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PLoS One. 2015 Jun 22;10(6):e0131022. doi: 10.1371/journal.pone.0131022. eCollection 2015.

Personalized Modeling for Prediction with Decision-Path Models.

Author information

1
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America; The Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
2
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
3
Department of Informatics, Federal University of Maranhão-UFMA, Sao Luis, MA, Brazil.

Abstract

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.

PMID:
26098570
PMCID:
PMC4476684
DOI:
10.1371/journal.pone.0131022
[Indexed for MEDLINE]
Free PMC Article

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