This paper is in the following e-collection/theme issue:

Mental Health Surveillance and Epidemiology (64) Epidemiology (161) Ontologies, Classifications, and Coding (243) Behavioural Surveillance for Public Health (235) Innovative Methods in Public Health and Surveillance (336) Theory and Frameworks in Mental Health (24) Mental Health Issues in Adolescence (457)

Published on in Vol 6, No 5 (2019): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13498, first published .
Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques

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

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

Authors of this article:

Jon Hael Brenas1 Author Orcid Image ;   Eun Kyong Shin1 Author Orcid Image ;   Arash Shaban-Nejad1 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

  1. Brenas J, Shaban-Nejad A. Health Intervention Evaluation Using Semantic Explainability and Causal Reasoning. IEEE Access 2020;8:9942 View
  2. Dhombres F, Charlet J. 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 2020;29(01):163 View
  3. Hao W, Menglin J, Guohui T, Qing M, Guoliang L. R-KG: A Novel Method for Implementing a Robot Intelligent Service. AI 2020;1(1):117 View
  4. Ammar N, Shaban-Nejad A. Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development. JMIR Medical Informatics 2020;8(11):e18752 View
  5. Ammar N, Bailey J, Davis R, Shaban-Nejad A. The Personal Health Library (PHL): Enabling an mHealth Recommender System for Self-Management of Diabetes among Underserved Population with Multiple Chronic Conditions (Preprint). JMIR Formative Research 2020 View
  6. Brakefield W, Ammar N, Shaban-Nejad A. An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model. JMIR Formative Research 2022;6(7):e36055 View
  7. Giabbanelli P, Rice K, Galgoczy M, Nataraj N, Brown M, Harper C, Nguyen M, Foy R. Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping. Social Network Analysis and Mining 2022;12(1) View
  8. Giabbanelli P, Rice K, Nataraj N, Brown M, Harper C. 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 2023;23(1) View
  9. White B, Prasad R, Ammar N, Yaun J, Shaban-Nejad A. Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review. JMIR Pediatrics and Parenting 2024;7:e58403 View

Books/Policy Documents

  1. Shaban-Nejad A, Michalowski M. Precision Health and Medicine. View
  2. Shaban-Nejad A, Michalowski M, Buckeridge D. Explainable AI in Healthcare and Medicine. View
  3. Shaban-Nejad A, Michalowski M, Bianco S. AI for Disease Surveillance and Pandemic Intelligence. View