@inproceedings{wang-etal-2023-hierarchical,
title = "Hierarchical Pretraining on Multimodal Electronic Health Records",
author = "Wang, Xiaochen and
Luo, Junyu and
Wang, Jiaqi and
Yin, Ziyi and
Cui, Suhan and
Zhong, Yuan and
Wang, Yaqing and
Ma, Fenglong",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.171",
doi = "10.18653/v1/2023.emnlp-main.171",
pages = "2839--2852",
abstract = "Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model. To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MedHMP, specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MedHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.",
}
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<abstract>Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model. To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MedHMP, specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MedHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.</abstract>
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%0 Conference Proceedings
%T Hierarchical Pretraining on Multimodal Electronic Health Records
%A Wang, Xiaochen
%A Luo, Junyu
%A Wang, Jiaqi
%A Yin, Ziyi
%A Cui, Suhan
%A Zhong, Yuan
%A Wang, Yaqing
%A Ma, Fenglong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-hierarchical
%X Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model. To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MedHMP, specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MedHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.
%R 10.18653/v1/2023.emnlp-main.171
%U https://aclanthology.org/2023.emnlp-main.171
%U https://doi.org/10.18653/v1/2023.emnlp-main.171
%P 2839-2852
Markdown (Informal)
[Hierarchical Pretraining on Multimodal Electronic Health Records](https://aclanthology.org/2023.emnlp-main.171) (Wang et al., EMNLP 2023)
ACL
- Xiaochen Wang, Junyu Luo, Jiaqi Wang, Ziyi Yin, Suhan Cui, Yuan Zhong, Yaqing Wang, and Fenglong Ma. 2023. Hierarchical Pretraining on Multimodal Electronic Health Records. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2839–2852, Singapore. Association for Computational Linguistics.