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

Source: [Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development](<https://www.sota2.com/research/paper/68fa68bb1e209131cea1ddf7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:45.311Z

Authors: Nariman Ammar, Arash Shaban-Nejad

Published: 2020-11-06T17:45:15.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.08090>)

[PDF](<https://arxiv.org/pdf/2011.08090.pdf>)