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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Crowdsourcing for Creating a Dataset for Training a Medication Chatbot.

Cyril R Zgraggen1, Sebastian B Kunz1, Kerstin Denecke1

  • 1Bern University of Applied Sciences, Switzerland.

Studies in Health Technology and Informatics
|May 27, 2021
PubMed
Summary

Crowdsourcing effectively generated thousands of diverse user questions for a medication chatbot in one week. This data will train the chatbot to better understand and respond to user needs in mobile health applications.

Keywords:
Conversational user interfaceartificial intelligencechatbotcrowdsourcingmedication managementnatural language understanding

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Area of Science:

  • Health Informatics
  • Human-Computer Interaction
  • Natural Language Processing

Background:

  • Chatbots are increasingly utilized in mobile health applications to facilitate user interaction.
  • A significant challenge is developing comprehensive knowledge bases for chatbot query interpretation.
  • Representing diverse user queries requires robust pattern and rule generation.

Purpose of the Study:

  • To evaluate the efficacy of crowdsourcing in generating user queries for a medication chatbot.
  • To assess the variety and scope of user information needs captured through crowdsourcing.

Main Methods:

  • A crowdsourcing approach was employed to gather examples of potential user queries.
  • Crowdworkers were tasked with generating questions for a medication chatbot over a one-week period.

Main Results:

  • The crowdsourcing effort successfully generated 2,738 user questions within one week.
  • The generated examples demonstrated a wide range of query formulations and user information needs.

Conclusions:

  • Crowdsourcing is a viable method for rapidly building extensive datasets of user queries for health chatbots.
  • The collected data will be instrumental in training a medication chatbot to enhance user interaction and understanding.