Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18752, first published .
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development

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

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

Authors of this article:

Nariman Ammar1 Author Orcid Image ;   Arash Shaban-Nejad1 Author Orcid Image

Journals

  1. Ammar N, Bailey J, Davis R, Shaban-Nejad A. Using a Personal Health Library–Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data. JMIR Formative Research 2021;5(3):e24738 View
  2. Balcombe L, De Leo D. Digital Mental Health Challenges and the Horizon Ahead for Solutions. JMIR Mental Health 2021;8(3):e26811 View
  3. Bi C, Zhou S, Liu X, Zhu Y, Yu J, Zhang X, Shi M, Wu R, He H, Zhan C, Lin Y, Shen B. NDDRF: A risk factor knowledgebase for personalized prevention of neurodegenerative diseases. Journal of Advanced Research 2022;40:223 View
  4. Shaban-Nejad A, Michalowski M, Brownstein J, Buckeridge D. Guest Editorial Explainable AI: Towards Fairness, Accountability, Transparency and Trust in Healthcare. IEEE Journal of Biomedical and Health Informatics 2021;25(7):2374 View
  5. Chen Y, Sinha B, Ye F, Tang T, Wu R, He M, Zheng X, Shen B. Prostate cancer management with lifestyle intervention: From knowledge graph to Chatbot. Clinical and Translational Discovery 2022;2(1) 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. Balcombe L, De Leo D. The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review. Frontiers in Psychology 2022;12 View
  8. Newn J, Kelly R, D'Alfonso S, Lederman R. Examining and Promoting Explainable Recommendations for Personal Sensing Technology Acceptance. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(3):1 View
  9. Joyce D, Kormilitzin A, Smith K, Cipriani A. Explainable artificial intelligence for mental health through transparency and interpretability for understandability. npj Digital Medicine 2023;6(1) View
  10. Schwartz D, Stewart A, Harris L, Ozdenerol E, Thomas F, Johnson K, Davis R, Shaban-Nejad A. The Memphis Pandemic Health Informatics System (MEMPHI-SYS)—Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance. Disaster Medicine and Public Health Preparedness 2023;17 View
  11. Rosario B, Zhang A, Patel M, Rajmane A, Xie N, Weeraratne D, Alterovitz G. Characterizing Thrombotic Complication Risk Factors Associated With COVID-19 via Heterogeneous Patient Data: Retrospective Observational Study. Journal of Medical Internet Research 2022;24(10):e35860 View
  12. Costello J, Kaur M, Reformat M, Bolduc F. Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study. Journal of Medical Internet Research 2023;25:e45268 View
  13. Kolajo T, Daramola O. Human-centric and semantics-based explainable event detection: a survey. Artificial Intelligence Review 2023;56(S1):119 View
  14. Maeda-Minami A, Yoshino T, Yumoto T, Sato K, Sagara A, Inaba K, Kominato H, Kimura T, Takishita T, Watanabe G, Nakamura T, Mano Y, Horiba Y, Watanabe K, Kamei J. Development of a novel drug information provision system for Kampo medicine using natural language processing technology. BMC Medical Informatics and Decision Making 2023;23(1) View
  15. Roytman G, Faydenko J, Budavich M, Pocius J, Cramer G. Automated Vibration and Acoustic Crepitus Sensing in Humans. Journal of Tribology 2023;145(9) View
  16. Ammar N, Olusanya O, Melton C, Chinthala L, Huang X, White B, Shaban-Nejad A. Digital Personal Health Coaching Platform for Promoting Human Papillomavirus Infection Vaccinations and Cancer Prevention: Knowledge Graph-Based Recommendation System. JMIR Formative Research 2023;7:e50210 View
  17. Beden S, Lakshmanan K, Giannetti C, Beckmann A. Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning. Applied Sciences 2023;13(23):12778 View
  18. Singh T, Rajput V, Sharma N, Satakshi , Kumar M. Sentiment analysis based distributed recommendation system. Multimedia Tools and Applications 2024 View
  19. Afzal H, Jahangir T, Mei Y, Madden A, Sarker A, Kim S. Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models. Frontiers in Public Health 2024;11 View
  20. Yu C, Zong H, Chen Y, Zhou Y, Liu X, Lin Y, Li J, Zheng X, Min H, Shen B. PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data. Briefings in Bioinformatics 2024;25(3) View
  21. Li J, Tang T, Wu E, Zhao J, Zong H, Wu R, Feng W, Zhang K, Wang D, Qin Y, Shen Z, Qin Y, Ren S, Zhan C, Yang L, Wei Q, Shen B. RARPKB: a knowledge-guide decision support platform for personalized robot-assisted surgery in prostate cancer. International Journal of Surgery 2024;110(6):3412 View

Books/Policy Documents

  1. Shaban-Nejad A, Michalowski M, Bianco S. Multimodal AI in Healthcare. View
  2. Laxmi Lydia E, Anupama C, Sharmili N. Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI). View
  3. Duque Anton S, Schneider D, Schotten H. Computer Safety, Reliability, and Security. SAFECOMP 2022 Workshops. View
  4. Shaban-Nejad A, Michalowski M, Bianco S. AI for Disease Surveillance and Pandemic Intelligence. View