Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .
Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)
Multi-dimensional Bayesian network classifiers ( MBCs ) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems , where each instance in the data set has to be assigned to more than one class variable . In this paper , we propose a Markov blanket-based approach for learning MBCs from data . Basically , it consists of determining the Markov blanket around each class variable using the HITON algorithm , then specifying the directionality over the MBC subgraphs . Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions ( EQ-5D ) from the 39-item Parkinson 's Disease Question naire ( PDQ-39 ) in order to estimate the health-related quality  of  life of Parkinson 's patients . Fivefold cross-validation experiments were carried out on r and omly generated synthetic data sets , Yeast data set , as well as on a real-world Parkinson 's disease data set containing 488 patients . The experimental study , including comparison with additional Bayesian network-based approaches , back propagation for multi-label learning , multi-label k-nearest neighbor , multinomial logistic regression , ordinary least squares , and censored least absolute deviations , shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables
Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .