Browse > Article
http://dx.doi.org/10.3745/KTSDE.2020.9.4.137

A Classification Method of Delirium Patients Using Local Covering-Based Rule Acquisition Approach with Rough Lower Approximation  

Son, Chang Sik (대구경북과학기술원 지능형로봇연구부)
Kang, Won Seok (대구경북과학기술원 지능형로봇연구부)
Lee, Jong Ha (계명대학교 의과대학 의용공학과)
Moon, Kyoung Ja (계명대학교 간호학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.9, no.4, 2020 , pp. 137-144 More about this Journal
Abstract
Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.
Keywords
Delirium; Geriatric Syndrome; Rough Set Approximation; LEM2; Classification Rule;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 K. J. Moon and H. O. Park, "Outcomes of Patients with Delirium in Long-term Care Facilities," Journal of Gerontological Nursing, Vol.44, No.9, pp.41-50, 2018.   DOI
2 Z. J. Lipowski, "Delirium (Acute Confusional States)," Journal of the American Medical Association (JAMA), Vol.258, No.13, pp.1789-1792, 1987.   DOI
3 T. M. Brown and M. F. Boyle, "Delirium," British Medical Journal (BMJ), Vol.325, No.7365, pp.644-647, 2002.   DOI
4 C. C. Bell, "DSM-IV: Diagnostic and Statistical Manual of Mental Disorders," Journal of the American Medical Association (JAMA), Vol.272, No.10, pp.828-829, 1994.   DOI
5 B. D. Ku and J. H. Hong, "Clinical Approach to the Patients with Delirium in the Neurocritical Care," Journal of Neurocritical Care, Vol.3, No.2, pp.27-37, 2010.
6 J. S. Park, J. J. Kim, S. J. Park, S. M. Kim, and J. Y. Park, "Clinical Course According to Antipsychotics Prescription Pattern in Delirium," Korean Journal of Psychosomatic Medicine, Vol.25, No.2, pp.120-128, 2017.   DOI
7 J. R. Fann, "The Epidemiology of Delirium: A Review of Studies and Methodological Issues," Seminars in Clinical Neuropsychiatry, Vol.5, No.2, pp.64-74, 2000.   DOI
8 S. K. Inouye, "Delirium in Older Persons," The New England Journal of Medicine, Vol.354, No.11, pp.1157-1165, 2006.   DOI
9 J. I. Salluh, M. Soares, J. M. Teles, D. Ceraso, N. Raimondi, V. S. Nava, P. Blasquez, S. Ugarte, C. Ibanez-Guzman, J. V. Centeno, M. Laca, G. Grecco, E. Jimenez, S. Arias-Rivera, C. Duenas, M. G. Rocha, and The DECCA(Delirium Epidemiology in Critical Care) Study Group, "Delirium Epidemiology in Critical Care (DECCA): an International Study," Critical Care, Vol.14, No.6, pp.R210, 2010.   DOI
10 G. Bucht, Y. Gustafson, and O. Sandberg, "Epidemiology of Delirium," Dementia and Geriatric Cognitive Disorders, Vol.10, No.5, pp.315-318, 1999.   DOI
11 C. O. Kim, "Delirium," The Korean Journal of Medicine, Vol.79, No.s2, pp.536-540, 2010.
12 M. Dubois, Y. Strobik, N. Bergeron, M. Dumont, and S. Dial, "Delirium in an Intensive Care Unit: a Study of Risk Factors," Intensive Care Medicine, Vol.27, No.8, pp.1297-1304, 2001.   DOI
13 E. Arend and M. Christensen, "Delirium in the Intensive Care Unit: a Review," Nursing in Critical Care, Vol.14, No.3, pp.145-154, 2009.   DOI
14 J. Y. Lee and J. Y. Bae, "The Effect Investigation of the Delirium by Bayesian Network and Radial Graph," The Koran Data and Information Science Society, Vol.22, No.5, pp.911-919, 2011.
15 A. Davoudi, A. Ebadi, P. Rashidi, T. O. Baslanti, A. Bihorac, and A. C. Bursian, "Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data," in Proceedings of IEEE International Symposium on Bioinformatics and Bioengineering (BIBE), Washington, DC, USA, 2017, pp.568-573.
16 J. P. Corradi, S. Thompson, J. F. Mather, C. M. Waszynski, and R. S. Dicks, "Prediction of Incident Delirium using a Random Forest Classifier," Journal of Medical Systems, Vol.42, No.12, pp.261, 2018.   DOI
17 J. Y. Oh, D. R. Cho, J. S. Park, S. H. Na, J. I. Kim, J. S. Heo, C. S. Shin, J. J. Kim, J. Y. Park, and B. R. Lee, "Prediction and Early Detection of Delirium in the Intensive Care Unit by using Heart Rate Variability and Machine Learning," Physiological Measurement, Vol.39, No.3, 035004, 2018.   DOI
18 J. W. Grzymala-Busse, "Rough Set Strategies to Data with Missing Attribute Values," In: L. T. Young, S. Ohsuga, C. J. Liau, X. Hu (eds) Foundations and Novel Approaches in Data Mining. Studies in Computational Intelligence, Vol.9, Springer, 2015.
19 H. N. Mufti, G. M. Hirsch, S. R. Abidi, and S. S. R. Abidi, "Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium after Cardiac Surgery: Models Development and Validation Study," JMIR Medical Informatics (JMI), Vol.7, No.4, e14993, 2019.   DOI
20 Z. Pawlak, "Rough Sets," International Journal of Computer and Information Sciences, Vol.11, No.5, pp.341-356, 1982.   DOI
21 Z. Pawlak and A. Skowron, "Rudiments of Rough Sets," Information Sciences, Vol.177, No.1, pp.3-27, 2007.   DOI
22 Z. Pawlak and A. Skowron, "Rough Sets and Boolean Reasoning," Information Sciences, Vol.177, No.1, pp.41-73, 2007.   DOI
23 J. W. Grzymala-Busse, "Rule Induction from Rough Approximations," In: J. Kacprzyk, W. Pedrycz (eds) Springer Handbook of Computational Intelligence, Springer, Berlin, Heidelberg, 2015.
24 U. M. Fayyad and K. B. Irani, "Multi-interval Discretization of Continuous-valued Attributes for Classification Learning," In: IJCAI, pp.1022-1029, 1993.
25 E. Frank, M. A. Hall, and I. H. Witten, The Weka Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques," Morgan Kaufmann, Fourth Edition, 2016.
26 J. W. Grzymala-Busse, "A New Version of the Rule Induction System LERS," Fundamenta Informaticae, Vol.31, No.1, pp.27-39, 1997.   DOI