Early Warning Model for Patient Deterioration: A Machine Learning Approach for Nurse-Led Monitoring
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The early recognition of clinical deterioration in hospital inpatients continues to be a major challenge in healthcare. In this work, we proposed an intelligible machine learning (iML) based EWS for predicting patient deterioration events and facilitating early nurse interventions. We compare a range of supervised learning models, including gradient boosting and logistic regression on electronic health record (EHR) data, emphasizing predictive performance and model interpretability. To be more clinically trusted and usable, we include an integration of SHAP (SHapley Additive exPlanations) for feature attributions and inline interpretability in alert interfaces. Our findings show that the proposed system not only has high predictive performance but also has a significantly positive impact on the nurse response behavior upon the actionable and interpretable alert generation. Transparency and user-centered design are further emphasized as keys to encouraging adoption in clinical practice. These results are a step toward the larger goal of incorporating AI into healthcare processes in a way that does not erode safety, trust, or human supervision.
Author summary
Monitoring patient decline is a crucial but serious problem in hospitals as it should be detected as early as possible. This work aimed to design an interpretable machine learning (iML) model to predict clinical deterioration based on patient electronic health records. In contrast to the classical black-box model, our system is highly accurate, transparent, and can be used by frontline healthcare professionals. We tested several machine learning methods and added SHAP (Shapley Additive exPlanations) to provide insight into how we make predictions. These explanations are on the alert interface, and this makes it easier to understand and trust the system by the nurses. Our findings demonstrate that the alerts enable nurses to react faster and better to care that resulting in better care coordination. This study emphasizes the need to develop AI tools that complement the clinician’s judgment, rather than substitute it, and that are simple to add to the existing hospital procedures. Our framework presents a solution in enhancing patient safety via AI without sacrificing the aspects of human control and confidence.
