论文标题
PharmMT：一种神经机器翻译方法，以简化处方方向
PharmMT: A Neural Machine Translation Approach to Simplify Prescription Directions
论文作者
论文摘要
医生和卫生专业人员在处方方向上使用的语言包括医疗术语和隐性指令，并在患者中引起了很多混乱。人力干预以简化药房的语言可能会引入其他错误，这可能导致潜在的严重健康结果。我们建议一种基于机器翻译的新型方法PharmMT，可以自动可靠地将处方方向简化为患者友好的语言，从而大大减少药剂师的工作量。我们在数据集上评估了提出的方法，该数据集由大型邮购药房获得的530k处方组成。端到端系统在药剂师生成的参考方向上达到60.27的BLEU得分，基于规则的归一化的相对改善39.6％。药剂师认为简化方向的94.3％是可用的AS或最小变化。这项工作证明了基于机器翻译的工具的可行性，用于简化现实生活中的处方方向。
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.
