###### Abstract  
(1) Background: Predictive modeling is becoming increasingly relevant in healthcare, aiding in clinical decision making and improving patient outcomes. However, many of the most potent predictive models, such as deep learning algorithms, are inherently opaque, and their decisions are challenging to interpret. This study addresses this challenge by employing Shapley Additive Explanations (SHAP) to facilitate model interpretability while maintaining prediction accuracy. (2) Methods: We utilized Gradient Boosting Machines (GBMs) to predict patient outcomes in an emergency department setting, with a focus on model transparency to ensure actionable insights. (3) Results: Our analysis identifies \"Acuity\", \"Hours\", and \"Age\" as critical predictive features. We provide a detailed exploration of their intricate interactions and effects on the model's predictions. The SHAP summary plots highlight that \"Acuity\" has the highest impact on predictions, followed by \"Hours\" and \"Age\". Dependence plots further reveal that higher acuity levels and longer hours are associated with poorer patient outcomes, while age shows a non-linear relationship with outcomes. Additionally, SHAP interaction values uncover that the interaction between \"Acuity\" and \"Hours\" significantly influences predictions. (4) Conclusions: We employed force plots for individual-level interpretation, aligning with the current shift toward personalized medicine. This research highlights the potential of combining machine learning's predictive power with interpretability, providing a promising route concerning a data-driven, evidence-based healthcare future. Shapley values; machine learning; SHAP; MIMIC-IV; GBM +
Footnote †: journal: MedicalThe decision regarding the hospitalization of patients arriving at the emergency department is primarily made by medical personnel, informed by clinical examination results and a series of medical investigations. However, in situations where the workload is exacerbated or the hospital is understaffed, a reliable decision support system becomes an invaluable tool [5]. The data underpinning these systems should ideally adhere to the criteria of the five V's: volume, velocity, variety, veracity, and value [6; 7]. In handling such large healthcare datasets, particular attention must be paid to record linkage, data cleaning, similarity search, and summarization algorithms [8; 9]. Recent studies on efficient record linkage using compact Hamming space and the development of parallel and distributed engines for record linkage and similarity search, such as LSHDB, provide the foundation for advanced data preprocessing techniques [10; 11]. These methods ensure the quality of the data, which in turn leads to more reliable and effective decision-making models in healthcare. In the contemporary landscape, machine learning (ML) models and artificial intelligence (AI) technologies have been increasingly leveraged to aid in decision support systems. Deep learning applications in healthcare are rapidly evolving, bringing about significant improvements in diagnosis, treatment, and overall patient care, such as the detection and classification of malaria parasites in blood smear images [12]. However, the 'black box' nature of many of these models hinders their broader application due to the lack of transparency and explainability [13]. To address this, the current study makes use of Shapley Additive Explanations (SHAP), a unified measure of feature importance that offers enhanced model interpretability while maintaining prediction accuracy [14]. We utilized the MIMIC-IV-ED database, a publicly available critical care database, that offers an abundant, diverse, and reliable data source for healthcare-related studies [15; 16]. This open dataset facilitates research studies, promoting a collaborative and transparent research culture in the healthcare domain [15].