EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling

• URL: http://arxiv.org/abs/2406.00036v1
• Date: Mon, 27 May 2024 10:53:15 GMT
• Title: EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling
• Authors: Yinghao Zhu, Changyu Ren, Zixiang Wang, Xiaochen Zheng, Shiyun Xie, Junlan Feng, Xi Zhu, Zhoujun Li, Liantao Ma, Chengwei Pan,
• Abstract summary: EMERGE is a Retrieval-Augmented Generation driven framework aimed at enhancing multimodal EHR predictive modeling. Our approach extracts entities from both time-series data and clinical notes by prompting Large Language Models. The extracted knowledge is then used to generate task-relevant summaries of patients' health statuses.
• Score: 22.94521527609479
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of multimodal Electronic Health Records (EHR) data has notably advanced clinical predictive capabilities. However, current models that utilize clinical notes and multivariate time-series EHR data often lack the necessary medical context for precise clinical tasks. Previous methods using knowledge graphs (KGs) primarily focus on structured knowledge extraction. To address this, we propose EMERGE, a Retrieval-Augmented Generation (RAG) driven framework aimed at enhancing multimodal EHR predictive modeling. Our approach extracts entities from both time-series data and clinical notes by prompting Large Language Models (LLMs) and aligns them with professional PrimeKG to ensure consistency. Beyond triplet relationships, we include entities' definitions and descriptions to provide richer semantics. The extracted knowledge is then used to generate task-relevant summaries of patients' health statuses. These summaries are fused with other modalities utilizing an adaptive multimodal fusion network with cross-attention. Extensive experiments on the MIMIC-III and MIMIC-IV datasets for in-hospital mortality and 30-day readmission tasks demonstrate the superior performance of the EMERGE framework compared to baseline models. Comprehensive ablation studies and analyses underscore the efficacy of each designed module and the framework's robustness to data sparsity. EMERGE significantly enhances the use of multimodal EHR data in healthcare, bridging the gap with nuanced medical contexts crucial for informed clinical predictions.

Related papers

• Continually Evolved Multimodal Foundation Models for Cancer Prognosis [50.43145292874533]
  Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. Previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. Existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals. Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities.
  arXiv  Detail & Related papers  (2025-01-30T06:49:57Z)
• HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation [89.3260120072177]
  We propose a novel Historical-Constrained Large Language Models (HC-LLM) framework for Radiology report generation. Our approach extracts both time-shared and time-specific features from longitudinal chest X-rays and diagnostic reports to capture disease progression. Notably, our approach performs well even without historical data during testing and can be easily adapted to other multimodal large models.
  arXiv  Detail & Related papers  (2024-12-15T06:04:16Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM [39.25272553560425]
  We propose a new framework called MINGLE, which integrates both structures and semantics in EHR effectively. Our framework uses a two-level infusion strategy to combine medical concept semantics and clinical note semantics into hypergraph neural networks. Experiment results on two EHR datasets, the public MIMIC-III and private CRADLE, show that MINGLE can effectively improve predictive performance by 11.83% relatively.
  arXiv  Detail & Related papers  (2024-02-19T23:48:40Z)
• REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models [19.62552013839689]
  Existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge. We propose REALM, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR representations. Our experiments on MIMIC-III mortality and readmission tasks showcase the superior performance of our REALM framework over baselines.
  arXiv  Detail & Related papers  (2024-02-10T18:27:28Z)
• Multimodal Interpretable Data-Driven Models for Early Prediction of Antimicrobial Multidrug Resistance Using Multivariate Time-Series [6.804748007823268]
  We present an approach built on a collection of interpretable multimodal data-driven models that may anticipate and understand the emergence of antimicrobial multidrug resistance (AMR) germs in the intensive care unit (ICU) of the University Hospital of Fuenlabrada (Madrid, Spain) The profile and initial health status of the patient are modeled using static variables, while the evolution of the patient's health status during the ICU stay is modeled using several MTS, including mechanical ventilation and antibiotics intake.
  arXiv  Detail & Related papers  (2024-02-09T10:16:58Z)
• Next Visit Diagnosis Prediction via Medical Code-Centric Multimodal Contrastive EHR Modelling with Hierarchical Regularisation [0.0]
  We propose NECHO, a novel medical code-centric multimodal contrastive EHR learning framework with hierarchical regularisation. First, we integrate multifaceted information encompassing medical codes, demographics, and clinical notes using a tailored network design. We also regularise modality-specific encoders using a parental level information in medical ontology to learn hierarchical structure of EHR data.
  arXiv  Detail & Related papers  (2024-01-22T01:58:32Z)
• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis [19.32686665459374]
  We introduce INSPECT, which contains de-identified longitudinal records from a large cohort of patients at risk for pulmonary embolism (PE) INSPECT contains data from 19,402 patients, including CT images, radiology report impression sections, and structured electronic health record (EHR) data (i.e. demographics, diagnoses, procedures, vitals, and medications)
  arXiv  Detail & Related papers  (2023-11-17T07:28:16Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.