Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources

JMIR Medical Informatics
Yingxiang HuangXiaoqian Jiang

Abstract

Data sharing has been a big challenge in biomedical informatics because of privacy concerns. Contextual embedding models have demonstrated a very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep-learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different, and naive pooling renders combined embeddings useless. The aim of this study was to present a novel approach to address these issues and to promote sharing representation without sharing data. Without sacrificing privacy, we also aimed to build a global model from representations learned from local private data and synchronize information from multiple sources. We propose a methodology that harmonizes different local contextual embeddings into a global model. We used Word2Vec to generate contextual embeddings from each source and Procrustes to fuse different vector models into one common space by using a list of corresponding pairs as anchor points. We performed prediction analysis with harmonized embeddings. We used sequential me...Continue Reading

References

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Citations

Oct 4, 2020·BMC Medical Informatics and Decision Making·Divneet MandairMichael A Rosenberg
Apr 4, 2021·Journal of Biomedical Informatics·Yingxiang HuangLucila Ohno-Machado

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Software Mentioned

Word2Vec
Procrustes
PDPS
Graph2Vec
III
Med2Vec
Metamap
GloVe
MIMIC

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