ArticleJMIR mental health2019
Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques.
Article in JMIR mental health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Exploring the link between adverse childhood experiences and cancer development - insights and intervention recommendations from a scoping review.Npj mental health research · 2025Article
- Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review.JMIR pediatrics and parenting · 2024Review
- A systems science approach to identifying data gaps in national data sources on adolescent suicidal ideation and suicide attempt in the United States.BMC public health · 2023Article
- Achieving Inclusivity by Design: Social and Contextual Information in Medical Knowledge.Yearbook of medical informatics · 2022Review
- An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model.JMIR formative research · 2022Article
- Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping.Social network analysis and mining · 2022Article
- Capturing Demographic, Health-Related, and Psychosocial Variables in a Standardized Manner: Towards Improving Cancer Screening Adherence.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Mapping the Complexity of Suicide by Combining Participatory Modeling and Network Science.Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining · 2021Article
- Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences.Frontiers in big data · 2021Article
- Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development.JMIR medical informatics · 2020Article
- Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook.Yearbook of medical informatics · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
backgroundAdverse 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.
objectiveTo 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.
methodsWe 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).
resultsThe 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.
conclusionsThe 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.
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