Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques

TL;DR

This paper presents an advanced formal ontology for Adverse Childhood Experiences (ACEs) to enhance data integration, surveillance, and research in mental health using Semantic Web techniques, making it accessible for diverse applications.

Contribution

The paper introduces a novel OWL 2-based ACEs ontology that facilitates data sharing, knowledge modeling, and practical applications in mental health research and intervention planning.

Findings

Ontology implemented and available on BioPortal

Multiple use-case scenarios demonstrate usability

Supports diagnosis, prediction, and prevention of ACEs

Abstract

Background: Adverse Childhood Experiences (ACEs), a set of negative events and processes that a person might encounter during childhood and adolescence, have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond. Objective: To better understand the relationship between ACEs and their relevant risk factors with associated health outcomes and to eventually design and implement preventive interventions, access to an integrated coherent dataset is needed. Therefore, we implemented a formal ontology as a resource to allow the mental health community to facilitate data integration and knowledge modeling and to improve ACEs surveillance and research. Methods: We use advanced knowledge representation and Semantic Web tools and techniques to implement the ontology. The current implementation of the ontology is expressed in the description logic ALCRIQ(D), a sublogic of Web Ontology Language (OWL 2). Results: The ACEs Ontology has been implemented and made available to the mental health community and the public via the BioPortal repository. Moreover, multiple use-case scenarios have been introduced to showcase and evaluate the usability of the ontology in action. The ontology was created to be used by major actors in the ACEs community with different applications, from the diagnosis of individuals and predicting potential negative outcomes that they might encounter to the prevention of ACEs in a population and designing interventions and policies. Conclusions: The ACEs Ontology provides a uniform and reusable semantic network and an integrated knowledge structure for mental health practitioners and researchers to improve ACEs surveillance and evaluation.