A semi-supervised learning approach to integrated salient risk features for bone diseases

The study of the risk factor analysis and prediction for diseases requires the understanding of the complicated and highly correlated relationships behind numerous potential risk factors (RFs). The existing models for this purpose usuallyx a small number of RFs based on the expert knowledge. Although handcrafted RFs are usually statistically significant, those abandoned RFs might still contain valuable information for explaining the comprehensiveness of a disease. However, it is impossible to simply keep all of RFs. So how to find the integrated risk features from numerous potential RFs becomes a particular challenging task. Another major challenge for this task is the lack of sufficient labeled data and missing values in the training data. In this paper, we focus on the identification of the relationships between a bone disease and its potential risk factors by learning a deep graphical model in an epidemiologic study for the purpose of predicting osteoporosis and bone loss. An effective risk factor analysis approach which delineates both observed and hidden risk factors behind a disease encapsulates the salient features and also provides a framework for two prediction tasks. Specifically, we first investigate an approach to show the salience of the integrated risk features yielding more abstract and useful representations for the prediction. Then we formulate the whole prediction problem as two separate tasks to evaluate our new representation of integrated features. With the success of the osteoporosis prediction, we further take advantage of the Positive output and predict the progression trend of osteoporosis severity.