PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions

Jiazhao Li, Corey Lester, Xinyan Zhao, Yuting Ding, Yun Jiang, V.G.Vinod Vydiswaran


Abstract
The language used by physicians and health professionals in prescription directions includes medical jargon and implicit directives and causes much confusion among patients. Human intervention to simplify the language at the pharmacies may introduce additional errors that can lead to potentially severe health outcomes. We propose a novel machine translation-based approach, PharmMT, to automatically and reliably simplify prescription directions into patient-friendly language, thereby significantly reducing pharmacist workload. We evaluate the proposed approach over a dataset consisting of over 530K prescriptions obtained from a large mail-order pharmacy. The end-to-end system achieves a BLEU score of 60.27 against the reference directions generated by pharmacists, a 39.6% relative improvement over the rule-based normalization. Pharmacists judged 94.3% of the simplified directions as usable as-is or with minimal changes. This work demonstrates the feasibility of a machine translation-based tool for simplifying prescription directions in real-life.
Anthology ID:
2020.findings-emnlp.251
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2785–2796
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.251
DOI:
10.18653/v1/2020.findings-emnlp.251
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.251.pdf
Video:
 https://slideslive.com/38940180