Targeted learning via probabilistic subpopulation matching

Music City Center 

To get more accurate prediction results from a target study, transfer knowledge from similar source studies is proved to be useful. However, in many real-world biomedical applications, populations in different studies, e.g., clinical sites, can be heterogeneous, causing challenges in properly borrowing information towards the target study. If using study-level matching to identify similar source studies, samples from source studies that significantly differ from the target study will all be dropped at the study level, which can lead to substantial information loss. We consider a general situation where all studies are sampled from a super-population composed of distinct subpopulations, and propose a novel framework of targeted learning via subpopulation matching. We first fit a finite mixture model jointly across all studies to get subject-wise probabilistic subpopulation information, and then transfer knowledge from source studies to the target study within each identified subpopulation. By measuring similarities between subpopulations, our method effectively decomposes between-study heterogeneity and allows knowledge transfer from all source studies without dropping any samples.

Finite mixture model

Generalized linear regression

Subpopulation structure

Transfer learning 

Biometrics Section