Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table

Matthew Shardlow, Raheel Nawaz


Abstract
Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients. We take existing neural text simplification software and augment it with a new phrase table that links complex medical terminology to simpler vocabulary by mining SNOMED-CT. In an evaluation task using crowdsourcing, we show that the results of our new system are ranked easier to understand (average rank 1.93) than using the original system (2.34) without our phrase table. We also show improvement against baselines including the original text (2.79) and using the phrase table without the neural text simplification software (2.94). Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to provide effective neural text simplification for any domain without the need for costly annotation.
Anthology ID:
P19-1037
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
380–389
Language:
URL:
https://aclanthology.org/P19-1037
DOI:
10.18653/v1/P19-1037
Bibkey:
Cite (ACL):
Matthew Shardlow and Raheel Nawaz. 2019. Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 380–389, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table (Shardlow & Nawaz, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1037.pdf
Supplementary:
 P19-1037.Supplementary.zip
Code
 MMU-TDMLab/ClinicalNTS