How to leverage the multimodal EHR data for better medical prediction?

Bo Yang, Lijun Wu


Abstract
Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of service and reduce costs. However, the complexity of electronic health records (EHR) data is a challenge for the application of deep learning. Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature and unstructured data like free text and laboratory measurements. Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration. Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear. Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction performance. The results on two prediction tasks show that our fused model with different data outperforms the state-of-the-art method without clinical notes, which illustrates the importance of our fusion method and the clinical note features.
Anthology ID:
2021.emnlp-main.329
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4029–4038
Language:
URL:
https://aclanthology.org/2021.emnlp-main.329
DOI:
10.18653/v1/2021.emnlp-main.329
Bibkey:
Cite (ACL):
Bo Yang and Lijun Wu. 2021. How to leverage the multimodal EHR data for better medical prediction?. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4029–4038, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
How to leverage the multimodal EHR data for better medical prediction? (Yang & Wu, EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.329.pdf
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 https://aclanthology.org/2021.emnlp-main.329.mp4