MOSCARD -- Causal Reasoning and De-confounding for Multimodal Opportunistic Screening of Cardiovascular Adverse Events

• URL: http://arxiv.org/abs/2506.19174v1
• Date: Mon, 23 Jun 2025 22:28:37 GMT
• Title: MOSCARD -- Causal Reasoning and De-confounding for Multimodal Opportunistic Screening of Cardiovascular Adverse Events
• Authors: Jialu Pi, Juan Maria Farina, Rimita Lahiri, Jiwoong Jeong, Archana Gurudu, Hyung-Bok Park, Chieh-Ju Chao, Chadi Ayoub, Reza Arsanjani, Imon Banerjee,
• Abstract summary: Major Adverse Cardiovascular Events (MACE) remain the leading cause of mortality globally, as reported in the Global Disease Study 2021.<n>Opportunistic screening leverages data collected from routine health check-ups and multimodal data can play a key role to identify at-risk individuals.<n>We propose a novel predictive modeling framework - MOSCARD, multimodal causal reasoning with co-attention to align two distinct modalities and simultaneously mitigate bias and confounders in opportunistic risk estimation.
• Score: 3.206697649226124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Major Adverse Cardiovascular Events (MACE) remain the leading cause of mortality globally, as reported in the Global Disease Burden Study 2021. Opportunistic screening leverages data collected from routine health check-ups and multimodal data can play a key role to identify at-risk individuals. Chest X-rays (CXR) provide insights into chronic conditions contributing to major adverse cardiovascular events (MACE), while 12-lead electrocardiogram (ECG) directly assesses cardiac electrical activity and structural abnormalities. Integrating CXR and ECG could offer a more comprehensive risk assessment than conventional models, which rely on clinical scores, computed tomography (CT) measurements, or biomarkers, which may be limited by sampling bias and single modality constraints. We propose a novel predictive modeling framework - MOSCARD, multimodal causal reasoning with co-attention to align two distinct modalities and simultaneously mitigate bias and confounders in opportunistic risk estimation. Primary technical contributions are - (i) multimodal alignment of CXR with ECG guidance; (ii) integration of causal reasoning; (iii) dual back-propagation graph for de-confounding. Evaluated on internal, shift data from emergency department (ED) and external MIMIC datasets, our model outperformed single modality and state-of-the-art foundational models - AUC: 0.75, 0.83, 0.71 respectively. Proposed cost-effective opportunistic screening enables early intervention, improving patient outcomes and reducing disparities.

Related papers

• Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment [10.200639509943443]
  We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment.<n>This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD)<n>The results indicate the potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical for early personalized risk stratification.
  arXiv  Detail & Related papers  (2025-08-04T11:20:31Z)
• Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks [47.19194118883552]
  We introduce a multimodal deep learning framework for survival analysis capable of modeling both single and competing risks scenarios.<n>We propose SAMVAE (Survival Analysis Multimodal Variational Autoencoder), a novel deep learning architecture designed for survival prediction.
  arXiv  Detail & Related papers  (2025-07-10T14:29:48Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis [2.668073128790639]
  We propose CardioCoT, a novel two-stage hierarchical reasoning-enhanced survival analysis framework.<n>In the first stage, we employ an evidence-augmented self-refinement mechanism to guide LLM/VLMs in generating robust hierarchical reasoning trajectories.<n>In the second stage, we integrate the reasoning trajectories with imaging data for risk model training and prediction.
  arXiv  Detail & Related papers  (2025-05-25T15:41:18Z)
• Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors [0.09208007322096533]
  ECG-DiaNet is a deep learning model that integrates electrocardiogram (ECG) features with clinical risk factors (CRFs) to enhance T2DM onset prediction.<n>The model's reliance on non-invasive and widely available ECG signals supports its feasibility in clinical and community health settings.
  arXiv  Detail & Related papers  (2025-04-05T19:59:59Z)
• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [46.56667527672019]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data.<n>Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans [12.652540031719571]
  We propose a novel joint representation that integrates discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker.
  arXiv  Detail & Related papers  (2024-10-24T10:06:45Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care [0.9503773054285559]
  We present a dataset and benchmarking protocol designed to advance multimodal decision support in emergency care.<n>Our models utilize demographics, biometrics, vital signs, laboratory values, and electrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses and patient deterioration.
  arXiv  Detail & Related papers  (2024-07-25T08:21:46Z)
• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records [18.87817671852005]
  We present a supervised topic model called MixEHR-SurG to simultaneously integrate heterogeneous EHR data and model survival hazard. This leads to a highly interpretable survival topic model that can infer PheCode-specific phenotype topics associated with patient mortality.
  arXiv  Detail & Related papers  (2023-12-20T22:13:45Z)
• Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients [42.09584755334577]
  Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
  arXiv  Detail & Related papers  (2023-03-09T15:38:16Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)
• Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent units [51.14334174570822]
  We propose a multi-task recurrent neural network with attention mechanism for predicting cardiovascular events from electronic health records. The proposed approach is compared to a standard clinical risk predictor (QRISK) and machine learning alternatives using 5-year data from a NHS Foundation Trust.
  arXiv  Detail & Related papers  (2020-07-16T17:43:13Z)

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.