TY - GEN
T1 - Towards an ontology-based medication conversational agent for PrEP and PEP
AU - Amith, Muhammad
AU - Cui, Licong
AU - Roberts, Kirk
AU - Tao, Cui
N1 - Funding Information:
Research was supported by the National Library of Medicine of the National Institutes of Health under Award Numbers R01LM011829 and R00LM012104, and the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R01AI130460.
Publisher Copyright:
© 2020 Association for Computational Linguistics.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85111339737
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 31
EP - 40
BT - ACL 2020 - Natural Language Processing for Medical Conversations, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 2020 Workshop on Natural Language Processing for Medical Conversations, NLPMC 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Y2 - 10 July 2020
ER -