A Multi-Layered Large Language Model Framework for Disease Prediction

TL;DR

This paper investigates the use of large language models and Arabic medical text preprocessing techniques to improve disease classification and severity assessment in social telehealth platforms, demonstrating significant performance gains with NER-augmented models.

Contribution

It introduces a novel application of LLMs with Arabic medical text preprocessing techniques, highlighting the effectiveness of NER-augmented CAMeL-BERT for disease classification and severity assessment.

Findings

CAMeL-BERT with NER-augmented text achieved 83% accuracy in disease classification.

Non-fine-tuned models showed poor performance, with 13-20% accuracy.

Integrating LLMs improves diagnostic accuracy in social telehealth systems.

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.