A Multi-Layered Large Language Model Framework for Disease Prediction

• URL: http://arxiv.org/abs/2502.00063v1
• Date: Thu, 30 Jan 2025 18:53:50 GMT
• Title: A Multi-Layered Large Language Model Framework for Disease Prediction
• Authors: Malak Mohamed, Rokaia Emad, Ali Hamdi,
• Abstract summary: Large language models (LLMs) process complex medical data to enhance disease classification.<n>This study explores three Arabic medical text preprocessing techniques.<n> evaluating CAMeL-BERT, AraBERT, and Asafaya-BERT with LoRA.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social telehealth has revolutionized healthcare by enabling patients to share symptoms and receive medical consultations remotely. Users frequently post symptoms on social media and online health platforms, generating a vast repository of medical data that can be leveraged for disease classification and symptom severity assessment. Large language models (LLMs), such as LLAMA3, GPT-3.5 Turbo, and BERT, process complex medical data to enhance disease classification. This study explores three Arabic medical text preprocessing techniques: text summarization, text refinement, and Named Entity Recognition (NER). Evaluating CAMeL-BERT, AraBERT, and Asafaya-BERT with LoRA, the best performance was achieved using CAMeL-BERT with NER-augmented text (83% type classification, 69% severity assessment). Non-fine-tuned models performed poorly (13%-20% type classification, 40%-49% severity assessment). Integrating LLMs into social telehealth systems enhances diagnostic accuracy and treatment outcomes.

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