Papers Published
Abstract:
Early detection and treatment of psychiatric disorders on children has shown significant impact in their subsequent development and quality of life. The assessment of psychopathology in childhood is commonly carried out by performing long comprehensive interviews such as the widely used Preschool Age Psychiatric Assessment (PAPA). Unfortunately, the time required to complete a full interview is too long to apply it at the scale of the actual population at risk, and most of the population goes undiagnosed or is diagnosed significantly later than desired. In this work, we aim to learn from unique and very rich previously collected PAPA examples the inter-correlations between different questions in order to provide a reliable risk analysis in the form of a much shorter interview. This helps to put such important risk analysis at the hands of regular practitioners, including teachers and family doctors. We use for this purpose the alternating decision trees algorithm, which combines decision trees with boosting to produce small and interpretable decision rules. Rather than a binary prediction, the algorithm provides a measure of confidence in the classification outcome. This is highly desirable from a clinical perspective, where it is preferable to abstain a decision on the low-confidence cases and recommend further screening. In order to prevent over-fitting, we propose to use network inference analysis to predefine a set of candidate question with consistent high correlation with the diagnosis. We report encouraging results with high levels of prediction using two independently collected datasets. The length and accuracy of the developed method suggests that it could be a valuable tool for preliminary evaluation in everyday care. © 2014 IEEE.