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.- Anthology ID:
- 2020.coling-main.60
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 680–695
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.60
- DOI:
- 10.18653/v1/2020.coling-main.60
- Cite (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.
- Cite (Informal):
- Extracting Adherence Information from Electronic Health Records (Sanders et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.60.pdf