Machine Learning Interpretability Methods to Characterize the Importance of Hematologic Biomarkers in Prognosticating Patients with Suspected Infection
- DOI
- 10.1101/2023.05.30.23290757
- Published
- 2023-06-01
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- openRxiv
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1101/2023.05.30.23290757,
title = {Machine Learning Interpretability Methods to Characterize the Importance of Hematologic Biomarkers in Prognosticating Patients with Suspected Infection},
author = {Dipak P Upadhyaya and Yasir Tarabichi and Katrina Prantzalos and Salman Ayub and David C Kaelber and Satya S Sahoo},
year = {2023},
doi = {10.1101/2023.05.30.23290757},
url = {https://doi.org/10.1101/2023.05.30.23290757}
}RIS
TY - JOUR TI - Machine Learning Interpretability Methods to Characterize the Importance of Hematologic Biomarkers in Prognosticating Patients with Suspected Infection AU - Dipak P Upadhyaya AU - Yasir Tarabichi AU - Katrina Prantzalos AU - Salman Ayub AU - David C Kaelber AU - Satya S Sahoo PY - 2023 DO - 10.1101/2023.05.30.23290757 UR - https://doi.org/10.1101/2023.05.30.23290757 ER -
APA
Upadhyaya, D. P., Tarabichi, Y., Prantzalos, K., Ayub, S., Kaelber, D. C., & Sahoo, S. S. (2023). Machine Learning Interpretability Methods to Characterize the Importance of Hematologic Biomarkers in Prognosticating Patients with Suspected Infection. https://doi.org/10.1101/2023.05.30.23290757
Source records
- crossref · retrieved 2026-09-25T18:48:29.439Z