Deep learning has yielded immense success on many different scenarios. With the success in other real world application, it has been applied into big medical data. However, discovering knowledge from these data can be very challenging because they normally contain large amount of unstructured data, they may have lots of missing values, and they can be highly complex and heterogeneous. In these cases the deep neural network itself is not applicable enough. To solve these problems we propose a Collaborative Filtering-Enhanced Deep Learning Approach. In particular, first we estimate missing values based on the information mining from the structured and unstructured data. Secondly, a deep neural network-based method is applied, which can help us handle complex and multi-modality data. The proposed algorithm is applied to analyze big medical data and make personalized health risk prediction. Extensive experiments on real-world datasets show improvements of our proposed algorithm over the state-of-the-art methods.