Professional to Plain Language Neural Translation: A Path Toward Actionable Health Information

健康素养是做出见多识广、改善结果的健康决策的关键。然而，当同龄人- 回顾的临床文献包含有价值的信息来指导健康决策，它通常是为 医疗保健专业人员的受众。即使在良好的一般文化背景下，医学术语和 专业语言的复杂结构使这些信息特别难以解释。虽然努力 已经用通俗易懂的语言总结了其中的一些文献，以便一般人都能读懂 公众，这些努力依赖于人类的专业知识。这种方法不能进行扩展以匹配快速的 文献中出现了新的发现。因此，对自动化方法的迫切需求尚未得到满足 规范的生物医学文献对公众的可及性。这个问题可以被框定为 医疗保健专业人员语言与医疗保健语言之间的翻译问题类型 消费者。这项拟议的研究建立在源于神经序列的深度学习的最新进展基础上。 TO-Sequence模型，最初在机器翻译任务中评估。在我们最近的工作中，我们 显示了这些模型可以有效地适应Cochrane摘要之间的翻译任务 系统评价数据库(CDSR)和相应的专业编写的通俗语言 摘要。由此产生的自动生成的摘要在以下方面优于其他模型 与专业人士撰写的摘要保持一致。此外，在一项试点用户评估中，参与者 在摘要来源方面被蒙在鼓里，他们通常被认为是专家撰写的 对口单位。在拟议的研究中，我们将进一步发展这一研究路线，通过评估 增加预训和辅助微调任务，以提高生成摘要的质量。 我们还将定制相关的模型，以提高其事实的准确性和可读性使用新颖 辅助培训目标和后处理程序。我们将评估我们的方法，将其与 以系统为中心的内容一致性评估中稳健的基准模型，包括参考摘要、可读性 和事实的正确性。使用机械土耳其人，我们将进行以用户为中心的易用性评估 与CDSR专家编写的普通模型相比，可以理解来自最佳性能模型的摘要 语言摘要。这些评估将同时考虑感知的可解释性和实际的理解性， 后者用一组选择题来考察理解、回忆和学习。在……里面 这样做，拟议的研究将推进自动化简化和摘要的最新水平 供公众阅读的生物医学文献。

Health literacy is key to making well-informed health decisions that improve outcomes. However, while the peer- reviewed clinical literature contains valuable information to guide health decisions, it is generally written for an audience of healthcare professionals. Even in the context of good general literacy, medical jargon and the complex structure of professional language make this information especially hard to interpret. While efforts have been made to summarize some of this literature in plain language to make it accessible to the general public, these efforts depend on human expertise. This approach cannot scale to match the rapid pace at which new findings emerge in the literature. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the canonical biomedical literature to the general public. This problem can be framed as a type of translation problem, between the language of healthcare professionals, and that of healthcare consumers. The proposed research builds on recent advances in deep learning stemming from neural sequence- to-sequence models, which were originally evaluated in machine translation tasks. In our recent work, we showed these models can be effectively adapted to the task of translating between abstracts in the Cochrane Database of Systematic Reviews (CDSR) and corresponding professionally-authored plain language summaries. The resulting automatically-generated summaries outperformed those from other models in their alignment with professionally-authored summaries. Furthermore, in a pilot user evaluation in which participants were blinded as to summary provenance, they were generally judged favorably to their expert-authored counterparts. In the proposed research we will develop this line of research further, by evaluating the utility of additional pre-training and auxiliary fine-tuning tasks as a means to improve the quality of generated summaries. We will also customize the models concerned to enhance their factual accuracy and readability using novel auxiliary training objectives and post-processing procedures. We will evaluate our methods as compared with robust baseline models in system-centric evaluations of content alignment with reference summaries, readability and factual correctness. Using Mechanical Turk, we will conduct user-centric evaluations of the ease with which summaries from best-performing models can be understood, as compared with CDSR expert-authored plain language summaries. These evaluations will consider both perceived interpretability, and actual comprehension, with the latter evaluated using sets of multiple choice questions to probe comprehension, recall and learning. In doing so, the proposed research will advance the state-of-the-art in automated simplification and summarization of the biomedical literature for consumption by the general public.