@inproceedings{sanders-etal-2020-extracting,
title = "Extracting Adherence Information from Electronic Health Records",
author = "Sanders, Jordan and
Gudala, Meghana and
Hamilton, Kathleen and
Prasad, Nishtha and
Stovall, Jordan and
Blanco, Eduardo and
Hamilton, Jane E and
Roberts, Kirk",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.60",
doi = "10.18653/v1/2020.coling-main.60",
pages = "680--695",
abstract = "Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.",
}
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<abstract>Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.</abstract>
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%0 Conference Proceedings
%T Extracting Adherence Information from Electronic Health Records
%A Sanders, Jordan
%A Gudala, Meghana
%A Hamilton, Kathleen
%A Prasad, Nishtha
%A Stovall, Jordan
%A Blanco, Eduardo
%A Hamilton, Jane E.
%A Roberts, Kirk
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F sanders-etal-2020-extracting
%X Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.
%R 10.18653/v1/2020.coling-main.60
%U https://aclanthology.org/2020.coling-main.60
%U https://doi.org/10.18653/v1/2020.coling-main.60
%P 680-695
Markdown (Informal)
[Extracting Adherence Information from Electronic Health Records](https://aclanthology.org/2020.coling-main.60) (Sanders et al., COLING 2020)
ACL
- Jordan Sanders, Meghana Gudala, Kathleen Hamilton, Nishtha Prasad, Jordan Stovall, Eduardo Blanco, Jane E Hamilton, and Kirk Roberts. 2020. Extracting Adherence Information from Electronic Health Records. In Proceedings of the 28th International Conference on Computational Linguistics, pages 680–695, Barcelona, Spain (Online). International Committee on Computational Linguistics.