Extracting meaningful knowledge & actionable comprehensions from complex, multi-dimensional, and heterogeneous biomedical data residues a significant challenge in healthcare. Modern health care systems generate various types of medical data that are often intricate, diverse, and typically unstructured. These large datasets are often difficult to interpret and process. Traditionally, data mining techniques have been employed to extract features from such data, with prediction or clustering models built on top of those features. However, this approach faces numerous challenges, particularly when dealing with complex data and limited domain expertise. Recent advancements in deep learning, however, have introduced new and effective methods for building learning models from these complex datasets. In this paper, we discuss various clinical data types and their relevant features that can serve as inputs to deep learning networks, contributing to the creation of a more reliable and sustainable healthcare system.