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

• Published in: Journal of medical Internet research Vol. 24; no. 7; p. e37744
• Main Authors: Kumar, Anish, Ren, Jennifer, Ornstein, Katherine A, Gliatto, Peter M
• Format: Journal Article
• Language: English
• Published: Canada Gunther Eysenbach MD MPH, Associate Professor 12.07.2022; JMIR Publications
• Subjects: African Americans; Algorithms; Application programming interface; Averages; Campaigns; Caregivers; Clustering; Comorbidity; Coronaviruses; COVID-19; COVID-19 - prevention & control; COVID-19 vaccines; Drug dosages; Ethics; Ethnic identity; Housebound people; Humans; Immunization; Machine Learning; Medical schools; Patients; Physicians; Public health; Racial identity; Relatives; Research Letter; Research subjects; Scheduling; Software; Vaccination; New York; United States > US; covid; clustering algorithm; patient data; digital surveillance; machine learning; geographic cluster; COVID-19; vaccine; home care; geospatial; electronic health record integration; public health; logistic operation; vaccination

Methods Ethics Approval The Icahn School of Medicine at Mount Sinai’s Program for the Protection of Human Subjects approved and granted a waiver of consent for this secondary data analysis study (STUDY- 21-00157) which was conducted in accordance with the Helsinki Declaration. Using the Google Maps...