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

COVID-19 Vaccination (181) JMIR Theme Issue: COVID-19 Special Issue (2559) Infectious Diseases (non-STD/STI) (1738) Cross-Sectional Studies in Public Health (453) Vaccination and Immunization in the Digital Age (450) Artificial Intelligence (2252) Machine Learning (2173)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47979, first published .
Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Authors of this article:

Nicole L Vike1 Author Orcid Image ;   Sumra Bari1 Author Orcid Image ;   Leandros Stefanopoulos2, 3 Author Orcid Image ;   Shamal Lalvani2 Author Orcid Image ;   Byoung Woo Kim1 Author Orcid Image ;   Nicos Maglaveras3 Author Orcid Image ;   Martin Block4 Author Orcid Image ;   Hans C Breiter1, 5 Author Orcid Image ;   Aggelos K Katsaggelos2, 6, 7 Author Orcid Image
Article Authors Cited by (9) Tweetations (6) Metrics

Journals

  1. Lalvani S, Bari S, Vike N, Stefanopoulos L, Kim B, Block M, Maglaveras N, Katsaggelos A, Breiter H. Predicting suicidality with small sets of interpretable reward behavior and survey variables. Nature Mental Health 2024;2(7):773 View
  2. Bari S, Kim B, Vike N, Lalvani S, Stefanopoulos L, Maglaveras N, Block M, Strawn J, Katsaggelos A, Breiter H. A novel approach to anxiety level prediction using small sets of judgment and survey variables. npj Mental Health Research 2024;3(1) View
  3. Stefanopoulos L, Kim B, Sheppard J, Azcona E, Vike N, Bari S, Lalvani S, Woodward S, Maglaveras N, Block M, Katsaggelos A, Breiter H. Discrete, recurrent, and scalable patterns in non-operant judgement underlie affective picture ratings. Cognitive Processing 2025;26(2):257 View
  4. Bruxvoort K, Sy L, Contreras R, Lewin B, Hong V, Qian L, Holmquist K, Han B, Xu S. Development and validation of a clinical prediction tool for non-receipt of updated COVID-19 vaccines. Vaccine 2025;53:127074 View
  5. Liang Z, Liang G, Kuang Y, Li Z. Uncovering the Time-Frequency Relationship Between Google Trends and COVID-19 Vaccination Metrics: A Hybrid ARDL-Wavelet Coherence Model for Prediction. Cureus 2025 View
  6. Andani A, Abbing-Karahagopian V, Kavaliauskaite J, Schaffner T, Sohn W, Graña M, Marshall H, Martinon-Torres F, Bonanni P, Rappuoli R, Taha M. Invasive meningococcal disease in adolescents in Europe and select geographies: Disease burden, unmet medical need, and optimizing prevention. Human Vaccines & Immunotherapeutics 2025;21(1) View
  7. Yaseliani M, Hong J, Bian J, Cavallari L, Duarte J, Nelson D, Lo-Ciganic W, Nguyen K, Hasan M. Machine Learning Prediction of Pharmacogenetic Testing Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: Retrospective Cohort Study. JMIR Medical Informatics 2026;14:e81048 View
  8. Haque U, Saha P, Jinghui G, Khan L, Robinson R. AI Meets Attitudes: Decoding COVID-19 Vaccine Hesitancy in Alaska’s Diverse Communities (Preprint). Journal of Medical Internet Research 2025 View
  9. Bari S, Vike N, Kim B, Block M, Stefanopoulos L, Katsaggelos A, Breiter H. Predicting substance use behaviors with machine learning using small sets of judgment and contextual variables. npj Mental Health Research 2026;5(1) View