Unleashing the power of explainable AI: sepsis sentinel's clinical assistant for early sepsis identification
Author:
Funder
RFIER-Jio Institute
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17828-y.pdf
Reference41 articles.
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3. Fu M, Yuan J, Lu M, Hong P, Zeng M (2019). An ensemble machine learning model for the early detection of sepsis from clinical data. In 2019 Computing in Cardiology (CinC) (pp. Page-1). IEEE.
4. Liu R, Greenstein JL, Sarma SV, Winslow RL (2019) Natural language processing of clinical notes for improved early prediction of septic shock in the ICU. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6103–6108). IEEE.
5. Goh KH, Wang L, Yeow AYK, Poh H, Li K, Yeow JJL, Tan GYH (2021) Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun 12(1):1–10
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