C4.5: programs for machine learning
C4.5: programs for machine learning
A comparison of logistic regression to decision-tree induction in a medical domain
Computers and Biomedical Research
Machine learning and statistics: the interface
Machine learning and statistics: the interface
Machine Learning
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Machine Learning
Expert Systems with Applications: An International Journal
Internet as a knowledge base for medical diagnostic assistance
Expert Systems with Applications: An International Journal
Discovery and inclusion of SOFA score episodes in mortality prediction
Journal of Biomedical Informatics
Artificial Intelligence in Medicine
Discovery and Integration of Organ-Failure Episodes in Mortality Prediction
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Gaining insight through case-based explanation
Journal of Intelligent Information Systems
Comparing data mining methods with logistic regression in childhood obesity prediction
Information Systems Frontiers
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Predicting the outcome of patients with subarachnoid hemorrhage using machine learning techniques
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Artificial Intelligence in Medicine
Glaucoma Classification Model Based on GDx VCC Measured Parameters by Decision Tree
Journal of Medical Systems
Predicting mortality in the intensive care using episodes
IWINAC'05 Proceedings of the First international conference on Mechanisms, Symbols, and Models Underlying Cognition: interplay between natural and artificial computation - Volume Part I
A comparative modelling analysis of firm performance
International Journal of Data Analysis Techniques and Strategies
Measuring performance in health care: case-mix adjustment by boosted decision trees
Artificial Intelligence in Medicine
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Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in the Intensive Care (IC). Currently there are various international quality of care programs for the evaluation of IC. At the heart of such quality of care programs lie prognostic models whose prediction of patient mortality can be used as a norm to which actual mortality is compared. The current generation of prognostic models in IC are statistical parametric models based on logistic regression. Given a description of a patient at admission, these models predict the probability of his or her survival. Typically, this patient description relies on an aggregate variable, called a score, that quantifies the severity of illness of the patient. The use of a parametric model and an aggregate score form adequate means to develop models when data is relatively scarce but it introduces the risk of bias. This paper motivates and suggests a method for studying and improving the performance behavior of current state-of-the-art IC prognostic models. Our method is based on machine learning and statistical ideas and relies on exploiting information that underlies a score variable. In particular, this underlying information is used to construct a classification tree whose nodes denote patient sub-populations. For these sub-populations, local models, most notably logistic regression ones, are developed using only the total score variable. We compare the performance of this hybrid model to that of a traditional global logistic regression model. We show that the hybrid model not only provides more insight into the data but also has a better performance. We pay special attention to the precision aspect of model performance and argue why precision is more important than discrimination ability.