Conversational ontology operator: patient-centric vaccine dialogue management engine for spoken conversational agents

Abstract

Background Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction. Methods We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials. Results Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators. Conclusions Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.

Machine and cognitive intelligence for human health: systematic review

“…For example, to facilitate question–answering in the medical domain, Abacha et al [ 30 ] developed a semantic model with the basis of NLP for in-depth analysis of medical questions and documents. Amith et al [ 84 ] proposed a software engine that harnesses patient health information dialog ontologies for dialog and contextual information management between agents and health consumers. In [ 85 ], a relative entropy-based sentence subset selection method promoted speech recognition error and language model perplexity.…”

The ontology of fast food facts: conceptualization of nutritional fast food data for consumers and semantic web applications

“…In a separate domain, we (MA, CT) have worked on interactive patient-facing technology for vaccine education and counseling. One of the tools from that endeavor is a dialogue system engine [ 55 ] that we envision to automate lightweight evidence-based nutritional counseling to assist in curbing eating behaviors in individuals who have diseases caused by diet and nutritional factors. We intend to use the Ontology of Fast Food Facts to supplement nutritional information that could furnish fast food nutritional information in the automated interactive dialogue.…”

“…We intend to use the Ontology of Fast Food Facts to supplement nutritional information that could furnish fast food nutritional information in the automated interactive dialogue. Supplementing the dialogue system engine was a question-answering (QA) subsystem that responded to health consumer questions and generated simple natural language responses from the vaccine knowledge base’s triples [ 55 ]. While we recognize that merely answering questions will not impact behavior the way formalized evidenced-based counseling would, we also plan on repurposing the technology to assess the portability and performance of our QA system.…”

Patient-Provider Communication Training Models for Interactive Speech Devices

“…In a later study, we developed a software engine that uses the PHIDO model to reason and manage the health dialogue strategies [65]. This software engine is named Conversational Ontology Operator (COO).…”

“…This basic process loops through continuously. More detailed treatment of the process is provided in our previous work [65].…”