Risk factor analysis based on deep learning models

Accurate rendering of diagnosis and prognosis for a disease with respect to a patient requires analysis of complicated, diverse, yet correlated risk factors (RFs). Most of the existing methods for this purpose are based on handcraft RFs by calculating their statistical significance to the disease. However, such methods not only incur intensive labor but also lack capability to discover or infer previously unknown complex relationships and combined effects among correlated RFs. Nowadays, deep learning models have emerged as a hot topic, due to its ability to automatically extract useful and complex features from raw data. In this paper, we explore the effectiveness of deep learning on medical data by building a deep learning based framework to analyze risk factors and study its prediction performance in disease diagnosis. Specifically, we investigate the application of deep learning with a special focus on interpreting the latent features extracted or created from raw data by the model. Experimental results demonstrate that deep learning based methods are able to aggregate features sharing same characteristics, and reduce effects from unimportant and uncorrelated RFs. The abstract features obtained by deep learning methods can represent the essentials of raw inputs, and give a good prediction performance in disease diagnosis.