Extracting Adherence Information from Electronic Health Records
Jordan Sanders, Meghana Gudala, Kathleen Hamilton, Nishtha Prasad, Jordan Stovall, Eduardo Blanco, Jane E Hamilton, Kirk Roberts
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.
BibTeX
@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"
}