A Classification-Tree Hybrid Method for Studying Prognostic Models in Intensive Care
Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in Intensive Care (IC). At the heart of quality of care programs lie prognostic models whose predictions for a particular patient population may be used as a norm to which actual outcomes of that population can be compared. This paper motivates and suggests a method based on Machine Learning and Statistical ideas to study the behavior of current IC prognostic models for predicting in-hospital mortality. An application of this method to an exemplary logistic regression model developed on the IC data from the National Intensive Care Evaluation registry reveals the model’s weaknesses and suggests ways for developing improved prognostic models.
KeywordsPrognostic Model Brier Score Admission Type National Intensive Care Evaluation Probabilistic Logistic Model
Unable to display preview. Download preview PDF.
- 1.Abu-Hanna A, Lucas PJF. Prognostic Models in Medicine AI and Statistical Approaches, (Abu-Hanna A. and Lucas PJF, eds.). Special issue of Methods of Information in Medicine 2001, 40:1–5.Google Scholar
- 2.Bennett D, Bion J. ABC of Intensive Care. Organisation of Intensive Care. BMJ 1999; 318:1468–1470.Google Scholar
- 5.Hosmer D.W., Lemeshow S. Applied Logistic Regression, Wiley, New-York, 1989.Google Scholar
- 7.de Keizer N. An Infrastructure for Quality Assessment in Intensive Care; Prognostic Models and Terminological Systems. PhD Thesis, 2000, University of Amsterdam.Google Scholar
- 9.Kohavi R. Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid. Proc. of the Second Int. Conference on Knowledge Discovery and Data Mining. 1996; 202–207.Google Scholar
- 11.Long WJ. A Comparison of Logistic Regression to Decision-Tree Induction in a Medical Domain. Compt Bio Res 1993:74–97.Google Scholar
- 12.Lucas PJF, Abu-Hanna A. Prognostic Methods in Medicine (Lucas PJF and Abu-Hanna A. eds.). Special issue of Artificial Intelligence in Medicine. 1999; 15(2):105–119.Google Scholar