title: Towards Predicting Temporal Changes in a Patient's Chest X-ray Images
    based on Electronic Health Records
summary: Chest X-ray imaging (CXR) is an important diagnostic tool used in hospitals
  to assess patient conditions and monitor changes over time. Generative models,
  specifically diffusion-based models, have shown promise in generating realistic
  synthetic X-rays. However, these models mainly focus on conditional generation
  using single-time-point data, i.e., typically CXRs taken at a specific time
  with their corresponding reports, limiting their clinical utility, particularly
  for capturing temporal changes. To address this limitation, we propose a novel
  framework, EHRXDiff, which predicts future CXR images by integrating previous
  CXRs with subsequent medical events, e.g., prescriptions, lab measures, etc.
  Our framework dynamically tracks and predicts disease progression based on a
  latent diffusion model, conditioned on the previous CXR image and a history of
  medical events. We comprehensively evaluate the performance of our framework
  across three key aspects, including clinical consistency, demographic
  consistency, and visual realism. We demonstrate that our framework generates
  high-quality, realistic future images that capture potential temporal changes,
  suggesting its potential for further development as a clinical simulation tool.
  This could offer valuable insights for patient monitoring and treatment
  planning in the medical field.
paper_authors._id: 66e3bf06dba1e4fee4b33434
paper_authors.name: Daeun Kyung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T11:33:57.894Z
paper_authors._id: 645cd00f5ebf379fd6d7a4c1
paper_authors.avatarUrl: /avatars/be8375ed32ac234919e26a2450bf9d38.svg
paper_authors.fullname: Daeun Kyung
paper_authors.type: user
paper_authors.user: dek924
paper_authors._id: 66e3bf06dba1e4fee4b33435
paper_authors.name: Junu Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T11:34:03.399Z
paper_authors._id: 64675818a9b4610868a134a5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64675818a9b4610868a134a5/BGCRgg0oP1S2aSHXquZdm.jpeg
paper_authors.fullname: Junu Kim
paper_authors.type: user
paper_authors.user: starmpcc
paper_authors._id: 66e3bf06dba1e4fee4b33436
paper_authors.name: Tackeun Kim
paper_authors._id: 66e3bf06dba1e4fee4b33437
paper_authors.name: Edward Choi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-17T11:34:29.235Z
paper_authors._id: 65e8fb912dcdb39d1934260c
paper_authors.avatarUrl: /avatars/358504b57f2a600753e9bc459091061b.svg
paper_authors.fullname: Edward Choi
paper_authors.type: user
paper_authors.user: forgetnight
paper_title: Towards Predicting Temporal Changes in a Patient's Chest X-ray Images
    based on Electronic Health Records
paper_summary: Chest X-ray imaging (CXR) is an important diagnostic tool used in hospitals
  to assess patient conditions and monitor changes over time. Generative models,
  specifically diffusion-based models, have shown promise in generating realistic
  synthetic X-rays. However, these models mainly focus on conditional generation
  using single-time-point data, i.e., typically CXRs taken at a specific time
  with their corresponding reports, limiting their clinical utility, particularly
  for capturing temporal changes. To address this limitation, we propose a novel
  framework, EHRXDiff, which predicts future CXR images by integrating previous
  CXRs with subsequent medical events, e.g., prescriptions, lab measures, etc.
  Our framework dynamically tracks and predicts disease progression based on a
  latent diffusion model, conditioned on the previous CXR image and a history of
  medical events. We comprehensively evaluate the performance of our framework
  across three key aspects, including clinical consistency, demographic
  consistency, and visual realism. We demonstrate that our framework generates
  high-quality, realistic future images that capture potential temporal changes,
  suggesting its potential for further development as a clinical simulation tool.
  This could offer valuable insights for patient monitoring and treatment
  planning in the medical field.
paper_ai_summary: EHRXDiff uses a latent diffusion model to generate future CXR images by integrating past CXR data and medical events, enhancing clinical utility by capturing temporal disease progression.
paper_ai_keywords: diffusion-based models latent diffusion model EHRXDiff clinical consistency demographic consistency visual realism future CXR images temporal changes patient monitoring treatment planning