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EQRbot: A chatbot delivering EQR argument-based explanations.

Federico Castagna1, Alexandra Garton1, Peter McBurney2

  • 1School of Computer Science, University of Lincoln, Lincoln, United Kingdom.

Frontiers in Artificial Intelligence
|April 10, 2023
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Summary
This summary is machine-generated.

This study introduces the EQR argument scheme for healthcare chatbots, enhancing patient understanding of treatment advice. EQRbot provides clear, detailed explanations via personalized Telegram messages, improving upon existing systems.

Keywords:
XAIargument schemeschatbotcomputational argumentationdecision-support systemsexplainabilityhealthcare

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Area of Science:

  • Artificial Intelligence
  • Computational Linguistics
  • Health Informatics

Background:

  • Healthcare communication demands clarity and accuracy, necessitating robust virtual support systems.
  • Argumentation schemes offer formal frameworks for modeling information exchange and generating explanations.
  • Existing chatbots often lack the detailed, interrogative explanations required for medical advice.

Purpose of the Study:

  • To detail the Explanation-Question-Response (EQR) argument scheme.
  • To deploy the EQR scheme in a chatbot (EQRbot) for generating patient treatment advice.
  • To evaluate EQRbot's effectiveness against baseline and existing argumentation-based chatbots.

Main Methods:

  • Development of the EQR argument scheme, a pattern for agent interactions (Explanation-Question-Response).
  • Implementation of EQRbot, a chatbot utilizing the EQR scheme to generate patient explanations.
  • Integration of EQRbot with Telegram for personalized message delivery of treatment advice.
  • Comparative analysis of EQRbot against a previous baseline and other argumentation-based chatbots.

Main Results:

  • EQRbot successfully generates detailed explanations for patient treatment advice using the EQR scheme.
  • The chatbot provides exhaustive information and answers follow-on queries through personalized Telegram messages.
  • EQRbot demonstrates significant improvements compared to baseline and existing argumentation-based conversational agents.

Conclusions:

  • The EQR argument scheme is a well-suited formal tool for modeling healthcare information exchange.
  • EQRbot effectively enhances patient understanding of treatment advice through detailed, interactive explanations.
  • Argumentation-based chatbots, like EQRbot, represent a promising advancement in virtual healthcare support systems.