# Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study

arXiv: https://arxiv.org/abs/2308.14321 · HTML: https://mlsanity.com/paper/2308.14321/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 17 of 17)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [Demystifying Prompts in Language Models via Perplexity Estimation](https://mlsanity.com/paper/2212.04037/) (2022)
- [Deep Bidirectional Language-Knowledge Graph Pretraining](https://mlsanity.com/paper/2210.09338/) (2022)
- [Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering](https://mlsanity.com/paper/2202.13296/) (2022)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2208.08408/) (2022)
- [Integrating Graph Contextualized Knowledge into Pre-trained Language Models](https://mlsanity.com/paper/1912.00147/) (2019)
- [Attention-based Clinical Note Summarization](https://mlsanity.com/paper/2104.08942/) (2021)
- [Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes](https://mlsanity.com/paper/2306.05270/) (2023)
- [CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models](https://mlsanity.com/paper/2306.05317/) (2023)
- [PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models](https://mlsanity.com/paper/2306.02754/) (2023)
- [Biomedical Event Extraction with Hierarchical Knowledge Graphs](https://mlsanity.com/paper/2009.09335/) (2020)

## Built on by (most cited first, 30 of 30)

- [Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds](https://mlsanity.com/paper/2403.06609/) (2024)
- [Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions](https://mlsanity.com/paper/2405.03205/) (2024)
- [DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models](https://mlsanity.com/paper/2408.01933/) (2024)
- [KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs](https://mlsanity.com/paper/2507.02773/) (2025)
- [KnowGuard: Knowledge-Driven Abstention for Multi-Round Clinical Reasoning](https://mlsanity.com/paper/2509.24816/) (2025)
- [Representation learning to advance multi-institutional studies with electronic health record data from US and France](https://mlsanity.com/paper/2502.08547/) (2025)
- [Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs](https://mlsanity.com/paper/2604.08016/) (2026)
- [MeDxAgent: Multi-Agent Consultation for Interactive Medical Diagnosis](https://mlsanity.com/paper/2606.03416/) (2026)
- [Automated Construction of Medical Indicator Knowledge Graphs Using Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2511.13526/) (2025)
- [ClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV](https://mlsanity.com/paper/2605.11143/) (2026)
- [Privately Learning from Graphs with Applications in Fine-tuning Large Language Models](https://mlsanity.com/paper/2410.08299/) (2024)
- [Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond](https://mlsanity.com/paper/2502.20988/) (2025)
- [DKG-LLM : A Framework for Medical Diagnosis and Personalized Treatment Recommendations via Dynamic Knowledge Graph and Large Language Model Integration](https://mlsanity.com/paper/2508.06186/) (2025)
- [Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response](https://mlsanity.com/paper/2510.05196/) (2025)
- [Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking](https://mlsanity.com/paper/2505.01696/) (2025)
- [A Multi-granularity Concept Sparse Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosis](https://mlsanity.com/paper/2507.08529/) (2025)
- [DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits](https://mlsanity.com/paper/2507.14079/) (2025)
- [TCM-5CEval: Extended Deep Evaluation Benchmark for LLM's Comprehensive Clinical Research Competence in Traditional Chinese Medicine](https://mlsanity.com/paper/2511.13169/) (2025)
- [Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes](https://mlsanity.com/paper/2512.08261/) (2025)
- [LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval](https://mlsanity.com/paper/2604.18913/) (2026)
- [VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs](https://mlsanity.com/paper/2604.23356/) (2026)
- [HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care](https://mlsanity.com/paper/2606.05994/) (2026)
- [Semantic Reasoning in Medicine: The Role of Knowledge Graphs Across Five Key Domains](https://mlsanity.com/paper/2606.15155/) (2026)
- [GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis](https://mlsanity.com/paper/2607.15280/) (2026)
- [Surgical Alignment in Knowledge Graph Training for Clinical Diagnosis with Large Language Models](https://mlsanity.com/paper/2608.26587/) (2026)
- [Propagating construction-time knowledge quality into medical question answering: A framework grounded in clinical guidelines](https://mlsanity.com/paper/2608.28360/) (2026)
- [Knowledge Graph-Augmented Ambient AI for Clinical Note Generation](https://mlsanity.com/paper/2609.22239/) (2026)
- [Improving Medical Calculation of LLMs with Embedded Coding](https://mlsanity.com/paper/2609.31908/) (2026)
- [Training-Free Clinical Reasoning through Medical Ontologies and Cognitive Mapping: A Symbolic-Probabilistic Knowledge Graph Framework](https://mlsanity.com/paper/2609.35298/) (2026)
- [Aligning Multimodal Patient Evidence with Biomedical Knowledge Graphs for Clinical LLMs](https://mlsanity.com/paper/2610.06685/) (2026)

## Abstract (arXiv)

Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives overload healthcare providers, risking diagnostic inaccuracies. While Large Language Models (LLMs) have showcased their potential in diverse language tasks, their application in the healthcare arena needs to ensure the minimization of diagnostic errors and the prevention of patient harm. In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr.Knows, inspired by the clinical diagnostic reasoning process. We derive the KG from the National Library of Medicine's Unified Medical Language System (UMLS), a robust repository of biomedical knowledge. Our method negates the need for pre-training and instead leverages the KG as an auxiliary instrument aiding in the interpretation and summarization of complex medical concepts. Using real-world hospital datasets, our experimental results demonstrate that the proposed approach of combining LLMs with KG has the potential to improve the accuracy of automated diagnosis generation. More importantly, our approach offers an explainable diagnostic pathway, edging us closer to the realization of AI-augmented diagnostic decision support systems.