# Health-LLM: Personalized Retrieval-Augmented Disease Prediction System

arXiv: https://arxiv.org/abs/2402.00746 · HTML: https://mlsanity.com/paper/2402.00746/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets](https://mlsanity.com/paper/2204.08997/) (2022)
- [CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation](https://mlsanity.com/paper/2307.08290/) (2023)

## Built on by (most cited first, 17 of 17)

- [Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities](https://mlsanity.com/paper/2406.02787/) (2024)
- [M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning](https://mlsanity.com/paper/2409.15657/) (2024)
- [Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models](https://mlsanity.com/paper/2407.11282/) (2024)
- [PersoBench: Benchmarking Personalized Response Generation in Large Language Models](https://mlsanity.com/paper/2410.03198/) (2024)
- [ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction](https://mlsanity.com/paper/2405.06649/) (2024)
- [KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs](https://mlsanity.com/paper/2507.02773/) (2025)
- [Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses](https://mlsanity.com/paper/2405.06712/) (2024)
- [From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice](https://mlsanity.com/paper/2410.01812/) (2024)
- [MedTVT-R1: A Multimodal LLM Empowering Medical Reasoning and Diagnosis](https://mlsanity.com/paper/2506.18512/) (2025)
- [Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs](https://mlsanity.com/paper/2402.14609/) (2024)
- [A Multi-Agent Framework Integrating Large Language Models and Generative AI for Accelerated Metamaterial Design](https://mlsanity.com/paper/2503.19889/) (2025)
- [MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement](https://mlsanity.com/paper/2601.10949/) (2026)
- [Eir: Thai Medical Large Language Models](https://mlsanity.com/paper/2409.08523/) (2024)
- [HealthBranches: Synthesizing Clinically-Grounded Question Answering Datasets via Decision Pathways](https://mlsanity.com/paper/2508.07308/) (2025)
- [Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare](https://mlsanity.com/paper/2607.17508/) (2026)
- [EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning](https://mlsanity.com/paper/2604.10455/) (2026)
- [Do No Harm? Hallucination and Actor-Level Abuse in Web-Deployed Medical Large Language Models](https://mlsanity.com/paper/2605.20591/) (2026)

## Abstract (arXiv)

Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in intelligent medical treatment. However, there are conspicuous challenges such as vast data volumes and inconsistent symptom characterization standards, preventing full integration of healthcare AI systems with individual patients' needs. To promote professional and personalized healthcare, we propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction. We experiment on a large number of health reports to assess the effectiveness of Health-LLM system. The results indicate that the proposed system surpasses the existing ones and has the potential to significantly advance disease prediction and personalized health management.