TY - GEN
T1 - Extracting Adherence Information from Electronic Health Records
AU - Sanders, Jordan
AU - Gudala, Meghana
AU - Hamilton, Kathleen
AU - Prasad, Nishtha
AU - Stovall, Jordan
AU - Blanco, Eduardo
AU - Hamilton, Jane E.
AU - Roberts, Kirk
N1 - Funding Information:
Research reported in this article was partially funded through a Patient-Centered Outcomes Research Institute©R (PCORI©R) Award (PCORI/ME-2018C1-10963). The views, statements and opinions presented in this article are solely the responsibility of the author and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute©R (PCORI©R ), its Board of Governors or Methodology Committee.
Publisher Copyright:
© 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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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M3 - Conference contribution
AN - SCOPUS:85143188209
T3 - COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference
SP - 680
EP - 695
BT - COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference
A2 - Scott, Donia
A2 - Bel, Nuria
A2 - Zong, Chengqing
PB - Association for Computational Linguistics (ACL)
T2 - 28th International Conference on Computational Linguistics, COLING 2020
Y2 - 8 December 2020 through 13 December 2020
ER -