Predicting the Eq-5d From the Parkinson's Disease Questionnaire PDQ-8 Using Multi-Dimensional Bayesian Network Classifiers

Abstract

The impact of the Parkinson's disease and its treatment on the patients' health-related quality of life can be estimated either by means of generic measures such as the european quality of Life-5 Dimensions (EQ-5D) or speci¯c measures such as the 8-item Parkinson's disease questionnaire . In clinical studies, PDQ-8 could be used in detriment of EQ-5D due to the lack of resources, time or clinical interest in generic measures. Nevertheless, PDQ-8 cannot be applied in cost-e®ectiveness analyses which require generic measures and quantitative utility scores, such as EQ-5D. To deal with this problem, a commonly used solution is the prediction of EQ-5D from PDQ-8. In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classi¯ers. Our approach is evaluated using ¯ve-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches, two commonly used mapping methods namely, ordinary least squares and censored least absolute deviations, and a deterministic model. Experimental results are promising in terms of predictive performance as well as the identi¯cation of dependence relationships among EQ-5D and PDQ-8 items that the mapping approaches are unable to detect.

Multi-dimensional classification via stacked dependency exploitation

“…Firstly, q 2 first-level classifiers are trained (steps 1-6). Then, training sets which will be used to train the second-level classifiers are constructed (steps [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. After that, the second-level classifier for each class space is induced one by one (steps [23][24][25].…”

“…Here, each class variable corresponds to one specific class space which characterizes the object's semantics from one dimension. Multi-dimensional classification (MDC) techniques have been widely utilized in real-world applications involving objects with rich semantics [4][5][6][7][8][9][10][11][12].…”

Multi-dimensional classification: paradigm, algorithms and beyond

“…), and from the scenario dimension (with possible classes wedding, memorial, saloon, etc.). Such kinds of applications widely exist in computer vision [3][4][5][6][7][8][9], text mining [10][11][12][13][14], bioinformatics [15][16][17][18][19][20][21], ecology [22,23], and beyond [24][25][26][27][28][29][30][31][32][33].…”