Optimizing operating room assignment using NLP-Based surgical case classification

In this paper, we address the problem of classifying medical cases into appropriate operating rooms. Each case is provided in textual form and must be assigned to one of two distinct operating rooms. To solve this problem, we begin with anonymization and preprocessing of the raw data. We then apply three different natural language processing (NLP) pipelines to transform the data into a structured tabular format suitable for classification. Common classification methods, including both shallow and deep learning approaches, are evaluated, and the results are presented using well-established classification metrics such as accuracy, precision, and recall. Numerical experiments conducted on real-world medical cases described in Polish demonstrate the effectiveness of the proposed method, which can serve as a decision support tool for surgical scheduling and operating room assignment.