Amith Muhammad Tuan, Cui Licong, Roberts Kirk, Tao Cui
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas.
Proc Conf Assoc Comput Linguist Meet. 2020 Jul;2020:31-40. doi: 10.18653/v1/2020.nlpmc-1.5.
HIV (human immunodeficiency virus) can damage a human's immune system and cause Acquired Immunodeficiency Syndrome (AIDS) which could lead to severe outcomes, including death. While HIV infections have decreased over the last decade, there is still a significant population where the infection permeates. PrEP and PEP are two proven preventive measures introduced that involve periodic dosage to stop the onset of HIV infection. However, the adherence rates for this medication is low in part due to the lack of information about the medication. There exist several communication barriers that prevent patient-provider communication from happening. In this work, we present our ontology-based method for automating the communication of this medication that can be deployed for live conversational agents for PrEP and PEP. This method facilitates a model of automated conversation between the machine and user can also answer relevant questions.
人类免疫缺陷病毒(HIV)会损害人体免疫系统,引发获得性免疫缺陷综合征(AIDS),这可能导致包括死亡在内的严重后果。尽管在过去十年中HIV感染有所减少,但仍有相当一部分人群受到感染。暴露前预防(PrEP)和暴露后预防(PEP)是两种经证实的预防措施,需要定期服药以阻止HIV感染的发生。然而,这种药物的依从率较低,部分原因是缺乏关于该药物的信息。存在多种沟通障碍,阻碍了医患之间的沟通。在这项工作中,我们展示了基于本体的方法,用于自动化这种药物的沟通,该方法可部署用于PrEP和PEP的实时对话代理。这种方法促进了机器与用户之间的自动对话模型,还能回答相关问题。