Abstract
There are many standard methods used at Intensive Care Units (ICU) in order to overview patient's situation. We present in this paper a new method that outperforms the prediction accuracy of each medical standard method by combining them using Machine Learning (ML) inspired classification approaches. We have used different Machine Learning algorithms to compare the accuracy of our new method with other existing approaches used by ML community. The new method is an hybrid made between the Nearest Neighbour and the Naive Bayes classification methods. Experimental results show that this new approach is better than any standard method used in the prediction of survival of ICU patients, and better than the combination of these medical approaches done by using standard ML algorithms.
This work was supported by the Gipuzkoako Foru Aldundi Txit Gorena under OF097/1998 grant and by the PI 96/12 grant from the Eusko Jaurlaritza - Hezkuntza, Unibertsitate eta Ikerkuntza Saila.
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References
D. Aha, D. Kibler and M.K. Albert (1991): “Instance-Based learning algorithms”, Machine Learning 6, 37–66
B.V. Dasarathy (1991): “Nearest Neighbor (NN) Norms: NN Pattern Recognition Classification Techniques”, IEEE Computer Society Press
W.A. Knaus, E.A. Draper, D.P. Wagner, J.E. Zimmerman (1985): “APACHE II: A severity of disease classification system”. Crit Care Med 13:818–829
R. Kohavi, D. Sommerfield and J. Dougherty (1997): “Data mining using MLC++, a Machine Learning Library in C++”, International Journal of Artificial Intelligence Tools Vol. 6, num. 4, 537–566. [ http://www.sgi.com/Technology/mlc/ ]
J.R. Le Gall, S. Lemeshow, F. Saulnier (1993): “A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study”. JAMA 270:2957–2963
S. Lemeshow, D. Teres, J. Klar, J.S. Avrunin, S.H. Gehlbach, J. Rapoport (1993): “Mortality Probability Models (MPM II) based on an international cohort of intensive
J.R. Quinlan (1986): “Induction of Decision Trees”, Machine Learning 1, 81–106
B. Sierra and P. Larrañaga (1998): “Predicting survival in malignant skin melanoma using Bayesian Networks automatically induced by genetic algorithms. An empirical comparision between different approachess”, Artificial Intelligence in Medicine 14, 215–230
Stone, M.: “Cross-validation choice and assessment of statistical procedures”. Journal Royal of Statistical Society 36 (1974) 111–147
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© 1999 Springer-Verlag Berlin Heidelberg
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Sierra, B. et al. (1999). Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit?. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_40
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DOI: https://doi.org/10.1007/3-540-48720-4_40
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