Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction.
- DOI
- 10.1016/j.gloepi.2024.100168
- Published
- 2024 Dec
- Container
- Global epidemiology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.gloepi.2024.100168,
title = {Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction.},
author = {Bjerre LM and Peixoto C and Alkurd R and Talarico R and Abielmona R},
year = {2024},
journal = {Global epidemiology},
doi = {10.1016/j.gloepi.2024.100168},
url = {https://doi.org/10.1016/j.gloepi.2024.100168}
}RIS
TY - JOUR TI - Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction. AU - Bjerre LM AU - Peixoto C AU - Alkurd R AU - Talarico R AU - Abielmona R PY - 2024 JO - Global epidemiology DO - 10.1016/j.gloepi.2024.100168 UR - https://doi.org/10.1016/j.gloepi.2024.100168 ER -
APA
LM, B., C, P., R, A., R, T., & R, A. (2024). Comparing AI/ML approaches and classical regression for predictive modeling using large population health databases: Applications to COVID-19 case prediction.. Global epidemiology. https://doi.org/10.1016/j.gloepi.2024.100168
Source records
- pubmed · retrieved 2026-09-26T10:34:50.041Z