Experimental Analysis on Severe Head Injury Outcome Prediction – A Preliminary Study

dc.contributor.authorYIN, Honglien_US
dc.contributor.authorLI, Guoliangen_US
dc.contributor.authorLEONG, Tze-Yunen_US
dc.contributor.authorKURALMANI, Vellaisamyen_US
dc.contributor.authorPANG, Boon Chuanen_US
dc.contributor.authorANG, Beng Tien_US
dc.contributor.authorLEE, Kah Keowen_US
dc.contributor.authorNG, Ivanen_US
dc.date.accessioned2006-09-12T01:09:44Zen_US
dc.date.accessioned2017-01-23T06:59:57Z
dc.date.available2006-09-12T01:09:44Zen_US
dc.date.available2017-01-23T06:59:57Z
dc.date.issued2006-09-12T01:09:44Zen_US
dc.description.abstractSevere 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.en_US
dc.format.extent474662 bytesen_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.urihttps://dl.comp.nus.edu.sg/xmlui/handle/1900.100/2250en_US
dc.language.isoenen_US
dc.relation.ispartofseriesTRD9/06en_US
dc.titleExperimental Analysis on Severe Head Injury Outcome Prediction – A Preliminary Studyen_US
dc.typeTechnical Reporten_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
TRD9-06.pdf
Size:
463.54 KB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.53 KB
Format:
Plain Text
Description: