Mapping Palestinian Arabic Symptom Expressions to Standardised Medical Concepts: Corpus Development and Expert Validation
Supervisor Name
Emad Natsheh
Supervisor Email
e.natsheh@najah.edu
University
An-Najah National University
Research field
Artificial Intelligence
Bio
Dr. Emad Natsheh is an Associate Professor in the Faculty of Engineering and Information Technology at An-Najah National University. He holds a PhD in Artificial Intelligence and Renewable Energy and an MSc in Computer and Network Technology from Manchester Metropolitan University (UK), and a BSc in Computer Engineering from An-Najah National University. His research focuses on artificial intelligence, machine learning, smart systems, and renewable energy applications. Dr. Natsheh has published several papers in international journals and conferences and has experience in training in data science, Python programming, and data analysis.
Description
This project aims to develop a validated linguistic resource linking everyday Palestinian Arabic symptom expressions to standardised medical concepts. Data collection has begun, but further collection is needed to capture the diversity of expressions used across regions and contexts. Because people may describe the same symptom in several ways, the resource will retain multiple expressions for each concept while preserving their original wording. Capturing this variation is essential to represent how Palestinian Arabic speakers communicate symptoms in everyday situations. Additional expressions will be collected from healthcare professionals, medical students, existing linguistic sources, and publicly available Palestinian Arabic health content. The expressions will be organised by medical meaning, with regional and contextual information recorded where available. A qualified healthcare professional will review each proposed mapping to confirm its medical accuracy and identify ambiguous expressions that require additional context before they can be linked to a specific concept. Clinical validation is the most resource-intensive stage, requiring coordination with healthcare institutions, hospital visits, and professional review time. The expected outcome is a validated dataset documenting variation in Palestinian Arabic symptom descriptions and their corresponding standardised medical concepts. This dataset will support research on dialect-aware medical language processing and provide a foundation for a subsequent phase focused on training and evaluating AI models to interpret Palestinian Arabic symptom expressions.