@inproceedings{xu-etal-2024-ram,
title = "{RAM}-{EHR}: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records",
author = "Xu, Ran and
Shi, Wenqi and
Yu, Yue and
Zhuang, Yuchen and
Jin, Bowen and
Wang, May Dongmei and
Ho, Joyce and
Yang, Carl",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-short.68",
pages = "754--765",
abstract = "We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain information related to medical concepts. This strategy addresses the difficulties associated with complex names for the concepts. RAM-EHR then augments the local EHR predictive model co-trained with consistency regularization to capture complementary information from patient visits and summarized knowledge. Experiments on two EHR datasets show the efficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4{\%} gain in AUROC and 7.2{\%} gain in AUPR), emphasizing the effectiveness of the summarized knowledge from RAM-EHR for clinical prediction tasks.",
}
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<abstract>We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain information related to medical concepts. This strategy addresses the difficulties associated with complex names for the concepts. RAM-EHR then augments the local EHR predictive model co-trained with consistency regularization to capture complementary information from patient visits and summarized knowledge. Experiments on two EHR datasets show the efficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4% gain in AUROC and 7.2% gain in AUPR), emphasizing the effectiveness of the summarized knowledge from RAM-EHR for clinical prediction tasks.</abstract>
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%0 Conference Proceedings
%T RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
%A Xu, Ran
%A Shi, Wenqi
%A Yu, Yue
%A Zhuang, Yuchen
%A Jin, Bowen
%A Wang, May Dongmei
%A Ho, Joyce
%A Yang, Carl
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F xu-etal-2024-ram
%X We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain information related to medical concepts. This strategy addresses the difficulties associated with complex names for the concepts. RAM-EHR then augments the local EHR predictive model co-trained with consistency regularization to capture complementary information from patient visits and summarized knowledge. Experiments on two EHR datasets show the efficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4% gain in AUROC and 7.2% gain in AUPR), emphasizing the effectiveness of the summarized knowledge from RAM-EHR for clinical prediction tasks.
%U https://aclanthology.org/2024.acl-short.68
%P 754-765
Markdown (Informal)
[RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records](https://aclanthology.org/2024.acl-short.68) (Xu et al., ACL 2024)
ACL
- Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Bowen Jin, May Dongmei Wang, Joyce Ho, and Carl Yang. 2024. RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 754–765, Bangkok, Thailand. Association for Computational Linguistics.