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

Shardlow, Matthew ORCID logoORCID: https://orcid.org/0000-0003-1129-2750 and Nawaz, Raheel ORCID logoORCID: https://orcid.org/0000-0001-9588-0052 (2019) Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table. In: 57th Annual Meeting of the Association for Computational Linguistics, 29 July 2019 - 31 July 2019, Florence, Italy.

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Abstract

Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical let- ters for patients. We take existing neural text simplification software and augment it with a new phrase table that links complex medi- cal terminology to simpler vocabulary by min- ing SNOMED-CT. In an evaluation task us- ing crowdsourcing, we show that the results of our new system are ranked easier to under- stand (average rank 1.93) than using the origi- nal system (2.34) without our phrase table. We also show improvement against baselines in- cluding the original text (2.79) and using the phrase table without the neural text simplifica- tion software (2.94). Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to pro- vide effective neural text simplification for any domain without the need for costly annotation.

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