Article

Generative AI-Based Imputation to Preserve Data Fidelity and Enhance Outcome Prediction: A Multi-Institutional Study in Cardiac Surgery

2026-01-23

Abstract excerpt

<title>Abstract</title> <p>Missing data in large-scale perioperative datasets can impair the accuracy, fairness, and transportability of predictive models in cardiac surgery, and no single imputation approach is uniformly optimal for heterogeneous clinical data. We present a multi-institutional benchmarking framework that jointly evaluates imputation fidelity and downstream clinical utility across classical metho...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 061c944f-ebc5-5150-87d2-35e529ae2ca2
DOI: 10.21203/rs.3.rs-8303629/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Generative AI-Based Imputation to Preserve Data Fidelity and Enhance Outcome Prediction: A Multi-Institutional Study in Cardiac SurgeryDOI 10.21203/rs.3.rs-8303629/v1Read

Stratified Covariate-Reconstructed Survival (SCRS): Extending Guyot IPD Reconstruction to Preserve Covariate-Outcome Associations for Population- Adjusted Indirect ComparisonsSemantic proximity Read

A Simulation-Based Validation Framework for Uncertainty-Aware Clinical Triage: Conformal Prediction and Ensemble Learning Applied to a Synthetic ICU Benchmark CohortSemantic proximity Read

Short-term and long-term outcome prediction for patients with coronary artery disease using machine learning and comprehensive multi-center patient dataSemantic proximity Read

Large Language Models forecast Patient Health Trajectories enabling Digital TwinsSemantic proximity Read

Propensity-score matching with GAN-generated observations from electronic health records: simulation study and application to the evaluation of prone positioning in COVID-19 patients under mechanical ventilationSemantic proximity Read

Generative AI Mitigates Representation Bias and Improves Model Fairness Through Synthetic Health DataSemantic proximity Read

Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicineSemantic proximity Read

A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.Semantic proximity Read

Missing Data in OHCA Registries: How Multiple Imputation Methods Affect Research Conclusions—Paper IISemantic proximity Read

Demonstrating the Consequences of Learning Missingness Patterns in Early Warning Systems for Preventative Health Care: A Novel Simulation and SolutionSemantic proximity Read

Explainable Clinician-Supervised Artificial Intelligence as an Implementation Framework for Cardiovascular–Kidney–Metabolic Population Health: Synthetic Data Validation of the CHAPERONE-CKM FrameworkSemantic proximity Read

Machine learning in cardiovascular medicine: are we there yet?Semantic proximity Read