## 5 Conclusions  
In conclusion, our research study has contributed valuable insights into the realm of healthcare predictive modeling. By employing Gradient Boosting Machines (GBMs) along with Shapley Additive Explanations (SHAP), we can build a highly predictive model, and yet maintain the level of interpretability that is crucial in healthcare settings [14, 29]. The necessity for such an approach is underscored by the requirement of transparency and explainability in decision-making processes, particularly in a sensitive and impactful area like healthcare [13].  
Our findings have highlighted \"Acuity\", \"Hours\", and \"Age\" as critical variables in predicting patient outcomes in an emergency department. By leveraging the SHAP values, we are not only able to determine the importance of these features but also elucidate the intricate interactions between them [14]. This understanding may have vital implications for patient assessment and triage processes, enabling a more informed and evidence-based approach to healthcare.  
The use of force plots further facilitated an individual-level interpretation, providing insights into each patient's unique characteristics and how they influenced the model's prediction. This aligns with the contemporary shift towards personalized medicine, which emphasizes understanding and addressing individual patient characteristics [32].  
While our study focused on an emergency department context, the demonstrated approach is generalizable and can be utilized across various healthcare contexts. Future research can explore other model types and feature interpretation methods, further pushing the envelope of predictive modeling in healthcare. Moreover, different healthcare settings could be explored to validate and further refine the methodologies used in this study.  
Overall, this study has emphasized the potential of combining predictive power with interpretability in machine learning models, specifically within healthcare. With the continued advancement of AI and machine learning, this study's methodology provides a promising route for navigating the path toward a data-driven healthcare future, where evidence-based practice and patient-specific treatment are paramount.  
Conceptualization, G.F., A.S. and V.S.V.; methodology, G.F., A.S., V.K. and V.S.V.; validation, G.F.; formal analysis, V.K.; investigation, G.F. and V.S.V.; data curation, I.K.; writing--original draft preparation, G.F., A.S., V.S.V., C.K., E.B. and P.K.; writing--review and editing, G.F., A.S., V.S.V., C.K., E.B., P.K., Y.K. and A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the MIMIC-IV-ED database, which is publicly accessible. The specific version of the dataset and the queries used for this analysis are available from the corresponding author upon reasonable request.  
We would like to acknowledge the use of the MIMIC-IV-ED database (version 2.2) in this study. The database was developed and is maintained by the Laboratory for Computational Physiology at the Massachusetts Institute of Technology. This work was conducted in compliance with the database's data use agreement. We would like to express our gratitude to the many individualswho contributed to this invaluable resource. For detailed information on the database, readers are referred to the publications [15; 16].  
The authors declare no conflicts of interest.