A framework to assess causal effects in observational data through deep digital phenotyping and creating digital twins
In a real-world setting, we have been limited in our ability to infer whether a particular lifestyle or a treatment received by an individual leads to a good or a bad health outcome, despite systems to measure both these lifestyle features and treatments, and the outcomes. A major limitation is that inferring such a cause-and-effect association requires that at least identical individuals exist, which differ only on the lifestyle or treatment in question. The ability to define individuals that resemble each other has, however, been limited by our ability to actually leverage only crude characteristics even though electronic health records as well as electrocardiographic and imaging data may capture some of these distinct patient features.
The current proposal investigates a novel strategy to create an efficient way to define each individual using their data captured in all these high-quality data sources. This virtual representation of each person (referred to as their "digital twin") will be used to identify other individuals who resemble this person on a set of measurable characteristics.
Our work will evaluate the least amount of unique data sources that are required to define digital twins. Pairs of digital twins will be followed over time for the development of adverse cardiovascular events while focusing on identifying lifestyle or treatment differences that may underlie differences in the trajectory of outcomes among such digital twins.
Collectively, our investigations will leverage the uniquely powerful contributions of UK Biobank participants to make methodologic advances that allow us to gain deeper insights from observational studies, potentially expanding their role in scientific discovery.