Predicting Patients' Vital Sign Deterioration Using Machine Learning

Accurate prediction of patient vital-sign deterioration is essential for preventing adverse clinical outcomes; however, accurate risk identification and effective early-warning mechanisms remain challenging in routine clinical practice. This study leverages data from the MIMIC-IV database to develop machine learning–based predictive models suitable for integration into automated clinical early-warning systems. Three models- Random Forest, Support Vector Machine (SVM), and an ensemble approach- were evaluated using comprehensive classification and regression performance metrics. Among these, the Random Forest model consistently outperformed baseline methods and established clinical benchmarks. Cross-validation demonstrated strong generalizability, with a mean accuracy of 0.99 ± 0.012 and an area under the receiver operating characteristic curve (AUC) of 0.98. The model also achieved a high F1-score of 0.98; balanced precision–recall performance and a low mean squared error (MSE) of 0.0073, indicating robust predictive accuracy. Moreover, the average predicted probability for emergency response reached 0.2, supporting effective risk stratification. Comparative analysis shows that the proposed framework exceeds commonly reported state-of-the-art performance, which typically achieves AUC values between 0.90 and 0.95. Overall, the findings highlight the model’s reliability, robustness, and adaptability across clinical contexts, demonstrating strong potential for deployment in hospital monitoring systems, intensive care units, and emergency medical services where real-time risk assessment is critical for enabling timely and targeted clinical interventions.