Medical data exploration based on the heterogeneous data sources aggregation system
Andrzej Opaliński, Krzysztof Regulski, Barbara Mrzygłód, Mirosław Głowacki, Aleksander Kania, Paweł Nastałek, Natalia Celejewska-Wójcik, Grażyna Bochenek, Krzysztof Sładek
DOI: http://dx.doi.org/10.15439/2019F258
Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 591–597 (2019)
Abstract. The paper presents the implementation and use of the IT system implemented in the Department of Pulmonology of The University Hospital in Cracow. The system integrates data from heterogeneous sources of therapy, diagnosis and medical test results of patients with Obstructive Sleep Apnea (OSA). The article presents the main architectural assumptions of the system, as well as an example of data mining analyzes based on the data served by the system. The example of the research aims to present the possibilities offered by the integration of clinical data in telemedicine and the diagnosis of patients with sleep disordered breathing that may lead to certain comorbidities and premature death.
References
- H.Y. Chiu, P.Y. Chen, L.P. Chuang, N.H. Chen, Y.K. Tu, Y.J. Hsieh, Y.C. Wang, and C. Guilleminault. Diagnostics accuracy of the berlin questionnaire, stop-bang, stop, and epworth sleepiness scale in detecting obstructive sleep apnea: A bivariate meta-analysis. Sleep Medicine Reviews, 36:57–70, 2016.
- P. Escourrou, L. Grote, T. Penzel, W. T. Mcnicholas, J. Verbraecken, R. Tkacova, and F. Barbé. The diagnostic method has a strong influence on classification of obstructive sleep apnea. Journal of sleep research, 24(6):730–738, 2015.
- A. Glowacz. Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics, 137:82–89, 2018.
- J. A. Hartigan and M. A. Wong. Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society, 28(1):100–108, 1979.
- J. Hedner, L. Grote, M. Bonsignore, W. McNicholas, P. Lavie, G. Parati, P. Sliwinski, F. Barbé, W. De Backer, P. Escourrou, I. Fietze, J. A. Kvamme, C. Lombardi, O. Marrone, J. F. Masa, J. M. Montserrat, T. Penzel, M. Pretl, R. Riha, D. Rodenstein, T. Saaresranta, R. Schulz, R. Tkacova, G. Varoneckas, A. Vitols, H. Vrints, and J. Zielinski. The european sleep apnoea database (esada): report from 22 european sleep laboratories. Eur Respir J, 38:635–42, 2011.
- A. K. Jain. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8):651–666, 2010.
- P. Jennum, R. Ibsen, and J. Kjellberg. Morbidity prior to a diagnosis of sleep-disordered breathing: a controlled national study. J Clin Sleep Med, 9(2):103–108, 2013.
- P. J. Jennum, P. Larsen, C. Cerqueira, T. Schmidt, and P. Tønnesen. The danish national database for obstructive sleep apnea. Clinical Epidemiology, 8:573–576, 2016.
- Riha R.L. Jennum, P. Epidemiology of sleep apnoea/hypopnoea syndrome and sleep-disordered breathing. Eur Respir J, 33:907–14, 2009.
- B. D. Kent, L. Grote, M. R. Bonsignore, T. Saaresranta, J. Verbraecken, and P. Lévy. Sleep apnoea severity independently predicts glycaemic health in nondiabetic subjects: the esada study. European Respiratory Journal, 44(1):130–139, 2014.
- J. B. MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Symposium on Math, pages 281–297, Statistics, and Probability, Berkeley, CA, 1967. University of California Press.
- O. Marrone, S. Battaglia, P. Steiropoulos, O.K. Basoglu, J.A. Kvamme, S. Ryan, J.L. Pepin, J. Verbraecken, L. Grote, J. Hedner, and M.R. Bonsignore. Chronic kidney disease in european patients with obstructive sleep apnea: the esada cohort study. J Sleep Res, 25:739–45, 2016.
- E. Nawarecki, S. Kluska-Nawarecka, and K. Regulski. Multi-aspect character of the man-computer relationship in a diagnostic-advisory system, pages 85–102. Springer-Verlag, 2012.
- American Academy of Sleep Medicine. International classification of sleep disorders: diagnostic and coding manual. Amer Academy of Sleep Medicine, Westchester, Illinois, USA, 2005.
- A. Opaliński, P. Nastałek, B. Mrzygłód, N. Celejewska-Wójcik, M. Głowacki, G. Bochenek, K. Regulski, K. Sładek, and A. Kania. The system for integration of heterogeneous data sources in the domain of obstructive sleep apnea. In Economic Advance In Behavioral and Sociocultural Computing (B. E. S. C. ) eds Economic, editors, Proc.Conf. 4th International Conference on Behavioral, pages 1–6. Demazeau Y, 2017.
- D. Passali, G. Caruso, L.C. Arigliano, F.M. Passali, and L. Bellussi. Arigliano lc, passali fm, bellussi i. database application for patients with obstructive sleep apnoea syndrome. Acta Otorhinolaryngol Ital, 32:252–255, 2012.
- J. R. Quinlan. Induction on Decision Trees, Machine Learning. Kluwer Academic Publishers, Boston, 1986.
- K. Regulski, D. Wilk-KoÅĆodziejczyk, and G. Gumienny. Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine. The International Journal of Advanced Manufacturing Technology, 87(1):1077–1093, 2016.
- T. Saaresranta, J. Hedner, M. R. Bonsignore, R. L. Riha, W. T. McNicholas, T. Penzel, U. Anttalainen, J. A. Kvamme, M. Pretl, P. Sliwinski, and J. Verbraecken. Clinical phenotypes and comorbidity in european sleep apnoea patients. PLoS One, 11(10), 2016.
- White D.P. Amin R. et al. Somers, V.K. Sleep apnea and cardiovascular disease: an american heart association/american college of cardiology foundation scientific statement from the american heart association council for high blood pressure research professional education committee, council on clinical cardiology, stroke council, and council on cardiovascular nursing. in collaboration with the national heart, lung, and blood institute national center on sleep disorders research (national institutes of health). Circulation, 118:1080–111, 2008.
- Y. Song and Y. Lu. Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry, 27(2):130–135, 2015.
- R. Tkacova, W. T. McNicholas, M. Javorsky, I. Fietze, P. Sliwinski, G. Parati, L. Grote, and J. Hedner. Nocturnal intermittent hypoxia predicts prevalent hypertension in the european sleep apnoea database cohort study. Eur Respir J, 44:931–41, 2014.