Predictive Analysis of Clinical Status Assessment of Critical Patients Using Electronic Early Warning System Records with Machine Learning
Suhartini Ismail, Zahrotul Wardah, Adi Wibowo, Satriya Pranata, Sri Wahyuni · The Malaysian Journal of Nursing · 2025
Background: Rapid and accurate assessment of a patient’s clinical status in the Emergency Room (ER) is essential for timely intervention and improved outcomes. With advancements in information technology, electronic health records such as the Electronic Early Warning Score (E-EWS) have become invaluable tools in monitoring vital signs and detecting early signs of clinical deterioration. Leveraging machine learning techniques to analyse E-EWS data presents a promising approach to predict critical events including sepsis, acute respiratory distress syndrome (ARDS), cardiac arrest, and mortality. This study focuses on the application of machine learning algorithms to predict patients’ clinical status based on E-EWS records, aiming to enhance early detection and support clinical decision-making in critical care settings. Methods: The research design uses cross-sectional analysis to analyse E-EWS records with machine learning using random forest regression and random forest classification, with a total of 206 respondents, by carrying out six observations at a period of 6 hours, 12 hours, 18 hours, 24 hours, 48 hours to 72 hours. Results: The results showed that the prediction accuracy of the E-EWS record score using machine learning reached 82.26% with an MAE (mean absolute error) of 0.22, in the prediction accuracy of the patient's clinical status in 48 hours (76.19%) and 72 hours (71.43%) and the results of the accuracy of predicting hospital discharge status, the accuracy of E-EWS records and machine learning reached 97.62% with MAE 0.02 indicating that E-EWS records with machine learning with random forest algorithms have the potential to predict patient clinical status and outcomes. Conclusion: E-EWS records based on machine learning can be used to predict future patient conditions using seven EWS parameters that can predict critical patient clinical status assessment.