Med2Meta: Learning Representations of Medical Concepts with Meta-embeddings

Shaika Chowdhury, Chenwei Zhang, Philip S. Yu, Yuan Luo

2020

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.

Paper Citation

in Harvard Style

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) - Volume 5: HEALTHINF; ISBN 978-989-758-398-8, SciTePress, pages 369-376. DOI: 10.5220/0008934403690376

in Bibtex Style

@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) - Volume 5: HEALTHINF},
year={2020},
pages={369-376},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008934403690376},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF
TI - Med2Meta: Learning Representations of Medical Concepts with Meta-embeddings
SN - 978-989-758-398-8
AU - Chowdhury S.
AU - Zhang C.
AU - Yu P.
AU - Luo Y.
PY - 2020
SP - 369
EP - 376
DO - 10.5220/0008934403690376
PB - SciTePress