Crossmark

State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators

Crossref DOI link: https://doi.org/10.1007/s10143-025-03450-z

Published Online: 2025-03-17

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Bangash, Ali Haider https://orcid.org/0000-0002-8256-3194
Toledo, Jayro

Essibayi, Muhammed Amir

Haranhalli, Neil

De la Garza Ramos, Rafael https://orcid.org/0000-0002-5536-2514
Altschul, David J. https://orcid.org/0000-0002-5130-1378
Tjoumakaris, Stavropoula https://orcid.org/0000-0002-1054-9414
Yassari, Reza

Starke, Robert M.

Rahmani, Redi https://orcid.org/0000-0003-3161-2125
License Information

Text and Data Mining valid from 2025-03-17

Version of Record valid from 2025-03-17
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Article History

Received: 17 August 2024

Revised: 29 December 2024

Accepted: 5 March 2025

First Online: 17 March 2025

Declarations

:

: Being a post-hoc analysis of a published trial, ethics approval was not required to be sought.

: Being a post-hoc analysis of a published trial, the consent to participate was not required to be sought.

: The authors declare no competing interests.

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