Mining Clinical Process from Hospital Information System: A Granular Computing Approach

• Autorzy: Tsumoto Shusaku , Hirano Shoji , Kimura Tomohiro , Iwata Haruko

Identyfikatory

DOI

10.3233/FI-2021-2070

• Języki publikacji: EN

Abstrakty

EN

Data mining methods in medicine is a very important tool for developing automated decision support systems. However, since information granularity of disease codes used in hospital information system is coarser than that of real clinical definitions of diseases and their treatment, automated data curation is needed to extract knowledge useful for clinical decision making. This paper proposes automated construction of clinical process plan from nursing order histories and discharge summaries stored in hospital information system with curation of disease codes as follows. First, the system applies EM clustering to estimate subgrouping of a given disease code from clinical cases. Second, it decomposes the original datasets into datasets of subgroups by using granular homogenization. Thirdly, clinical pathway generation method is applied to the datasets. Fourthly, classification models of subgroups are constructed by using the analysis of discharge summaries to capture the meaning of each subgroup. Finally, the clinical pathway of a given disease code is output as the combination of the classifiers of subgroups and the the pathways of the corresponding subgroups. The proposed method was evaluated on the datasets extracted hospital information system in Shimane University Hosptial. The obtained results show that more plausible clinical pathways were obtained, compared with previously introduced methods.

Słowa kluczowe

EN

clinical pathway mining; hierarchical clustering; EM clustering; Hospital information syst Big data analytics

• Wydawca: Polskie Towarzystwo Matematyczne
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2021
• Tom: Vol. 182, nr 2
• Strony: 181--218
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr.

