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

Amith, Muhammad; Lin, Rebecca; Cui, Licong; Wang, Dennis; Zhu, Anna; Xiong, Grace; Xu, Hua; Roberts, Kirk; Tao, Cui

doi:10.1186/s12911-020-01267-y

Cited by 10 publications

(10 citation statements)

References 24 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

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Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Amith

Onye

Ledoux

et al. 2021

BMC Med Inform Decis Mak

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Background Fast food with its abundance and availability to consumers may have health consequences due to the high calorie intake which is a major contributor to life threatening diseases. Providing nutritional information has some impact on consumer decisions to self regulate and promote healthier diets, and thus, government regulations have mandated the publishing of nutritional content to assist consumers, including for fast food. However, fast food nutritional information is fragmented, and we realize a benefit to collate nutritional data to synthesize knowledge for individuals. Methods We developed the ontology of fast food facts as an opportunity to standardize knowledge of fast food and link nutritional data that could be analyzed and aggregated for the information needs of consumers and experts. The ontology is based on metadata from 21 fast food establishment nutritional resources and authored in OWL2 using Protégé. Results Three evaluators reviewed the logical structure of the ontology through natural language translation of the axioms. While there is majority agreement (76.1% pairwise agreement) of the veracity of the ontology, we identified 103 out of the 430 statements that were erroneous. We revised the ontology and publicably published the initial release of the ontology. The ontology has 413 classes, 21 object properties, 13 data properties, and 494 logical axioms. Conclusion With the initial release of the ontology of fast food facts we discuss some future visions with the continued evolution of this knowledge base, and the challenges we plan to address, like the management and publication of voluminous amount of semantically linked fast food nutritional data.

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“…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).…”

Section: Ontology-driven Software Engine For Conversational Agentsmentioning

confidence: 99%

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

Section: Ontology-driven Software Engine For Conversational Agentsmentioning

confidence: 99%

Patient-Provider Communication Training Models for Interactive Speech Devices

Ngantcha

Amith

Tao

et al. 2021

Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Body, Motion and Behavior

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Patient-provider communication plays a major role in healthcare with its main goal being to improve the patient's health and build a trustworthy relationship between the patient and the doctor. Provider's efficiency and effectiveness in communication can be improved through training in order to meet the essential elements of communication that are relevant during medical encounters. We surmised that speech-enabled conversational agents could be used as a training tool. In this study, we propose designing an ontology-based interaction model that can direct software agents to train dental and medical students. We transformed sample scenario scripts into a formalized ontology training model that links utterances of the user and the machine that expresses patient-provider communication. We created two instance-based models from the ontology to test the operational execution of the model using a prototype software engine. The assessment revealed that the dialogue engine was able to handle about 62% of the dialogue links. Future direction of this work will focus on further enhancing and capturing the features of patient-provider communication, and eventual deployment for pilot testing.

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

Cited by 10 publications

References 24 publications

Machine and cognitive intelligence for human health: systematic review

Machine and cognitive intelligence for human health: systematic review

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

Patient-Provider Communication Training Models for Interactive Speech Devices

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