The healthcare domain has pioneered the rich capture of information in images, video, and text. It is also offers an exceptionally good example that illustrates how large bodies of information can be used to help improve our everyday lives. Advances in medical science arrive quickly and modern medical data acquisition devices produce increasingly more images (e.g., due to CT or MRI scans) and new discoveries involving omics data (e.g., attributed to genomics or proteomics) produce more data than medical doctors can analyze or health care organizations can preserve in their storage systems. The increasingly important role of algorithmic diagnosis and treatment creates the perfect opportunity to integrate images and omics data with prescriptive analytics and would allow new medical decision-support systems, real-time patient monitoring systems, more accurate diagnosis tools, and enable individualized treatments.  Key to these innovations will be the discovery of associations and a better understanding of patterns within the very large collections of medical data. It has the potential to improve healthcare, save lives, and even lower costs for health care providers. In this project, we will develop new strategies and algorithms to make this vision become reality. Our aim is to design new integrated analysis methods for existing big data sets and the upcoming datanami in healthcare, which will enable the detection of diseases at very early stages when they can be treated more easily and effectively.