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

Personalized Modeling for Prediction with Decision-Path Models

Shyam Visweswaran  1 , Antonio Ferreira  2 , Guilherme A Ribeiro  3 , Alexandre C Oliveira  3 , Gregory F Cooper  1
Affiliations

Personalized Modeling for Prediction with Decision-Path Models

Shyam Visweswaran et al. PLoS One. .

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. An example population decision tree and a personalized decision path.
Panel (a) gives the names of the 21 variables and panel (b) gives their values for a test (current) patient whose outcome we want to predict. Panel (c) shows a population decision tree (derived by CART) and the path used for performing inference, and panel (d) shows a personalized decision path (derived by the DP-BAY method that is described later) for the patient in (b).
Fig 2
Fig 2. Pseudocode for the DP-BAY method.
Fig 3
Fig 3. Pseudocode for the DP-AUC method.
See this image and copyright information in PMC

References

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