Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset.

Khorram B, Kouchaki S

Open source

DOI: 10.1136/bmjhci-2025-101762
Published: 2026 May 6
Container: BMJ health & care informatics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1136/bmjhci-2025-101762,
  title = {Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset.},
  author = {Khorram B and Kouchaki S},
  year = {2026},
  journal = {BMJ health \& care informatics},
  doi = {10.1136/bmjhci-2025-101762},
  url = {https://doi.org/10.1136/bmjhci-2025-101762}
}

RIS

TY  - JOUR
TI  - Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset.
AU  - Khorram B
AU  - Kouchaki S
PY  - 2026
JO  - BMJ health & care informatics
DO  - 10.1136/bmjhci-2025-101762
UR  - https://doi.org/10.1136/bmjhci-2025-101762
ER  -

APA

B, K., & S, K. (2026). Early sepsis prediction using a hybrid LSTM-GAT model: a study on the PhysioNet 2019 dataset.. BMJ health & care informatics. https://doi.org/10.1136/bmjhci-2025-101762

Source records

pubmed · retrieved 2026-11-10T14:29:16.986Z
europe-pmc · retrieved 2026-11-10T14:29:16.990Z
doaj · retrieved 2026-11-10T14:29:16.986Z