Personalized Modeling for Prediction with Decision-Path Models

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 tak...

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Bibliographic Details
Published in:PloS one 2015-06, Vol.10 (6), p.e0131022
Main Authors: Visweswaran, Shyam, Ferreira, Antonio, Ribeiro, Guilherme A, Oliveira, Alexandre C, Cooper, Gregory F
Format: Article
Language:English
Subjects:
Area Under Curve
Classification
Clinical outcomes
Decision making
Decision Trees
Humans
Informatics
Mathematical models
Modelling
Models, Theoretical
Patients
Performance evaluation
Performance prediction
Pneumonia
Population
Prediction models
Probability distribution
Regression analysis
ROC Curve
Sepsis
Statistical methods
Variables
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0131022