DR.EHR: Dense Retrieval for Electronic Health Record with Knowledge Injection and Synthetic Data

• URL: http://arxiv.org/abs/2507.18583v1
• Date: Thu, 24 Jul 2025 17:02:46 GMT
• Title: DR.EHR: Dense Retrieval for Electronic Health Record with Knowledge Injection and Synthetic Data
• Authors: Zhengyun Zhao, Huaiyuan Ying, Yue Zhong, Sheng Yu,
• Abstract summary: EHRs are pivotal in clinical practices, yet their retrieval remains a challenge mainly due to semantic gap issues.<n>Recent advancements in dense retrieval offer promising solutions but existing models, both general-domain and biomedical-domain, fall short due to insufficient medical knowledge or mismatched training corpora.<n>This paper introduces textttDR.EHR, a series of dense retrieval models specifically tailored for EHR retrieval.
• Score: 2.9929405444223205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electronic Health Records (EHRs) are pivotal in clinical practices, yet their retrieval remains a challenge mainly due to semantic gap issues. Recent advancements in dense retrieval offer promising solutions but existing models, both general-domain and biomedical-domain, fall short due to insufficient medical knowledge or mismatched training corpora. This paper introduces \texttt{DR.EHR}, a series of dense retrieval models specifically tailored for EHR retrieval. We propose a two-stage training pipeline utilizing MIMIC-IV discharge summaries to address the need for extensive medical knowledge and large-scale training data. The first stage involves medical entity extraction and knowledge injection from a biomedical knowledge graph, while the second stage employs large language models to generate diverse training data. We train two variants of \texttt{DR.EHR}, with 110M and 7B parameters, respectively. Evaluated on the CliniQ benchmark, our models significantly outperforms all existing dense retrievers, achieving state-of-the-art results. Detailed analyses confirm our models' superiority across various match and query types, particularly in challenging semantic matches like implication and abbreviation. Ablation studies validate the effectiveness of each pipeline component, and supplementary experiments on EHR QA datasets demonstrate the models' generalizability on natural language questions, including complex ones with multiple entities. This work significantly advances EHR retrieval, offering a robust solution for clinical applications.

Related papers

• EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation [22.94521527609479]
  EMERGE is a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR predictive modeling.<n>We extract entities from time-series data and clinical notes by prompting Large Language Models (LLMs) and align them with professional PrimeKG.<n>The extracted knowledge is then used to generate task-relevant summaries of patients' health statuses.
  arXiv  Detail & Related papers  (2024-05-27T10:53:15Z)
• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• 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)
• Recent Advances in Predictive Modeling with Electronic Health Records [71.19967863320647]
  utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. Deep learning has demonstrated its superiority in various applications, including healthcare.
  arXiv  Detail & Related papers  (2024-02-02T00:31:01Z)
• 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)
• Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining [121.89793208683625]
  Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks. We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
  arXiv  Detail & Related papers  (2023-04-26T01:26:19Z)
• sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion [0.0]
  sEHR-CE is a novel framework based on transformers to enable integrated phenotyping and analyses of heterogeneous clinical datasets. We validate our approach using primary and secondary care data from the UK Biobank, a large-scale research study.
  arXiv  Detail & Related papers  (2022-11-30T16:00:43Z)
• GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning [9.406539794019581]
  General Healthcare Predictive Framework (GenHPF) is applicable to any EHR with minimal preprocessing for multiple prediction tasks. Our framework significantly outperforms baseline models that utilize domain knowledge in multi-source learning.
  arXiv  Detail & Related papers  (2022-07-20T12:46:26Z)
• Competence-based Multimodal Curriculum Learning for Medical Report Generation [98.10763792453925]
  We propose a Competence-based Multimodal Curriculum Learning framework ( CMCL) to alleviate the data bias and make best use of available data. Specifically, CMCL simulates the learning process of radiologists and optimize the model in a step by step manner. Experiments on the public IU-Xray and MIMIC-CXR datasets show that CMCL can be incorporated into existing models to improve their performance.
  arXiv  Detail & Related papers  (2022-06-24T08:16:01Z)
• 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)
• Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks [3.1580072841682734]
  This study presents the Claim Pre-Training (Claim-PT) framework, a generic pre-training model that first trains on the entire pediatric claims dataset. The effective knowledge transfer is completed through the task-aware fine-tuning stage. We conducted experiments on a real-world claims dataset with more than one million patient records.
  arXiv  Detail & Related papers  (2021-06-24T15:25:41Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)

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.