Machine learning–optimized discharge timing in typhoid care: Implications for clinical outcomes, cost efficiency, and health system performance
- DOI
- 10.1371/journal.pone.0354148
- Published
- 2026-07-23
- Container
- PLOS One
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0354148,
title = {Machine learning–optimized discharge timing in typhoid care: Implications for clinical outcomes, cost efficiency, and health system performance},
author = {Shekoofeh Sadat Momahhed and Atefehsadat Haghighathoseini},
year = {2026},
journal = {PLOS One},
doi = {10.1371/journal.pone.0354148},
url = {https://doi.org/10.1371/journal.pone.0354148}
}RIS
TY - JOUR TI - Machine learning–optimized discharge timing in typhoid care: Implications for clinical outcomes, cost efficiency, and health system performance AU - Shekoofeh Sadat Momahhed AU - Atefehsadat Haghighathoseini PY - 2026 JO - PLOS One DO - 10.1371/journal.pone.0354148 UR - https://doi.org/10.1371/journal.pone.0354148 ER -
APA
Momahhed, S. S., & Haghighathoseini, A. (2026). Machine learning–optimized discharge timing in typhoid care: Implications for clinical outcomes, cost efficiency, and health system performance. PLOS One. https://doi.org/10.1371/journal.pone.0354148
Source records
- crossref · retrieved 2026-09-26T04:43:42.922Z