Applying Gaussian Mixture Model for Clustering Analysis of Emergency Room Patients Based on Intubation Status
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66538-7_1
Reference8 articles.
1. Bolourani, S., et al.: A machine learning prediction model of respiratory failure within 48 hours of patient admission for COVID-19: model development and validation. J. Med. Internet Res. 23(2), e24246 (2021)
2. Venturini, M., Van Keilegom, I., De Corte, W., Vens, C.: A novel survival analysis approach to predict the need for intubation in intensive care units. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds.) AIME 2022. LNCS, vol. 13263, pp. 358–364. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09342-5_35
3. Stefan, M.S., et al.: A scoring system derived from electronic health records to identify patients at high risk for noninvasive ventilation failure. BMC Pulm. Med. 21, 52 (2021)
4. Gaudet, A., et al.: Derivation and validation of a predictive score for respiratory failure worsening leading to secondary intubation in COVID-19: the CERES score. J. Clin. Med. 11, 2172 (2022)
5. Arvind, V., et al.: Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19. J. Crit. Care 62, 25–30 (2021)
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