Proactive advising: a machine learning driven approach to vaccine hesitancy
Open accessAbstract
Despite once being nearly eradicated, Measles cases in Europe have surged to a 20-year high with more than 60,000 cases in 2018, due to a dramatic decrease in vaccination rates. The decrease in Measles, Mumps, and Rubella (MMR) vaccination rates can be attributed to an increase in ‘vaccine hesitancy’, or the delay in acceptance or refusal of vaccines despite their availability. Vaccine hesitancy is a relatively new global problem for which effective interventions are not yet established. In this paper, a novel machine learning approach to identify children at risk of not being vaccinated against MMR is proposed, with the objective of facilitating proactive action by healthcare workers and policymakers. A use case of the approach is the provision of individualized informative guidance to families that may otherwise become or are already vaccine hesitant. Using a LASSO logistic regression model trained on 44,000 child Electronic Health Records (EHRs), vaccine hesitant families can be identified with a higher precision (0.72) than predicting vaccine uptake based on a child’s infant vaccination record alone (0.63). The model uses a low number of attributes of the child and his or her family and community to produce a prediction, making it readily interpretable by healthcare professionals. The implementation of the machine learning model into an open source dashboard for use by healthcare providers and policymakers as an Early Warning and Monitoring System (EWS) against vaccine hesitancy is proposed. The EWS would facilitate a wide variety of proactive, anticipatory and therefore potentially more effective public health interventions, compared to reactive interventions taken after vaccine rejections.
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The classifiers chosen are representative of the types of models commonly used in ML for public policy systems.
“We chose these classifiers because they are representative of the types of models commonly used in ML for public policy systems [9,12,20,56].”
From It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy (2022) · cites this paper as [9]
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Bell, A., Solano-Kamaiko, I., Nov, O., & Stoyanovich, J. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533090 · Cited by 103 · see citing works
Self-citation
- Authors
- Bell, A., Rich, A., Teng, M., Orešković, T., Bras, N. B., Mestrinho, L., Golubovic, S., Pristas, I., & Zejnilovic, L.
- Affiliations
- NOVA School of Business and Economics (PT)
- New York University (US)
- CentraleSupélec/ENS Paris-Sacla,Gif-sur-Yvette,France
- IBM (United States) (US)
- Universidade Autónoma de Lisboa,Lisbon,Portugal
- Nova School of Business and Economics,Lisbon,Portugal
- Hrvatski zavod za javno zdravstvo (HR)
- Croatian National Institute of Public Health,Zagreb,Croatia
- Venue
- 2019 IEEE International Conference on Healthcare Informatics (ICHI)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1-6
- OpenAlex
- W2991533154
- Cited by
- 12
- References
- 14
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 3 · 2022: 1 · 2021: 4
- FWCI
- 13.48
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE International Conference on Healthcare Informatics (ICHI) (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Topics
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- vaccine hesitancy, measles, MMR vaccination, risk prediction, LASSO logistic regression, electronic health records, early warning systems, model interpretability, childhood immunization, public health interventions, measles resurgence
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