Outcome Prediction of Patients for Different Stages of Sepsis Using Machine Learning Models
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Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-15-5341-7_82
Reference34 articles.
1. Novosad SA, Sapiano MR, Grigg C et al (2016) Vital signs: epidemiology of sepsis: prevalence of health care factors and opportunities for prevention. MMWR Morb Mortal Wkly Rep 65(33):864–869
2. van Wyk F, Khojandi A, Kamaleswaran R (2019) Improving prediction performance using hierarchical analysis of real-time data: a sepsis case study. IEEE J Biomed Health Inf 23(3):978–986
3. Subbe CP, Slater A, Menon D, Gemmell L (2006) Validation of physiological scoring systems in the accident and emergency department. Emerg Med J 23 (11):841–845
4. Lamping F, Jack T, Rübsamen N et al (2018) Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children—a data-driven approach using machine-learning algorithms. BMC Pediatrics 18(1):1471–2431
5. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M et al (2016) The third international consensus definitions for sepsis and septic shock (Sepsis-3). J Am Med Assoc 315(8):801–810
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