REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models
<|reference_start|>REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models: The integration of multimodal Electronic Health Records (EHR) data has significantly improved clinical predictive capabilities. Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG). Previous approaches with KG knowledge have primarily focused on structured knowledge extraction, neglecting unstructured data modalities and semantic high dimensional medical knowledge. In response, we propose REALM, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR representations that address these limitations. Firstly, we apply Large Language Model (LLM) to encode long context clinical notes and GRU model to encode time-series EHR data. Secondly, we prompt LLM to extract task-relevant medical entities and match entities in professionally labeled external knowledge graph (PrimeKG) with corresponding medical knowledge. By matching and aligning with clinical standards, our framework eliminates hallucinations and ensures consistency. Lastly, we propose an adaptive multimodal fusion network to integrate extracted knowledge with multimodal EHR data. Our extensive experiments on MIMIC-III mortality and readmission tasks showcase the superior performance of our REALM framework over baselines, emphasizing the effectiveness of each module. REALM framework contributes to refining the use of multimodal EHR data in healthcare and bridging the gap with nuanced medical context essential for informed clinical predictions.<|reference_end|>
@article{zhu2024realm:,
    title={REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records
  Analysis via Large Language Models},
    author={Yinghao Zhu, Changyu Ren, Shiyun Xie, Shukai Liu, Hangyuan Ji, Zixiang
  Wang, Tao Sun, Long He, Zhoujun Li, Xi Zhu, Chengwei Pan},
    journal={arXiv preprint arXiv:2402.07016},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07016},
    primaryClass={cs.AI}
}