A conversational agent system for dietary supplements use

Background Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively. Conversational agent (CA) systems have been applied to healthcare domain,...

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Bibliographic Details
Published in:BMC medical informatics and decision making 2022-07, Vol.22 (Suppl 1), p.1-153, Article 153
Main Authors: Singh, Esha, Bompelli, Anu, Wan, Ruyuan, Bian, Jiang, Pakhomov, Serguei, Zhang, Rui
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Artificial intelligence
Classification
Classifiers
Consumers
Conversational agent
Datasets
Deep learning
Diet
Dietary supplements
Domains
Health aspects
Health informatics
Knowledge
Knowledge bases (artificial intelligence)
Language
Machine learning
Named entity recognition
Natural language processing
Queries
Question answering
Questions
System effectiveness
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Summary:Background Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively. Conversational agent (CA) systems have been applied to healthcare domain, but there is no such system to answer consumers regarding DS use, although widespread use of DS. In this study, we develop the first CA system for DS use. Methods Our CA system for DS use developed on the MindMeld framework, consists of three components: question understanding, DS knowledge base, and answer generation. We collected and annotated 1509 questions to develop a natural language understanding module (e.g., question type classifier, named entity recognizer) which was then integrated into MindMeld framework. CA then queries the DS knowledge base (i.e., iDISK) and generates answers using rule-based slot filling techniques. We evaluated the algorithms of each component and the CA system as a whole. Results CNN is the best question classifier with an F1 score of 0.81, and CRF is the best named entity recognizer with an F1 score of 0.87. The system achieves an overall accuracy of 81% and an average score of 1.82 with succ@3 + score of 76.2% and succ@2 + of 66% approximately. Conclusion This study develops the first CA system for DS use using the MindMeld framework and iDISK domain knowledge base. Keywords: Dietary supplements, Question answering, Conversational agent, Natural language processing, Deep learning, Named entity recognition
ISSN:1472-6947
1472-6947
DOI:10.1186/s12911-022-01888-5