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Personalized sepsis mortality prediction: An interpretable machine learning nomogram

Crossref DOI link: https://doi.org/10.1016/j.clinsp.2026.100872

Published Print: 2026-01

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Authors

Weng, Lulu https://orcid.org/0009-0005-8768-5739
Li, Haidong https://orcid.org/0000-0002-7471-129X
Lv, Yonglai https://orcid.org/0009-0005-3556-1793
Luo, Jiayi https://orcid.org/0009-0001-3352-7349
Wen, Zhenliang https://orcid.org/0000-0002-7942-7060
Shi, Jiawen https://orcid.org/0009-0009-4888-7900
Zhong, Li https://orcid.org/0000-0002-2006-4138
Funding

Funding for this research was provided by:

Huzhou Municipal Science and Technology Bureau (2025GYB06)

Zhejiang Province Natural Science Foundation (LTGD23H090001)

Medical Science and Technology Project of Zhejiang Province (2023KY317)
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Text and Data Mining valid from 2026-01-01

Text and Data Mining valid from 2026-01-01

Version of Record valid from 2026-02-08
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This article is maintained by: Elsevier

Article Title: Personalized sepsis mortality prediction: An interpretable machine learning nomogram

Journal Title: Clinics

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.clinsp.2026.100872

Content Type: article

Copyright: © 2026 HCFMUSP. Published by Elsevier España, S.L.U.