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Brenas JH, Shin EK, Shaban-Nejad A. Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques. JMIR Ment Health. 2019;6(5):e13498doi: 10.2196/13498.
Brenas, J. H., Shin, E. K., & Shaban-Nejad, A. (2019). Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques. JMIR mental health, 6(5), e13498. https://doi.org/10.2196/13498
Brenas, Jon Hael, et al. "Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques." JMIR mental health vol. 6,5 (2019): e13498. doi: https://doi.org/10.2196/13498
Brenas JH, Shin EK, Shaban-Nejad A. Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques. JMIR Ment Health. 2019 May 21;6(5):e13498. doi: 10.2196/13498. PMID: 31115344; PMCID: PMC6707574.
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JMIR Ment Health. 2019 May 21;6(5):e13498. doi: 10.2196/13498.

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

JMIR mental health

Jon Hael Brenas, Eun Kyong Shin, Arash Shaban-Nejad

Affiliations

  1. Department of Pediatrics, Oak Ridge National Laboratory Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN, United States.

PMID: 31115344 PMCID: PMC6707574 DOI: 10.2196/13498

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.

©Jon Hael Brenas, Eun Kyong Shin, Arash Shaban-Nejad. Originally published in JMIR Mental Health (http://mental.jmir.org), 21.05.2019.

Keywords: adverse childhood experiences; computational psychiatry; mental health surveillance; ontologies; semantics

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