Abstract
Critical health care is one of the most difficult areas to make decisions. Every day new situations appear and doctors need to decide very quickly. Moreover, it is difficult to have an exact perception of the patient situation and a precise prediction on the future condition. The introduction of Intelligent Decision Support Systems (IDSS) in this area can help the doctors in the decision making process, giving them an important support based in new knowledge. Previous work has demonstrated that is possible to use data mining models to predict future situations of patients. Even so, two other problems arise: i) how fast; and ii) how accurate? To answer these questions, an ensemble strategy was experimented in the context of INTCare system, a pervasive IDSS to automatically predict the organ failure and the outcome of the patients throughout next 24 hours. This paper presents the results obtained combining real-time data processing with ensemble approach in the intensive care unit of the Centro Hospitalar do Porto, Porto, Portugal.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Portela, F., Santos, M.F., Gago, P., Silva, Á., Rua, F., Abelha, A., Machado, J., Neves, J.: Enabling real-time intelligent decision support in intensive care. In: 25th European Simulation and Modelling Conference, ESM 2011, 446 p. (2011)
Vilas-Boas, M., Santos, M.F., Portela, F., Silva, Á., Rua, F.: Hourly prediction of organ failure and outcome in intensive care based on data mining techniques. In: ICEIS, Funchal, Madeira, Portugal (2010)
Kantardzic, M.: Data mining: concepts, models, methods, and algorithms. Wiley-IEEE Press (2011)
Gago, P., Santos, M.F.: Towards an Intelligent Decision Support System for Intensive Care Units. In: 18th European Conference on Artificial Intelligence, Greece (2008)
Gago, P., Santos, M.F.: Evaluating Hybrid Ensembles for Intelligent Decision Support for Intensive Care. In: Okun, O., Valentini, G. (eds.) Applications of Supervised and Unsupervised Ensemble Methods. SCI, vol. 245, pp. 251–265. Springer, Heidelberg (2009)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Portela, F., Pinto, F., Santos, M.F.: Data Mining Predictive Models For Pervasive Intelligent Decision Support In Intensive Care Medicine. In: KMIS 2012. INSTICC, Barcelona (2012)
McMillen, R.E.: End of life decisions: Nurses perceptions, feelings and experiences. Intensive and Critical Care Nursing 24, 251–259 (2008)
Mador, R.L., Shaw, N.T.: The impact of a Critical Care Information System (CCIS) on time spent charting and in direct patient care by staff in the ICU: A review of the literature. International Journal of Medical Informatics 78, 435–445 (2009)
Häyrinen, K., Saranto, K., Nykänen, P.: Definition, structure, content, use and impacts of electronic health records: A review of the research literature. International Journal of Medical Informatics 77, 291–304 (2008)
Orwat, C., Graefe, A., Faulwasser, T.: Towards pervasive computing in health care - A literature review. BMC Medical Informatics and Decision Making 8(26) (2008)
Lyerla, F., LeRouge, C., Cooke, D.A., Turpin, D., Wilson, L.: A Nursing Clinical Decision Support System and potential predictors of Head-of-Bed position for patients receiving Mechanical Ventilation. American Journal of Critical Care 19, 39–47 (2010)
Portela, F., Santos, M.F., Silva, Á., Machado, J., Abelha, A.: Enabling a Pervasive approach for Intelligent Decision Support in Critical Health Care. In: HCist 2011, Algarve, Portugal, p. 10 (2011)
Silva, Á., Cortez, P., Santos, M.F., Gomes, L., Neves, J.: Rating organ failure via adverse events using data mining in the intensive care unit. Artificial Intelligence in Medicine 43, 179–193 (2008)
Santos, M.F., Portela, F., Vilas-Boas, M., Machado, J., Abelha, A., Neves, J., Silva, A., Rua, F.: Information Modeling for Real-Time Decision Support in Intensive Medicine. In: Chen, S.Y., Li, Q. (eds.) Proceedings of the 8th International Conference on Applied Computer and Applied Computational Science, pp. 360–365. World Scientific and Engineering Acad. and Soc., Athens (2009)
Tamayo, P., Berger, C., Campos, M., Yarmus, J., Milenova, B., Mozes, A., Taft, M., Hornick, M., Krishnan, R., Thomas, S.: Oracle Data Mining. In: Data Mining and Knowledge Discovery Handbook, pp. 1315–1329 (2005)
Concepts, O.D.M.: 11g Release 1 (11.1). Oracle Corp. 2007 (2005)
Quinlan, J.R.: C4. 5: programs for machine learning. Morgan kaufmann (1993)
Cherkassky, V., Mulier, F.: Vapnik-Chervonenkis(VC) learning theory and its applications. IEEE Transactions on Neural Networks 10, 985–987 (1999)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 1145–1159 (1997)
Wang, W., Partridge, D., Etherington, J.: Hybrid ensembles and coincident-failure diversity, pp. 2376–2381. IEEE (2001)
Ting, K.M., Witten, I.H.: Issues in stacked generalization. Arxiv preprint arXiv:1105.5466 (2011)
Wolpert, D.H.: Stacked generalization*. Neural Networks 5, 241–259 (1992)
Portela, F., Gago, P., Santos, M.F., Silva, A., Rua, F., Machado, J., Abelha, A., Neves, J.: Knowledge Discovery for Pervasive and Real-Time Intelligent Decision Support in Intensive Care Medicine. In: Publication, S.-A.T. (ed.) KMIS 2011. Springer, Paris, France (2011)
Guy, W.: ECDEU assessment manual for psychopharmacology, Rockville, Md (1976)
Guy, W., Modified From: Rush, J., et al.: Clinical Global Impressions (CGI) Scale. Psychiatric Measures. APA (2000)
Portela, F., Aguiar, J., Santos, M.F., Silva, Á., Rua, F.: Pervasive Intelligent Decision Support System — Technology Acceptance in Intensive Care Units. In: Rocha, Á., Correia, A.M., Wilson, T., Stroetmann, K.A. (eds.) Advances in Information Systems and Technologies. AISC, vol. 206, pp. 279–292. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Portela, F., Santos, M.F., Machado, J., Abelha, A., Silva, Á. (2013). Pervasive and Intelligent Decision Support in Critical Health Care Using Ensembles. In: Bursa, M., Khuri, S., Renda, M.E. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2013. Lecture Notes in Computer Science, vol 8060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40093-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-40093-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40092-6
Online ISBN: 978-3-642-40093-3
eBook Packages: Computer ScienceComputer Science (R0)