Minutolo Aniello, Damiano Emanuele, De Pietro Giuseppe, Fujita Hamido, Esposito Massimo
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Naples, Italy.
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Naples, Italy.
Comput Biol Med. 2022 Feb;141:105004. doi: 10.1016/j.compbiomed.2021.105004. Epub 2021 Nov 6.
In the last years, the rise of digital technologies has enormously augmented the possibility for people to access health information and consult online versions of Patient Information Leaflets (PILs), enabling them to improve their knowledge about medication and adherence to therapies. However, health information may often be difficult to consult and comprehend due to an excessively lengthy and undersized text, coupled with the presence of many incomprehensible medical terms. To face these issues, this paper proposes a conversational agent as a valuable solution to simplify health information retrieval and improve health literacy in Italian by codifying PILs and making them query-able in natural language. In particular, the system has been devised to: i) comprehend natural language questions on medicines of interest; ii) proactively ask the user or automatically infer from the dialog state all the missing information necessary to generate an answer; iii) extract the answer from a structured knowledge base built from PILs of registered drugs. An experimental study has been carried out to evaluate both the performance and usability of the proposed system. Results showed an adequate ability of the system to handle most of the dialogues started by participants correctly, good users satisfaction, and, thus, proved its feasibility and usefulness.
在过去几年中，数字技术的兴起极大地增加了人们获取健康信息和查阅患者信息传单（PIL）在线版本的可能性，使他们能够增进对药物的了解并提高对治疗的依从性。然而，由于文本过长且字号过小，再加上存在许多难以理解的医学术语，健康信息往往难以查阅和理解。为解决这些问题，本文提出了一种对话代理，作为一种有价值的解决方案，通过对PIL进行编码并使其能够用自然语言查询，来简化健康信息检索并提高意大利人的健康素养。具体而言，该系统旨在：i）理解关于感兴趣药物的自然语言问题；ii）主动询问用户或根据对话状态自动推断生成答案所需的所有缺失信息；iii）从由注册药物的PIL构建的结构化知识库中提取答案。已开展一项实验研究来评估所提出系统的性能和可用性。结果表明，该系统有足够的能力正确处理参与者发起的大多数对话，用户满意度较高，从而证明了其可行性和实用性。
