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Authors: Shaika Chowdhury 1 ; Chenwei Zhang 2 ; Philip S. Yu 1 and Yuan Luo 3

Affiliations: 1 Department of Computer Science, University of Illinois at Chicago, Chicago, Illinois, U.S.A. ; 2 Amazon, Seattle, Washington, U.S.A. ; 3 Department of Preventive Medicine, Northwestern University, Chicago, Illinois, U.S.A.

Keyword(s): Representation Learning, Electronic Health Records, Meta-embeddings, Graph Neural Networks.

Abstract: Distributed representations of medical concepts have been used to support downstream clinical tasks recently. Electronic Health Records (EHR) capture different aspects of patients’ hospital encounters and serve as a rich source for augmenting clinical decision making by learning robust medical concept embeddings. However, the same medical concept can be recorded in different modalities (e.g., clinical notes, lab results) — with each capturing salient information unique to that modality — and a holistic representation calls for relevant feature ensemble from all information sources. We hypothesize that representations learned from heterogeneous data types would lead to performance enhancement on various clinical informatics and predictive modeling tasks. To this end, our proposed approach makes use of meta-embeddings, embeddings aggregated from learned embeddings. Firstly, modality-specific embeddings for each medical concept is learned with graph autoencoders. The ensemble of all the embeddings is then modeled as a meta-embedding learning problem to incorporate their correlating and complementary information through a joint reconstruction. Empirical results of our model on both quantitative and qualitative clinical evaluations have shown improvements over state-of-the-art embedding models, thus validating our hypothesis. (More)

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Paper citation in several formats:
Chowdhury, S.; Zhang, C.; Yu, P. and Luo, Y. (2020). Med2Meta: Learning Representations of Medical Concepts with Meta-embeddings. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 369-376. DOI: 10.5220/0008934403690376

@conference{healthinf20,
author={Shaika Chowdhury. and Chenwei Zhang. and Philip S. Yu. and Yuan Luo.},
title={Med2Meta: Learning Representations of Medical Concepts with Meta-embeddings},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF},
year={2020},
pages={369-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008934403690376},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - HEALTHINF
TI - Med2Meta: Learning Representations of Medical Concepts with Meta-embeddings
SN - 978-989-758-398-8
IS - 2184-4305
AU - Chowdhury, S.
AU - Zhang, C.
AU - Yu, P.
AU - Luo, Y.
PY - 2020
SP - 369
EP - 376
DO - 10.5220/0008934403690376
PB - SciTePress