Hadi Hamoud


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DAVE: Differential Diagnostic Analysis Automation and Visualization from Clinical Notes
Hadi Hamoud | Fadi Zaraket | Chadi Abou Chakra | Mira Dankar
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

The Differential Analysis Visualizer for Electronic Medical Records (DAVE) is a tool that utilizes natural language processing and machine learning to help visualize diagnostic algorithms in real-time to help support medical professionals in their clinical decision-making process

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Arabic Topic Classification in the Generative and AutoML Era
Doha Albared | Hadi Hamoud | Fadi Zaraket
Proceedings of ArabicNLP 2023

Most recent models for Arabic topic classification leveraged fine-tuning existing pre-trained transformer models and targeted a limited number of categories. More recently, advances in automated ML and generative models introduced novel potentials for the task. While these approaches work for English, it is a question of whether they perform well for low-resourced languages; Arabic in particular. This paper presents (i) ArBoNeClass; a novel Arabic dataset with an extended 14-topic class set covering modern books from social sciences and humanities along with newspaper articles, and (ii) a set of topic classifiers built from it. We finetuned an open LLM model to build ArGTClass. We compared its performance against the best models built with Vertex AI (Google), AutoML(H2O), and AutoTrain(HuggingFace). ArGTClass outperformed the VertexAi and AutoML models and was reasonably similar to the AutoTrain model.