Title: Federated Tensor Factorization for Computational Phenotyping,
Abstract: Tensor factorization models offer an effective approach to convert massive
electronic health records into meaningful clinical concepts (phenotypes) for
data analysis. These models need a large amount of diverse samples to avoid
population bias. An open challenge is how to derive phenotypes jointly across
multiple hospitals, in which direct patient-level data sharing is not possible
(e.g., due to institutional policies). In this paper, we developed a novel
solution to enable federated tensor factorization for computational phenotyping
without sharing patient-level data. We developed secure data harmonization and
federated computation procedures based on alternating direction method of
multipliers (ADMM). Using this method, the multiple hospitals iteratively
update tensors and transfer secure summarized information to a central server,
and the server aggregates the information to generate phenotypes. We
demonstrated with real medical datasets that our method resembles the
centralized training model (based on combined datasets) in terms of accuracy
and phenotypes discovery while respecting privacy.