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.

View at publisher


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.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 79894
Item Type: Conference Paper
Refereed: No
Keywords: Algorithms, Data structures, Failure analysis, Health risks, Patient treatment, Pattern recognition, Feature selection, Pregnancy, Risk prediction, Medical computing
ISBN: 05891019 (ISSN)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: IEEE
Deposited On: 07 Jan 2015 05:26
Last Modified: 07 Jan 2015 05:26

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page