# KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs

arXiv: https://arxiv.org/abs/2507.02773 · HTML: https://mlsanity.com/paper/2507.02773/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 16 of 16)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs](https://mlsanity.com/paper/2311.09774/) (2023)
- [Large Language Models and Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2308.06374/) (2023)
- [Large Language Models for Disease Diagnosis: A Scoping Review](https://mlsanity.com/paper/2409.00097/) (2024)
- [CPLLM: Clinical Prediction with Large Language Models](https://mlsanity.com/paper/2309.11295/) (2023)
- [Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study](https://mlsanity.com/paper/2308.14321/) (2023)
- [RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records](https://mlsanity.com/paper/2403.00815/) (2024)
- [Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds](https://mlsanity.com/paper/2403.06609/) (2024)
- [Health-LLM: Personalized Retrieval-Augmented Disease Prediction System](https://mlsanity.com/paper/2402.00746/) (2024)
- [medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs](https://mlsanity.com/paper/2406.14326/) (2024)
- [PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking](https://mlsanity.com/paper/2405.07500/) (2024)
- [LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction](https://mlsanity.com/paper/2403.15464/) (2024)
- [TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data](https://mlsanity.com/paper/2406.10061/) (2024)

## Built on by (most cited first, 12 of 12)

- [HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare](https://mlsanity.com/paper/2507.19726/) (2025)
- [Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?](https://mlsanity.com/paper/2508.03963/) (2025)
- [Conditional Neural ODE for Longitudinal Parkinson's Disease Progression Forecasting](https://mlsanity.com/paper/2511.04789/) (2025)
- [Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning](https://mlsanity.com/paper/2509.18316/) (2025)
- [Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes](https://mlsanity.com/paper/2511.16548/) (2025)
- [Towards Automatic Evaluation and Selection of PHI De-identification Models via Multi-Agent Collaboration](https://mlsanity.com/paper/2510.16194/) (2025)
- [EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs](https://mlsanity.com/paper/2605.30637/) (2026)
- [EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning](https://mlsanity.com/paper/2601.03471/) (2026)
- [Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training](https://mlsanity.com/paper/2603.13297/) (2026)
- [Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development](https://mlsanity.com/paper/2603.23937/) (2026)
- [LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval](https://mlsanity.com/paper/2604.18913/) (2026)
- [When More Evidence Hurts: Publication-Bias Drift and Principled Stopping for Biomedical Causal Search](https://mlsanity.com/paper/2609.24101/) (2026)

## Abstract (arXiv)

Medical diagnosis prediction plays a critical role in disease detection and personalized healthcare. While machine learning (ML) models have been widely adopted for this task, their reliance on supervised training limits their ability to generalize to unseen cases, particularly given the high cost of acquiring large, labeled datasets. Large language models (LLMs) have shown promise in leveraging language abilities and biomedical knowledge for diagnosis prediction. However, they often suffer from hallucinations, lack structured medical reasoning, and produce useless outputs. To address these challenges, we propose KERAP, a knowledge graph (KG)-enhanced reasoning approach that improves LLM-based diagnosis prediction through a multi-agent architecture. Our framework consists of a linkage agent for attribute mapping, a retrieval agent for structured knowledge extraction, and a prediction agent that iteratively refines diagnosis predictions. Experimental results demonstrate that KERAP enhances diagnostic reliability efficiently, offering a scalable and interpretable solution for zero-shot medical diagnosis prediction.