Use of Artificial Intelligence in Early Warning Score in Critical ill Patients: Scoping Review


  • Suhartini Ismail Department of Nursing, Faculty of Medicine, Unversitas Diponegoro, Semarang, Central Java, Indonesia
  • Zahrotul Wardah Master of Nursing Program, Unversitas Diponegoro, Semarang, Central Java, Indonesia
  • Adi Wibowo Department of Computer Science, Universitas Diponegoro, Semarang, Central Java, Indonesia



Early Warning Score, Artificial Intelligence, Machine Learning, Computational Intelligence, Critical Patients


Early Warning Score (EWS) systems can identify critical patients through the application of artificial intelligence (AI). Physiological parameters like blood pressure, body temperature, heart rate, and respiration rate are encompassed in the EWS. One of AI's advantages is its capacity to recognize high-risk individuals who need emergency medical attention because they are at risk of organ failure, heart attack, or even death. The objective of this study is to review the body of research on the use of AI in EWS to accurately predict patients who will become critical. The analysis model of Arksey and O'Malley is employed in this study. Electronic databases such as ScienceDirect, Scopus, PubMed, and SpringerLink were utilized in a methodical search. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA SR) guidelines were utilized in the creation and selection of the literature. This analysis included a total of 14 articles. This article summarizes the findings on several aspects: the usefulness of AI algorithms in EWS for critical patients, types of AI algorithm models, and the accuracy of AI in predicting the quality of life of patients in EWS. The results of this review show that the integration of AI into EWS can increase accuracy in predicting patients in critical condition, including cardiac arrest, sepsis, and ARDS events that cause inhalation until the patient dies. The AI models that are often used are machine learning and deep learning models because they are considered to perform better and achieve high accuracy. The importance of further research is to identify the application of AI with EWS in critical care patients by adding laboratory result parameters and pain scales to increase prediction accuracy to obtain optimal results.


Download data is not yet available.


Abbott, T. E. F., Cron, N., Vaid, N., Ip, D., Torrance, H. D. T., & Emmanuel, J. (2018). Pre-hospital National Early Warning Score (NEWS) is associated with in-hospital mortality and critical care unit admission: A cohort study. Annals of Medicine and Surgery, 27(December 2017), 17–21.

Alam, M. Z., Rahman, M. S., & Rahman, M. S. (2019). A Random Forest based predictor for medical data classification using feature ranking. Informatics in Medicine Unlocked, 15(January), 100180.

Allen, A., Mataraso, S., Siefkas, A., Burdick, H., Braden, G., Dellinger, R. P., McCoy, A., Pellegrini, E., Hoffman, J., Green-Saxena, A., Barnes, G., Calvert, J., & Das, R. (2020). A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study. JMIR Public Health and Surveillance, 6(4), e22400.

Arksey, H., & O’Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.

Armstrong, R., Hall, B. J., Doyle, J., & Waters, E. (2011). “Scoping the scope” of a cochrane review. Journal of Public Health, 33(1), 147–150.

Arnold, J., Davis, A., Fischhoff, B., Yecies, E., Grace, J., Klobuka, A., Mohan, D., & Hanmer, J. (2019). Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study. BMJ Open, 9(10), e032187.

Awad, A., Bader-El-Den, M., McNicholas, J., & Briggs, J. (2017). Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. International Journal of Medical Informatics, 108, 185–195.

Barton, C., Chettipally, U., Zhou, Y., Jiang, Z., Lynn-Palevsky, A., Le, S., Calvert, J., & Das, R. (2019). Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs. Computers in Biology and Medicine, 109, 79–84.

Chiew, C. J., Liu, N., Tagami, T., Wong, T. H., Koh, Z. X., & Ong, M. E. H. (2019). Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department. Medicine, 98(6), e14197.

da Silva, D. B., Schmidt, D., da Costa, C. A., da Rosa Righi, R., & Eskofier, B. (2021). DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration. Expert Systems with Applications, 165, 113905.

Dziadzko, M. A., Novotny, P. J., Sloan, J., Gajic, O., Herasevich, V., Mirhaji, P., Wu, Y., & Gong, M. N. (2018). Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital. Critical Care, 22(1), 1–12.

Kang, D.-Y., Cho, K.-J., Kwon, O., Kwon, J.-M., Jeon, K.-H., Park, H., Lee, Y., Park, J., & Oh, B.-H. (2020). Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 28(1), 17.

Kapitanova, K., & Son, S. H. (2012). Machine learning basics. In Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning. CRC Press.

Kia, A., Timsina, P., Joshi, H. N., Klang, E., Gupta, R. R., Freeman, R. M., ... & Levin, M. A. (2020). MEWS++: enhancing the prediction of clinical deterioration in admitted patients through a machine learning model. Journal of clinical medicine, 9(2), 343.

Kim, S. Y., Kim, S., Cho, J., Kim, Y. S., Sol, I. S., Sung, Y., Cho, I., Park, M., Jang, H., Kim, Y. H., Kim, K. W., & Sohn, M. H. (2019). A deep learning model for real-time mortality prediction in critically ill children. Critical Care, 23, 279.

Kong, G., Xu, D.-L., Yang, J.-B., Yin, X., Wang, T., Jiang, B., & Hu, Y. (2016). Belief rule-based inference for predicting trauma outcome. Knowledge-Based Systems, 95, 35–44.

Kuan-Han, W., Fu-Jen, C., Hsiang-Ling, T., Jui-Cheng, W., Yii-Ting, H., Chih-Min, S., & Yun-Nan, C. (2021). Predicting in-hospital mortality in adult non-traumatic emergency department patients: A retrospective comparison of the modified early warning score (MEWS) and machine learning approach. PeerJ, 9(11988), 14.

Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020). Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature Communications, 2020, 1–11.

Lauritsen, S. M., Kristensen, M., Olsen, M. V, Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020). Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature Communications, 11(1), , 3852.

Lee, D. H., Yetisgen, M., Vanderwende, L., & Horvitz, E. (2020). Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision. Journal of Biomedical Informatics, 107, 103425.

Lee, Y. J., Cho, K.-J., Kwon, O., Park, H., Lee, Y., Kwon, J.-M., Park, J., Kim, J. S., Lee, M.-J., Kim, A. J., Ko, R.-E., Jeon, K., & Jo, Y. H. (2021). A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards. Resuscitation, 163, 78–85.

Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1), 271.

Lei, Y. (2017). 3 - Individual intelligent method-based fault diagnosis. In Y. Lei (Ed.), Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery (pp. 67–174). Butterworth-Heinemann.

McGowan, J., Straus, S., Moher, D., Langlois, E. V, O’Brien, K. K., Horsley, T., Aldcroft, A., Zarin, W., Garitty, C. M., Hempel, S., Lillie, E., Tunçalp, Ӧzge, & Tricco, A. C. (2020). Reporting scoping reviews-PRISMA ScR extension. Journal of Clinical Epidemiology, 123, 177–179.

Nielsen, P. B., Langkjær, C. S., Schultz, M., Kodal, A. M., Pedersen, N. E., Petersen, J. A., Lange, T., Arvig, M. D., Meyhoff, C. S., Bestle, M. H., Hølge-Hazelton, B., Bunkenborg, G., Lippert, A., Andersen, O., Rasmussen, L. S., & Iversen, K. K. (2022). Clinical assessment as a part of an early warning score—a Danish cluster-randomised, multicentre study of an individual early warning score. The Lancet Digital Health, 4(7), e497–e506.

Ongsulee, P., Chotchaung, V., Bamrungsi, E., & Rodcheewit, T. (2018). Big Data, Predictive Analytics and Machine Learning. 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE), 1–6.

Pepito, J. A., C. Locsin, R., & Constantino, R. E. (2019). Caring for Older Persons in a Technologically Advanced Nursing Future. Health, 11(05), 439–463.

Pirneskoski, J., Tamminen, J., Kallonen, A., Nurmi, J., Kuisma, M., Olkkola, K. T., & Hoppu, S. (2020). Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study. Resuscitation Plus, 4(October), 100046.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England Journal of Medicine, 380(14), 1347–1358.

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., ... & Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ digital medicine, 1(1), 18.

Rangan, E. S., Pathinarupothi, R. K., Anand, K. J. S., & Snyder, M. P. (2022). Performance effectiveness of vital parameter combinations for early warning of sepsis-an exhaustive study using machine learning. JAMIA Open, 5(4), ooac080.

Rojas, J. C., Carey, K. A., Edelson, D. P., Venable, L. R., Howell, M. D., & Churpek, M. M. (2018). Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data. Annals of the American Thoracic Society, 15(7), 846–853.

Romero-Brufau, S., Whitford, D., Johnson, M. G., Hickman, J., Morlan, B. W., Therneau, T., Naessens, J., & Huddleston, J. M. (2021). Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS). Journal of the American Medical Informatics Association, 28(6), 1207–1215.

Royal College of Physicians. (2017). National Early Warning Score (NEWS) 2 Standardising the assessment of acute-illness severity in the NHS. Updated report of a working party. Royal College of Physicians.

Royal College of Physicians. (2019). Resources to support the adoption of the National Early Warning Score. Royal College of Physucians.

Shang, Z. (2021). A Concept Analysis on the Use of Artificial Intelligence in Nursing. Cureus, 13(5).

Shickel, B., Loftus, T. J., Adhikari, L., Ozrazgat-Baslanti, T., Bihorac, A., & Rashidi, P. (2019). DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning. Scientific Reports, 9(1), 1–12.

Smith, G. B., Prytherch, D. R., Schmidt, P. E., & Featherstone, P. I. (2008). Review and performance evaluation of aggregate weighted ‘track and trigger’ systems. Resuscitation, 77(2), 170–179.

Soudan, B., Dandachi, F. F., & Nassif, A. B. (2022). Smart Health Attempting cardiac arrest prediction using artificial intelligence on vital signs from Electronic Health Records. Smart Health, 25(October 2021), 100294.

Spangler, D., Hermansson, T., Smekal, D., & Blomberg, H. (2019). A validation of machine learning-based risk scores in the prehospital setting. PLoS ONE, 14(12), 1–18.

Tang, K. J. W., Ang, C. K. E., Constantinides, T., Rajinikanth, V., Acharya, U. R., & Cheong, K. H. (2021). Artificial Intelligence and Machine Learning in Emergency Medicine. Biocybernetics and Biomedical Engineering, 41(1), 156–172.

Wu, J., Liu, C., Xie, L., Li, X., Xiao, K., Xie, G., & Xie, F. (2022). Early prediction of moderate-to-severe condition of inhalation-induced acute respiratory distress syndrome via interpretable machine learning. BMC Pulmonary Medicine, 22(1), 1–9.

Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2018). Efficient KNN Classification with Different Numbers of Nearest Neighbors. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1774–1785.




How to Cite

Ismail, S., Wardah, Z., & Wibowo, A. (2023). Use of Artificial Intelligence in Early Warning Score in Critical ill Patients: Scoping Review. JURNAL INFO KESEHATAN, 21(4), 652–670.
Abstract viewed = 16 times