Crossmark

Deep learning approaches for time series prediction of renal recovery in medical critically Ill patients with acute kidney injury: LSTM, GRU, and transformer models

Crossref DOI link: https://doi.org/10.1186/s13054-026-05942-w

Published Online: 2026-03-07

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

Authors

Ratchatorn, Anawat

Ketdao, Natdanai

Sonsilphong, Suphachoke

Triamwichanon, Donlaporn

Panitchote, Anupol https://orcid.org/0000-0001-7125-9886
Funding

Funding for this research was provided by:

The NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (B04G650012)
License Information

Text and Data Mining valid from 2026-03-07

Accepted Manuscript valid from 2026-03-07
More Information

Article History

Received: 21 January 2026

Accepted: 2 March 2026

First Online: 7 March 2026

Declarations

:

: The Khon Kaen University Ethics Committee approved the protocol (HE664004) in accordance with the Declaration of Helsinki. Written informed consent was obtained for all prospective participants.

: Not applicable.

: The authors declare no competing interests.

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