Twórcy

autor

Tsumoto Shusaku

• Faculty of Medicine, Shimane University, 89-1 Enya-cho Izumo 693-8501 Japan

autor

Hirano Shoji

• Faculty of Medicine, Shimane University, 89-1 Enya-cho Izumo 693-8501 Japan

autor

Kimura Tomohiro

• Faculty of Medicine, Shimane University, 89-1 Enya-cho Izumo 693-8501 Japan

autor

Iwata Haruko

• Faculty of Medicine, Shimane University, 89-1 Enya-cho Izumo 693-8501 Japan

Bibliografia

• [1] Topol E. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019. 25(1):44-56.
• [2] Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med, 2019. 25(1):24-50.
• [3] Motoda H (ed.). Active Mining. Number 79 in Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, 2002.
• [4] Tsumoto S, Yamaguchi T, Numao M, Motoda H (eds.). Active Mining, Second International Workshop, AM 2003, Maebashi, Japan, October 28, 2003, Revised Selected Papers, volume 3430 of Lecture Notes in Computer Science. Springer, 2005. ISBN 3-540-26157-5.
• [5] Iwata H, Hirano S, Tsumoto S. Maintenance and Discovery of Domain Knowledge for Nursing Care using Data in Hospital Information System. Fundam. Inform., 2015. 137(2):237-252. doi:10.3233/FI-2015-1177.
• [6] Tsumoto Y, Iwata H, Hirano S, Tsumoto S. Construction of Clinical Pathway Using Dual Clustering. Neuroscience and Biomedical Engineering, 2015. 3.
• [7] Tsumoto S, Hirano S, Kimura T, Iwata H. From Hospital Big Data to Clinical Process: A Granular
• Computing Approach. In: Abe N, Liu H, Pu C, Hu X, Ahmed NK, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz JS (eds.), IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018. IEEE, 2018 pp. 2669-2678. doi:10.1109/BigData.2018.8622240.
• [8] McLachlan GJ, Peel D. Finite Mixture Models. Wiley, New York, 2000.
• [9] Yang W, Su Q. Process mining for clinical pathway: Literature review and future directions. In: 2014 11th International Conference on Service Systems and Service Management (ICSSSM). 2014 pp. 1-5.
• [10] Xu X, Jin T, Wei Z, Lv C, Wang J. TCPM: Topic-Based Clinical Pathway Mining. In: 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). 2016 pp. 292-301.
• [11] Xu X, Jin T, Wang J. Summarizing patient daily activities for clinical pathway mining. In: 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom). 2016 pp. 1-6.
• [12] Zhang X, Chen S. Pathway identification via process mining for patients with multiple conditions. In: 2012 IEEE International Conference on Industrial Engineering and Engineering Management. 2012 pp. 1754-1758.
• [13] Le HH, Edman H, Honda Y, Kushima M, Yamazaki T, Araki K, Yokota H. 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). 2017 pp. 1726-1731.
• [14] Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Hsu MC. Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Trans. on Knowl. and Data Eng., 2004. 16(11):1424-1440. doi: http://dx.doi.org/10.1109/TKDE.2004.77. Member-Jian Pei and Senior Member-Jiawei Han and Member-Umeshwar Dayal,
• [15] binti Omar N, Supriyanto E, Al-Ashwal RH, binti Abdul Wahab A. Personalized Clinical Pathway for Heart Failure Management. In: 2018 International Conference on Applied Engineering (ICAE). 2018 pp. 1-5. 218 S. Tsumoto et al / Clinical Pathway Mining
• [16] Dagliati A, Sacchi L, Cerra C, Leporati P, De Cata P, Chiovato L, Holmes JH, Bellazzi R. 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). 2014 pp. 240-243.
• [17] Melton G, McDonald CJ, Tang PC, Hripcsak G. Elecronic Health Records, chapter 16. Springer, New York, fourth edition, 2014.
• [18] Tsumoto S, Iwata H, Hirano S, Tsumoto Y. Similarity-based behavior and process mining of medical practices. Future Generation Comp. Syst., 2014. 33:21-31. doi:10.1016/j.future.2013.10.014.
• [19] Igakutsushinsha. Quick Reference of DPC points (in Japanese). Igakutsushinsha, Tokyo, 2020. [20] Hyde E, Murphy B. Computerized clinical pathways (care plans): piloting a strategy to enhance quality patient care. Clin Nurse Spec, 2012. 26(4):277-282.
• [21] Ward M, Vartak S, Schwichtenberg T, Wakefield D. Nurses’ perceptions of how clinical information system implementation affects workflow and patient care. Comput Inform Nurs, 2011. 29(9):502-511.
• [22] Iwata H, Hirano S, Tsumoto S. Construction of Clinical Pathway based on Similarity-based Mining in Hospital Information System. In: Proceedings of the Second International Conference on Information Technology and Quantitative Management, ITQM 2014, National Research University Higher School of Economics (HSE), Moscow, Russia, June 3-5, 2014. 2014 pp. 1107-1115. doi:10.1016/j.procs.2014.05.366.
• [23] Tsumoto S, Hirano S, Iwata H. Construction of clinical pathway from histories of clinical actions in hospital information system. In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016. 2016 pp. 1972-1981. doi:10.1109/BigData.2016.7840819.
• [24] Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Caski F (eds.), Proceedings of the 2nd International Symposium on Information Theory. Akadimiai Kiado, Budapest, 1973 pp. 267-281.
• [25] Tsumoto S, Kimura T, , Hirano S. Determinaion of Diseases from Discharge Summaries – A Text Mining Approach –. Review of Socionetwork Strategies, 2021. 15:49-66.
• [26] Ishida M. RMeCab. 2016.
• [27] Jones KS. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 1972. pp. 11-21.
• [28] Rajaraman A, Ullman J. Data Mining, pp. 1-17. Cambridge University Press, 2011.
• [29] Tsumoto S, Kimura T, Iwata H, Hirano S. Construction of Discharge Summaries Classifier. In: 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017, Park City, UT, USA, August 23-26, 2017. IEEE. ISBN 978-1-5090-4881-6, 2017 pp. 74-82. doi:10.1109/ICHI.2017.92.
• [30] Kim JH. Estimating Classification Error Rate: Repeated Cross-validation, Repeated Hold-out and Bootstrap. Comput. Stat. Data Anal., 2009. 53(11):3735-3745. doi:10.1016/j.csda.2009.04.009.
• [31] Schwarz G. Estimating the dimension of a model. Ann. Statist., 1978. pp. 461-464.
• [32] Janusz A, Slezak D, Nguyen HS. Unsupervised Similarity Learning from Textual Data. Fundam. Informaticae, 2012. 119(3-4):319-336. doi:10.3233/FI-2012-740.
• [33] Bazan JG, Szczur A, Dydo L, Wojtowicz H, Szpyrka M. Classifiers for Behavioral Patterns Identification Induced from Huge Temporal Data. Fundam. Informaticae, 2016. 143(1-2):19-34. doi:10.3233/FI-2016-130

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023). (PL)