  The study of adverse childhood experiences and their consequences has emerged
over the past 20 years. In this study, we aimed to leverage explainable
artificial intelligence, and propose a proof-of-concept prototype for a
knowledge-driven evidence-based recommendation system to improve surveillance
of adverse childhood experiences. We used concepts from an ontology that we
have developed to build and train a question-answering agent using the Google
DialogFlow engine. In addition to the question-answering agent, the initial
prototype includes knowledge graph generation and recommendation components
that leverage third-party graph technology. To showcase the framework
functionalities, we here present a prototype design and demonstrate the main
features through four use case scenarios motivated by an initiative currently
implemented at a children hospital in Memphis, Tennessee. Ongoing development
of the prototype requires implementing an optimization algorithm of the
recommendations, incorporating a privacy layer through a personal health
library, and conducting a clinical trial to assess both usability and
usefulness of the implementation. This semantic-driven explainable artificial
intelligence prototype can enhance health care practitioners ability to provide
explanations for the decisions they make.
