  Population attributes are essential in health for understanding who the data
represents and precision medicine efforts. Even within disease infection
labels, patients can exhibit significant variability; "fever" may mean
something different when reported in a doctor's office versus from an online
app, precluding directly learning across different datasets for the same
prediction task. This problem falls into the domain adaptation paradigm.
However, research in this area has to-date not considered who generates the
data; symptoms reported by a woman versus a man, for example, could also have
different implications. We propose a novel population-aware domain adaptation
approach by formulating the domain adaptation task as a multi-source
hierarchical Bayesian framework. The model improves prediction in the case of
largely unlabelled target data by harnessing both domain and population
invariant information.
