Synergistic Data Integration

One of the most powerful aspects of Rejuve’s AI approach is its ability to effectively integrate patterns found in multiple types of relevant data. The volume of data and computational power needed to solve complex health problems like aging has only recently been made available to research scientists. Much of AI’s impressive recent gains still exists mostly in silos, like healthcare claims, and biomarker, genomic, and imaging studies. Despite this progress, these successful siloed applications only weakly relate to the general issue of aging; and when analyzing a complex system like the human body, siloed artificial intelligence is not enough. 

Rejuve seeks to improve this situation by combining these siloed views of the human body to more clearly understand the underlying complex system. Rejuve’s analysis tools fuse multiple data channels that will facilitate the interpretation of sparsely overlapping observational data. For example, when a dataset contains patients with measurable variables A and B, as well as patients with measurable variables B and C, Rejuve’s AI can interpret variables ‘A’ and ‘C’ by using the overlapping variable ‘B’. Rejuve’s AI combines specific information and insights to develop a greater high-level understanding of the human body. To be able to combine these silos, Rejuve has to offer a platform that allows for deeply synergistic data integration.