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The study of adverse childhood experiences and their consequences has emergedover the past 20 years. In this study, we aimed to leverage explainableartificial intelligence, and propose a proof-of-concept prototype for aknowledge-driven evidence-based recommendation system to improve surveillanceof adverse childhood experiences. We used concepts from an ontology that wehave developed to build and train a question-answering agent using the GoogleDialogFlow engine. In addition to the question-answering agent, the initialprototype includes knowledge graph generation and recommendation componentsthat leverage third-party graph technology. To showcase the frameworkfunctionalities, we here present a prototype design and demonstrate the mainfeatures through four use case scenarios motivated by an initiative currentlyimplemented at a children hospital in Memphis, Tennessee. Ongoing developmentof the prototype requires implementing an optimization algorithm of therecommendations, incorporating a privacy layer through a personal healthlibrary, and conducting a clinical trial to assess both usability andusefulness of the implementation. This semantic-driven explainable artificialintelligence prototype can enhance health care practitioners ability to provideexplanations for the decisions they make.

jmir medical informatics

Explainable Artificial Intelligence Recommendation System by Leveraging  the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype  Development