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Personalized Modeling for Prediction with Decision-Path Models.

Visweswaran S, Ferreira A, Ribeiro GA, Oliveira AC, Cooper GF - PLoS ONE (2015)

Bottom Line: Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals.We introduce three personalized methods that derive personalized decision-path models.Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART.

View Article: PubMed Central - PubMed

Affiliation: 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.

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.

No MeSH data available.


Related in: MedlinePlus

Pseudocode for the DP-BAY method.
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pone.0131022.g002: Pseudocode for the DP-BAY method.

Mentions: The pseudocode for DP-BAY is given in Fig 2. The DP-BAY method uses greedy forward-stepping search to identify features to add to S. Given a training dataset and a test person for whom we want to estimate the distribution of T, DP-BAY begins with S that has no features. It successively adds features to S by identifying at each step the best feature to add using a Bayesian score.


Personalized Modeling for Prediction with Decision-Path Models.

Visweswaran S, Ferreira A, Ribeiro GA, Oliveira AC, Cooper GF - PLoS ONE (2015)

Pseudocode for the DP-BAY method.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4476684&req=5

pone.0131022.g002: Pseudocode for the DP-BAY method.
Mentions: The pseudocode for DP-BAY is given in Fig 2. The DP-BAY method uses greedy forward-stepping search to identify features to add to S. Given a training dataset and a test person for whom we want to estimate the distribution of T, DP-BAY begins with S that has no features. It successively adds features to S by identifying at each step the best feature to add using a Bayesian score.

Bottom Line: Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals.We introduce three personalized methods that derive personalized decision-path models.Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART.

View Article: PubMed Central - PubMed

Affiliation: 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.

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.

No MeSH data available.


Related in: MedlinePlus