1. Heyland, D. K., Dhaliwal, R., Jiang, X. & Day, A. G. Identifying critically ill patients who benefit the most from nutrition therapy: the development and initial validation of a novel risk assessment tool. Crit. Care15, R268 (2011).
2. Rahman, A. et al. Identifying critically-ill patients who will benefit most from nutritional therapy: Further validation of the “modified NUTRIC” nutritional risk assessment tool. Clin. Nutr.35, 158–162 (2016).
3. Jeong, D. H. et al. Relationship between Nutrition Intake and 28-Day Mortality Using Modified NUTRIC Score in Patients with Sepsis. Nutrients11, 1906 (2019).
4. Mukhopadhyay, A. et al. Association of modified NUTRIC score with 28-day mortality in critically ill patients. Clin. Nutr. Edinb. Scotl.36, 1143–1148 (2017).
5. Villacrs, C. Q. et al. 376: ASSESSING THE NUTRIC SCORE 28-DAY MORTALITY PREDICTION IN CRITICALLY ILL CANCER PATIENTS. Crit. Care Med.48, 170–170 (2020).
6. Jones, S. L. et al. Outcomes and Resource Use of Sepsis-associated Stays by Presence on Admission, Severity, and Hospital Type. Med. Care54, 303–310 (2016).
7. Al-Saad, N. A. & Nortje, J. Principles of resource allocation in critical care. BJA Educ.17, 6 (2017).
8. Levy, M. M. et al. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference: Crit. Care Med.31, 1250–1256 (2003).
9. Singer, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA315, 801–810 (2016).
10. Knaus, W. A., Draper, E. A., Wagner, D. P. & Zimmerman, J. E. APACHE II: a severity of disease classification system. Crit. Care Med.13, 818–829 (1985).
11. Vincent, J.-L. et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive Care Med.22, 707–710 (1996).
12. Miranda, D. R., de Rijk, A. & Schaufeli, W. Simplified Therapeutic Intervention Scoring System: the TISS-28 items--results from a multicenter study. Crit. Care Med.24, 64–73 (1996).
13. Jensen, G. L. et al. Adult starvation and disease-related malnutrition: A proposal for etiology-based diagnosis in the clinical practice setting from the International Consensus Guideline Committee. Clin. Nutr.29, 151–153 (2010).
14. Malnutrition Advisory Group. A consistent and reliable tool for malnutrition screening. Nurs. Times99, 26–27 (2003).
15. Lim, S.-L. et al. Development and validation of 3-Minute Nutrition Screening (3-MinNS) tool for acute hospital patients in Singapore. Asia Pac. J. Clin. Nutr.18, 395–403 (2009).
16. Kruizenga, H. M., Seidell, J. C., de Vet, H. C. W., Wierdsma, N. J. & van Bokhorst-de van der Schueren, M. a. E. Development and validation of a hospital screening tool for malnutrition: the short nutritional assessment questionnaire (SNAQ). Clin. Nutr. Edinb. Scotl.24, 75–82 (2005).
17. Ferguson, M., Capra, S., Bauer, J. & Banks, M. Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutr. Burbank Los Angel. Cty. Calif15, 458–464 (1999).
18. Anthony, P. S. Nutrition screening tools for hospitalized patients. Nutr. Clin. Pract. Off. Publ. Am. Soc. Parenter. Enter. Nutr.23, 373–382 (2008).
19. Kondrup, J., Rasmussen, H. H., Hamberg, O., Stanga, Z., & Ad Hoc ESPEN Working Group. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin. Nutr. Edinb. Scotl.22, 321–336 (2003).
20. Detsky, A. S. et al. What is subjective global assessment of nutritional status? JPEN J. Parenter. Enteral Nutr.11, 8–13 (1987).
21. Heyland, D. et al. A Randomized Trial of Glutamine and Antioxidants in Critically Ill Patients. N. Engl. J. Med.368, 1489–1497 (2013).
22. de Vries, M. C., Koekkoek, W., Opdam, M. H., van Blokland, D. & van Zanten, A. R. Nutritional assessment of critically ill patients: validation of the modified NUTRIC score. Eur. J. Clin. Nutr.72, 428–435 (2018).
23. Rudd, K. E. et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. The Lancet395, 200–211 (2020).
24. Verburg, I. W. M. et al. Which Models Can I Use to Predict Adult ICU Length of Stay? A Systematic Review*: Crit. Care Med.45, e222–e231 (2017).
25. Lisboa, T. et al. The Ventilator-Associated Pneumonia PIRO Score. Chest134, 1208–1216 (2008).
26. Sukmark, T. et al. SEA-MAKE score as a tool for predicting major adverse kidney events in critically ill patients with acute kidney injury: results from the SEA-AKI study. Ann. Intensive Care10, 42 (2020).
27. Kwak, G. H., Ling, L. & Hui, P. Predicting the Need for Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model. ShockPublish Ahead of Print, (2020).
28. Liu, R. et al. Data-driven discovery of a novel sepsis pre-shock state predicts impending septic shock in the ICU. Sci. Rep.9, 6145 (2019).
29. Johnson, A. E. W. et al. MIMIC-III, a freely accessible critical care database. Sci. Data3, 160035 (2016).
30. Siu, B. M. K., Kwak, G. H., Ling, L. & Hui, P. Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches. Sci. Rep.10, 20931 (2020).
31. Buswell, L., Hayes, A. & Baombe, J. BET 2: Predicting the need for endotracheal intubation in poisoned patients. Emerg. Med. J. EMJ36, 573–575 (2019).
32. Onishi, S., Osuka, A., Kuroki, Y. & Ueyama, M. Indications of early intubation for patients with inhalation injury. Acute Med. Surg.4, 278–285 (2017).