Real-Time Patient Risk Scoring Using Ensemble Machine Learning Methods

Efficient real-time patient risk assessment is important in the contemporary medical practice especially in intensive care and emergency departments. Older scoring systems like MEWS or APACHE are rules driven and usually neglect nonlinear and complex relationships on patient data. In this paper, we would like to create a real-time system of patient risk scoring that involves the ensemble machine learning techniques, such as Random Forests, Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost). Based on a large clinical data resource, we built and tested predictive models of short-term patient outcomes of deterioration. The bundle models were more accurate and sensitive s than single-model methodologies and performed better than conventional risk scoring indicators. As presented in this paper, it can be shown that ensemble learning can be used as a sound investment in real-time dynamic data-driven risk forecasting in clinical arenas, which can help clinicians to make decisions better and faster. Keywords— Real-time risk scoring, ensemble learning, patient monitoring, Random Forest, Gradient Boosting, XGBoost, healthcare analytics, clinical deterioration, machine learning in medicine, predictive modeling.