What patient information allows us to make accurate predictions of outcome?
Lovell, D. R., Dance, C. R., Niranjan, M., Prager, R. W., & Dalton, K. J. (1997) What patient information allows us to make accurate predictions of outcome? In Proceedings of the 18th Annual International Conference of IEEE Engineering-in-Medicine-amd-Biology-Society, IEEE, Amsterdam, The Netherlands, pp. 2020-2021.
Only some of the information contained in a medical record will be useful to the prediction of patient outcome. We describe a novel method for selecting those outcome predictors which allow us to reliably discriminate between adverse and benign end results. Using the area under the receiver operating characteristic as a nonparametric measure of discrimination, we show how to calculate the maximum discrimination attainable with a given set of discrete valued features. This upper limit forms the basis of our feature selection algorithm. We use the algorithm to select features (from maternity records) relevant to the prediction of failure to progress in labour. The results of this analysis motivate investigation of those predictors of failure to progress relevant to parous and nulliparous sub-populations.
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|Item Type:||Conference Paper|
|Keywords:||Algorithms, Data structures, Failure analysis, Health risks, Patient treatment, Pattern recognition, Feature selection, Pregnancy, Risk prediction, Medical computing|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||07 Jan 2015 05:26|
|Last Modified:||07 Jan 2015 05:26|
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