In this paper, we propose a hybrid approach for the optimal allocation of hospital beds to emergency patients, tackling the common challenge of unlabeled clinical data. Instead of relying on predefined labels, we use a rule-based labeling step to assign initial risk levels based on key medical features. These labels are then used to train several supervised learning models, helping to improve risk prediction and explainability. The predicted levels: Minimum, Moderate, or Maximum, serve as input to an optimization model that assigns patients to beds while reducing transfer cost and supporting infection control. Results show promising improvements in fairness and allocation efficiency, with a comparative study highlighting the benefits of our method.

This study addresses the critical challenge of hospital bed assignment during emergencies by introducing a two-stage combining unsupervised learning and optimization. In the first stage, clustering techniques (K-means, GMM, DBSCAN, and HDBSCAN) are employed to automatically categorize patients into different risk levels based on clinical features, eliminating the need for labeled training data. In the second stage, a linear programming model optimizes the assignment of patients to hospital beds by minimizing costs while respecting capacity limits and medical priority constraints. The proposed approach is tested and validated on instances derived from real-world datasets from Tunisian hospitals. Our proposal approach significantly reduces the data preprocessing workload while ensuring effective prioritization of critical cases across both small and large-scale scenarios. Additionally, the optimal number of clusters is determined through silhouette analysis, enhancing the clinical relevance of the patient clutering.

The patient bed assignment problem (PBAP) consists of assigning a set of patients to a set of beds over a horizon time, by considering not only patient pathologies and state conditions, but also bed availability, and medical needs. The PBAP becomes more complex when dealing with elective patients who require specific clinical needs. A solution to this problem is not trivial to find due to the multiple constraints that are considered. The problem is hence modeled as an optimization problem that aims to minimize a set of penalties related to the non-satisfied constraints. To deal with this complex problem, this paper proposes a constraint satisfaction-based approach to solve the problem, namely the min-conflict algorithm (MCA). The proposed approach is compared with different existing solution approaches to evaluate its performance. The experiments show that the proposed MCA performs better in most benchmark instances in terms of costs and computational time.

This study focuses on the issue of assigning hospital beds to patients based on their medical condition within a specific length of stay. To handle this problem a two-stage approach combines machine learning (ML) techniques and optimization is proposed. The first level seeks to classify patients into three categories based on their contagious states by considering various features. The second one aims to minimize the cost of assigning patients to specific beds according to their states under a set of constraints (capacity constraints, transfer constraints, etc). The problem is formulated as a linear programming to increase the patient admission rate during emergencies. The proposed methodology is tested using a set of instances based on real data taken from Tunisian Hospitals. Experimental results illustrate the effectiveness of this approach, the best model is chosen based on the highest accuracy, specificity, and sensitivity.