Article
Version 1
Preserved in Portico This version is not peer-reviewed
Leveraging Medical Discourse to Answer Complex Questions
Version 1
: Received: 27 December 2023 / Approved: 28 December 2023 / Online: 29 December 2023 (01:11:27 CET)
How to cite: Galitsky, B. Leveraging Medical Discourse to Answer Complex Questions. Preprints 2023, 2023122149. https://doi.org/10.20944/preprints202312.2149.v1 Galitsky, B. Leveraging Medical Discourse to Answer Complex Questions. Preprints 2023, 2023122149. https://doi.org/10.20944/preprints202312.2149.v1
Abstract
We review the literature on medical discourse and attempt to build a computational model of it. Medical discourse sheds a light on communication structure of patient-doctor and other communication scenarios in healthcare and should be leveraged to facilitate and automate this communication when it is possible and practical. We propose a unified framework to represent communication discourse at the meta-level, where the subject of the communication is expressed in a language object. So far, the broad range of work on medical discourse is detached from computational discourse analysis, and we explore the possibilities of filling this gap and computationally treat the peculiarities of how information is passed between the agents in a hospital setting. We select the domain of question answering (QA) against a corpus of medical documents of diverse nature to evaluate our computational model of medical discourse. It turns out that applying specific structures obtained in medical discourse studies improves the relevance and efficiency of question answering.
Keywords
Question answering system in health; computational medical discourse; large language models
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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