A Hybrid Enumeration Framework for Optimal Counterfactual Generation in Post-Acute COVID-19 Heart Failure

• URL: http://arxiv.org/abs/2510.18841v1
• Date: Tue, 21 Oct 2025 17:35:12 GMT
• Title: A Hybrid Enumeration Framework for Optimal Counterfactual Generation in Post-Acute COVID-19 Heart Failure
• Authors: Jingya Cheng, Alaleh Azhir, Jiazi Tian, Hossein Estiri,
• Abstract summary: We present a counterfactual inference framework for individualized risk estimation and intervention analysis.<n>The framework combines exact enumeration with optimization-based methods, including the Nearest Instance Counterfactual Explanations (NICE) and Multi-Objective Counterfactuals (MOC) algorithms.
• Score: 1.9794615806637272
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Counterfactual inference provides a mathematical framework for reasoning about hypothetical outcomes under alternative interventions, bridging causal reasoning and predictive modeling. We present a counterfactual inference framework for individualized risk estimation and intervention analysis, illustrated through a clinical application to post-acute sequelae of COVID-19 (PASC) among patients with pre-existing heart failure (HF). Using longitudinal diagnosis, laboratory, and medication data from a large health-system cohort, we integrate regularized predictive modeling with counterfactual search to identify actionable pathways to PASC-related HF hospital admissions. The framework combines exact enumeration with optimization-based methods, including the Nearest Instance Counterfactual Explanations (NICE) and Multi-Objective Counterfactuals (MOC) algorithms, to efficiently explore high-dimensional intervention spaces. Applied to more than 2700 individuals with confirmed SARS-CoV-2 infection and prior HF, the model achieved strong discriminative performance (AUROC: 0.88, 95% CI: 0.84-0.91) and generated interpretable, patient-specific counterfactuals that quantify how modifying comorbidity patterns or treatment factors could alter predicted outcomes. This work demonstrates how counterfactual reasoning can be formalized as an optimization problem over predictive functions, offering a rigorous, interpretable, and computationally efficient approach to personalized inference in complex biomedical systems.

Related papers

• MedAD-R1: Eliciting Consistent Reasoning in Interpretible Medical Anomaly Detection via Consistency-Reinforced Policy Optimization [46.65200216642429]
  We introduce MedAD-38K, the first large-scale, multi-modal, and multi-center benchmark for MedAD featuring diagnostic Chain-of-Thought (CoT) annotations alongside structured Visual Question-Answering (VQA) pairs.<n>Our proposed model, MedAD-R1, achieves state-of-the-art (SOTA) performance on the MedAD-38K benchmark, outperforming strong baselines by more than 10%.
  arXiv  Detail & Related papers  (2026-02-01T07:56:10Z)
• Case Prompting to Mitigate Large Language Model Bias for ICU Mortality Prediction [17.91443453604627]
  Large language models (LLMs) show promise in predicting outcomes from structured medical data.<n>LLMs may exhibit demographic biases related to sex, age, and race, limiting their trustworthy use in clinical practice.<n>We propose a training-free, clinically adaptive prompting framework to simultaneously improve fairness and performance.
  arXiv  Detail & Related papers  (2025-12-17T12:29:53Z)
• Counterfactual Probabilistic Diffusion with Expert Models [44.96279296893773]
  We propose a time series diffusion-based framework that incorporates guidance from imperfect expert models.<n>Our method, ODE-Diff, bridges mechanistic and data-driven approaches, enabling more reliable and interpretable causal inference.
  arXiv  Detail & Related papers  (2025-08-18T20:44:32Z)
• 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)
• 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)
• Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models [8.798959872821962]
  This paper outlines an approach in the domain of federated survival analysis, specifically the Cox Proportional Hazards (CoxPH) model. We present an FL approach that employs feature-based clustering to enhance model accuracy across synthetic datasets and real-world applications.
  arXiv  Detail & Related papers  (2024-07-20T18:34:20Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of Right Ventricular Volume [0.5492530316344587]
  The right ventricular (RV) function deterioration strongly predicts clinical outcomes in numerous circumstances. We propose to complement the volume predictions with uncertainty scores. The proposed framework can be used to enhance the decision-making process and reduce risks.
  arXiv  Detail & Related papers  (2024-03-04T12:36:31Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction [15.06049250330114]
  The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making.
  arXiv  Detail & Related papers  (2023-08-24T05:26:56Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Bayesian prognostic covariate adjustment [59.75318183140857]
  Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates.
  arXiv  Detail & Related papers  (2020-12-24T05:19:03Z)

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.