COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics.

Al-Hindawi A, Abdulaal A, Rawson TM, Alqahtani SA, Mughal N, Moore LSP

DOI: 10.3389/fdgth.2021.637944
Published: 2021
Container: Frontiers in digital health
Publisher: Not recorded
Open access: yes

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BibTeX

@article{allodium:10.3389/fdgth.2021.637944,
  title = {COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics.},
  author = {Al-Hindawi A and Abdulaal A and Rawson TM and Alqahtani SA and Mughal N and Moore LSP},
  year = {2021},
  journal = {Frontiers in digital health},
  doi = {10.3389/fdgth.2021.637944},
  url = {https://doi.org/10.3389/fdgth.2021.637944}
}

RIS

TY  - JOUR
TI  - COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics.
AU  - Al-Hindawi A
AU  - Abdulaal A
AU  - Rawson TM
AU  - Alqahtani SA
AU  - Mughal N
AU  - Moore LSP
PY  - 2021
JO  - Frontiers in digital health
DO  - 10.3389/fdgth.2021.637944
UR  - https://doi.org/10.3389/fdgth.2021.637944
ER  -

APA

A, A., A, A., TM, R., SA, A., N, M., & LSP, M. (2021). COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics.. Frontiers in digital health. https://doi.org/10.3389/fdgth.2021.637944

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pubmed · retrieved 2026-09-26T02:16:51.011Z