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Predicting readmission rates in critically ill heart failure patients during a 90-day vulnerable phase using interpretable machine learning models

Crossref DOI link: https://doi.org/10.1016/j.clinsp.2025.100775

Published Print: 2025-01

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Jiang, Meng-Han https://orcid.org/0009-0003-7009-8736
Yu, Fang https://orcid.org/0000-0002-8608-9645
Yang, Hai-Ying https://orcid.org/0009-0005-2592-2188
Yin, Sun-Jun https://orcid.org/0000-0002-2972-9132
Yang, Li-Juan https://orcid.org/0009-0003-9896-7702
Chen, Yu https://orcid.org/0000-0002-1432-3375
Li, De-Min https://orcid.org/0009-0001-9404-1334
Guo, Yu https://orcid.org/0009-0002-0473-1284
Zhu, Jia-De https://orcid.org/0009-0009-4749-7177
Cai, Wen-Ke https://orcid.org/0000-0003-2495-6632
He, Gong-Hao https://orcid.org/0000-0002-1274-3130
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Text and Data Mining valid from 2025-01-01

Text and Data Mining valid from 2025-01-01

Version of Record valid from 2025-09-01
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This article is maintained by: Elsevier

Article Title: Predicting readmission rates in critically ill heart failure patients during a 90-day vulnerable phase using interpretable machine learning models

Journal Title: Clinics

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.clinsp.2025.100775

Content Type: article

Copyright: © 2025 HCFMUSP. Published by Elsevier España, S.L.U.