Identifying informative risk factors and predicting bone disease progression via deep belief networks

摘要

Osteoporosis is a common disease which frequently causes death, permanent disability, and loss of quality of life in the geriatric population. Identifying risk factors for the disease progression and capturing the disease characteristics have received increasing attentions in the health informatics research. In data mining area, risk factors are features of the data and diagnostic results can be regarded as the labels to train a model for a regression or classification task. We develop a general framework based on the heterogeneous electronic health records (EHRs) for the risk factor (RF) analysis that can be used for informative RF selection and the prediction of osteoporosis. The RF selection is a task designed for ranking and explaining the semantics of informative RFs for preventing the disease and improving the understanding of the disease. Predicting the risk of osteoporosis in a prospective and population-based study is a task for monitoring the bone disease progression. We apply a variety of well-trained deep belief network (DBN) models which inherit the following good properties: (1) pinpointing the underlying causes of the disease in order to assess the risk of a patient in developing a target disease, and (2) discriminating between patients suffering from the disease and without the disease for the purpose of selecting RFs of the disease. A variety of DBN models can capture characteristics for different patient groups via a training procedure with the use of different samples. The case study shows that the proposed method can be efficiently used to select the informative RFs. Most of the selected RFs are validated by the medical literature and some new RFs will attract interests across the medical research. Moreover, the experimental analysis on a real bone disease data set shows that the proposed framework can successfully predict the progression of osteoporosis. The stable and promising performance on the evaluation metrics confirms the effectiveness of our model. (C) 2014 Elsevier Inc. All rights reserved.