@inproceedings{fed4dc49b0a74959b8ad657087616cc5,
title = "Towards an ontology-based medication conversational agent for PrEP and PEP",
abstract = "HIV (human immunodeficiency virus) can damage a human{\textquoteright}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.",
author = "Muhammad Amith and Licong Cui and Kirk Roberts and Cui Tao",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics.; 2020 Workshop on Natural Language Processing for Medical Conversations, NLPMC 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 ; Conference date: 10-07-2020",
year = "2020",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "31--40",
booktitle = "ACL 2020 - Natural Language Processing for Medical Conversations, Proceedings of the Workshop",
address = "United States",
}