Abstract
Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. Further, free-text medical notes may contain information not immediately available in structured variables. We propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction, respectively. We also explore the relationships between learned features from structured and unstructured variables using projection-weighted canonical correlation analysis. Finally, we outline a protocol to evaluate model usability in a clinical decision support context. From domain-expert evaluations, our model generates informative rationales that have promising real-life applications.- Anthology ID:
- 2020.emnlp-main.115
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1478–1489
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.115
- DOI:
- 10.18653/v1/2020.emnlp-main.115
- Cite (ACL):
- Jinyue Feng, Chantal Shaib, and Frank Rudzicz. 2020. Explainable Clinical Decision Support from Text. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1478–1489, Online. Association for Computational Linguistics.
- Cite (Informal):
- Explainable Clinical Decision Support from Text (Feng et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.emnlp-main.115.pdf