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Personalized modeling for prediction with decision-path models

Visweswaran, S and Ferreira, A and Ribeiro, GA and Oliveira, AC and Cooper, GF (2015) Personalized modeling for prediction with decision-path models. PLoS ONE, 10 (6).

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Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Visweswaran, Sshv3@pitt.eduSHV3
Ferreira, A
Ribeiro, GA
Oliveira, AC
Cooper, GF
ContributionContributors NameEmailPitt UsernameORCID
Date: 22 June 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS ONE
Volume: 10
Number: 6
DOI or Unique Handle: 10.1371/journal.pone.0131022
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
School of Medicine > Biomedical Informatics
Refereed: Yes
Date Deposited: 29 Jun 2016 15:41
Last Modified: 30 Mar 2021 10:55


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