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Artificial intelligence framework for real-time integration of electronic health records and clinical notes for early prediction and management of sepsis in intensive care units

Crossref DOI link: https://doi.org/10.1007/s44163-026-01820-0

Published Online: 2026-08-15

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

Authors

Kanthavel, R.

Dhaya, R.

Jayanthiladevi, A.

Rutakemwa, Maki Matandiko
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Text and Data Mining valid from 2026-08-15

Version of Record valid from 2026-08-18
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Article History

Received: 16 February 2026

Accepted: 14 July 2026

First Online: 15 August 2026

Declarations

Ethics approval and consent to participate: This study was conducted using publicly available, de-identified datasets (MIMIC-IV and eICU Collaborative Research Database). Both databases contain anonymized patient data and were collected with institutional review board approval by the respective institutions. As this study involved secondary analysis of fully de-identified data, additional ethical approval and informed consent were not required. This research is based entirely on publicly available and de-identified datasets, and no individual-level participation was involved.

Consent for publication: Not applicable. The manuscript does not contain any identifiable personal data, images, or information requiring consent for publication.

Competing interests: The authors declare no competing interests.

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