Early Prediction of Sepsis Utilizing Multi-branches Multi-tasks Hybrid Deep Learning Model
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65522-7_6
Reference20 articles.
1. Despins, L.A.: Automated detection of sepsis using electronic medical record data: a systematic review. J. Healthc. Qual. 39(6), 322–333 (2017)
2. El-Rashidy, N., et al.: Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning. Neural Comput. Appl. 34, 3603–3632 (2022)
3. Gultepe, E., et al.: From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J. Am. Med. Inf. Assoc 21(2), 315–325 (2014)
4. Hatfield, K.M., et al.: Assessing variability in hospital-level mortality among US medicare beneficiaries with hospitalizations for severe sepsis and septic shock. Crit. Care Med. 46(11), 1753–1760 (2018)
5. Kok, C., et al.: Automated prediction of sepsis using temporal convolutional network. Comput. Biol. Med. 127(103957), 1–10 (2020)
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