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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Mar 4, 2022
Open Peer Review Period: Mar 4, 2022 - Apr 29, 2022
Date Accepted: Jun 7, 2022
Date Submitted to PubMed: Jun 9, 2022
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Using Machine Learning to Efficiently Vaccinate Homebound Patients Against COVID-19: A Real-time Immunization Campaign

Kumar A, Ren J, Ornstein K, Gliatto P

Using Machine Learning to Efficiently Vaccinate Homebound Patients Against COVID-19: A Real-time Immunization Campaign

J Med Internet Res 2022;24(7):e37744

DOI: 10.2196/37744

PMID: 35679053

PMCID: 9328783

Using Machine Learning to Efficiently Vaccinate Homebound Patients Against COVID-19: A Real Time Immunization Campaign

  • Anish Kumar; 
  • Jen Ren; 
  • Katherine Ornstein; 
  • Peter Gliatto

ABSTRACT

Machine learning-based tools that geographically cluster patients make route planning for homebound vaccinations easier and more efficient for home-based primary care programs’ administrative teams.


 Citation

Please cite as:

Kumar A, Ren J, Ornstein K, Gliatto P

Using Machine Learning to Efficiently Vaccinate Homebound Patients Against COVID-19: A Real-time Immunization Campaign

J Med Internet Res 2022;24(7):e37744

DOI: 10.2196/37744

PMID: 35679053

PMCID: 9328783

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