Experimental Analysis on Severe Head Injury Outcome Prediction – A Preliminary Study
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Date
2006-09-12T01:09:44Z
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Abstract
Severe head injury management is a very costly and labor-intensive process. There has been growing interest in building outcome analysis models using existing patient records to facilitate decision making and resource planning. However, traditional methods and results in the literature are often inconsistent in variable discretization, accuracy evaluation and class label assignment. In this paper, we examined the effectiveness of applying different outcome analysis methods in head injury management in a uniform manner, based on a set of actual patient records. We have conducted a set of experiments using sound statistical techniques to derive the results. Besides the comparative analysis that highlight the strengths and limitations of different outcome analysis methods, the experiments also show that Minimal-Description-Length (MDL)-based discretization method can help improve prediction accuracy substantially, and that class label assignments in the classification techniques play a very important role on prediction accuracy.