Title: Optimising text mining applications for enhanced medical decision making

Authors: Rym Zwawi; Emna Ammar Elhadjamor; Sonia Ayachi Ghannouchi; Slah-Eddine Ghannouchi

Addresses: RIADI Laboratory, National School of Computer Sciences (ENSI), University of La Manouba, Tunis, Tunisia; Higher Institute of Computer Science and Communication Technologies of Hammam Sousse (ISITCom), University of Sousse, Sousse, Tunisia ' RIADI Laboratory, National School of Computer Sciences (ENSI), University of La Manouba, Tunis, Tunisia; Higher Institute of Applied Sciences and Technology of Sousse (ISSAT Sousse), University of Sousse, Sousse, Tunisia ' RIADI Laboratory, National School of Computer Sciences (ENSI), University of La Manouba, Tunis, Tunisia; Higher Institute of Management of Sousse (ISGS), University of Sousse, Sousse, Tunisia ' Farhat Hached University Hospital, Sousse, Tunisia; Faculty of Medicine of Sousse, University of Sousse, Sousse, Tunisia

Abstract: The exponential growth of electronic medical records (EMRs) has increased the need for efficient data management and decision-support solutions in healthcare. This study presents a practical text-mining approach designed to support medical experts of the National Health Insurance Fund (CNAM) in Tunisia in assessing lumbalgia cases. Using regular expressions and machine learning techniques, unstructured reports from medical experts are automatically transformed into structured datasets, enabling faster and more reliable decision making. The system, developed in Python, processes 255 Word-format expert reports and converts them into a structured Excel database with 98% accuracy, as validated by domain specialists. This workflow significantly reduces processing time and enhances data quality. By integrating structured datasets with predictive analytics, the proposed approach improves medical decision support and demonstrates the potential of engineered text-mining solutions for broader healthcare applications.

Keywords: text mining; unstructured medical expert reports; medical decision-making; machine learning; electronic medical records; EMRs; healthcare optimisation.

DOI: 10.1504/IJBPIM.2025.151626

International Journal of Business Process Integration and Management, 2025 Vol.12 No.4, pp.295 - 306

Received: 04 Apr 2025
Accepted: 03 Jun 2025
Published online: 10 Feb 2026 *

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