and Senior Member-Jiawei Han and Member-Umeshwar Dayal].
Rissanen, J. (2007). Information and complexity in statistical modeling. Berlin: Springer.
Shortliffe, E., & Cimino, J. (Eds.). (2006). Biomedical informatics: Computer applications in health care and biomedicine (3rd ed.). Berlin: Springer.
Tsumoto, S., & Hirano, S. (2010). Risk mining in medicine: Application of data mining to medical risk management. Fundamenta Informaticae, 98(1), 107–121.
Tsumoto, Y., & Tsumoto, S. (2010). Exploratory univariate analysis on the characterization of a university hospital: A preliminary step to data-mining-based hospital management using an exploratory univariate analysis of a university hospital. The Review of Socionetwork Strategies, 4(2), 47–63.
Tsumoto, Y., & Tsumoto, S. (2011). Correlation and regression analysis for characterization of university hospital (submitted). The Review of Socionetwork Strategies, 5(2), 43–55.
Ward, M., Vartak, S., Schwichtenberg, T., & Wakefield, D. (2011). Nurses’ perceptions of how clinical information system implementation affects workflow and patient care. Computers, Informatics, Nursing, 29(9), 502–511.
WHO (1993). ICD-10. ICD-10/World Health Organization. World Health Organization.
Xu, X., Jin, T., Wang, J. (2016). Summarizing patient daily activities for clinical pathway mining. In 2016 IEEE 18th international conference on e-health networking, applications and services (Healthcom), pp. 1–6
Xu, X., Jin, T., Wei, Z., Lv, C., & Wang, J. (2016). Tcpm: Topic-based clinical pathway mining. In 2016 IEEE first international conference on connected health: Applications, systems and engineering technologies (CHASE), pp. 292–301.
Yang, W., & Su, Q. (2014). Process mining for clinical pathway: Literature review and future directions. In 2014 11th international conference on service systems and service management (ICSSSM), pp. 1–5.
Zhang, X., & Chen, S. (2012). Pathway identification via process mining for patients with multiple conditions. In 2012 IEEE international conference on industrial engineering and engineering management, pp. 1754–1758.
Acknowledgements
This research is supported by a Grant-in-Aid for Scientific Research (B) 15H2750 and 18H03289 from Japan Society for the Promotion of Science (JSPS).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there are no conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tsumoto, S., Kimura, T. & Hirano, S. Mining Clinical Pathways Using Dual Clustering. Rev Socionetwork Strat 15, 287–307 (2021). https://doi.org/10.1007/s12626-021-00082-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12626-021-00082-9