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Lookup NU author(s): Dr Dexter CanoyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2022 IEEE. Electronic health records (EHR) represent a holistic overview of patients' trajectories. Their increasing availability has fueled new hopes to leverage them and develop accurate risk prediction models for a wide range of diseases. Given the complex interrelationships of medical records and patient outcomes, deep learning models have shown clear merits in achieving this goal. However, a key limitation of current study remains their capacity in processing long sequences, and long sequence modelling and its application in the context of healthcare and EHR remains unexplored. Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records collected for decades and from multiple resources can inevitably exceed the receptive field of the most existing deep learning architectures. This can result in missing crucial, long-term dependencies. To address this gap, we present Hi-BEHRT, a hierarchical Transformer-based model that can significantly expand the receptive field of Transformers and extract associations from much longer sequences. Using a multimodal large-scale linked longitudinal EHR, the Hi-BEHRT exceeds the state-of-the-art deep learning models 1% to 5% for area under the receiver operating characteristic (AUROC) curve and 1% to 8% for area under the precision recall (AUPRC) curve on average, and 2% to 8% (AUROC) and 2% to 11% (AUPRC) for patients with long medical history for 5-year heart failure, diabetes, chronic kidney disease, and stroke risk prediction. Additionally, because pretraining for hierarchical Transformer is not well-established, we provide an effective end-to-end contrastive pre-training strategy for Hi-BEHRT using EHR, improving its transferability on predicting clinical events with relatively small training dataset.
Author(s): Li Y, Mamouei M, Salimi-Khorshidi G, Rao S, Hassaine A, Canoy D, Lukasiewicz T, Rahimi K
Publication type: Article
Publication status: Published
Journal: IEEE Journal of Biomedical and Health Informatics
Year: 2023
Volume: 27
Issue: 2
Pages: 1106-1117
Print publication date: 01/02/2023
Online publication date: 25/11/2022
Acceptance date: 20/11/2022
Date deposited: 01/03/2024
ISSN (print): 2168-2194
ISSN (electronic): 2168-2208
Publisher: IEEE
URL: https://doi.org/10.1109/JBHI.2022.3224727
DOI: 10.1109/JBHI.2022.3224727
PubMed id: 36427286
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