# A Multi-Layered Large Language Model Framework for Disease Prediction

arXiv: https://arxiv.org/abs/2502.00063 · HTML: https://mlsanity.com/paper/2502.00063/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction](https://mlsanity.com/paper/2509.02446/) (2025)
- [Two-Stage Quranic QA via Ensemble Retrieval and Instruction-Tuned Answer Extraction](https://mlsanity.com/paper/2508.06971/) (2025)
- [Severity-Aware Weighted Loss for Arabic Medical Text Generation](https://mlsanity.com/paper/2604.06346/) (2026)
- [A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation](https://mlsanity.com/paper/2604.06365/) (2026)
- [Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation](https://mlsanity.com/paper/2606.05510/) (2026)

## Abstract (arXiv)

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.