arXiv:2607. 06133v1 Announce Type: cross Abstract: Modern software systems increasingly depend on data for analysis, prediction, testing, and decision-making.
arXiv:2607. 09404v1 Announce Type: new Abstract: Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants.
arXiv:2609.23780v1 Announce Type: new Abstract: Many real-world decisions require prioritizing high-risk cases, such as clinicians prioritizing high-risk patients before lower-risk ones. Falling rule...
arXiv:2606. 06990v1 Announce Type: new Abstract: The generation of high-fidelity synthetic Electronic Health Records (EHR) is crucial for advancing medical research while preserving patient privacy.
The study evaluates whether synthetic data can replicate real epidemiological findings using a new method called adversarial random forests (ARF). Six epidemiological analyses—covering topics such as blood pressure, myocardial infarction, and diabetes—were replicated on ARF-generated synthetic data from three large cohorts, and the results consistently matched the original studies. The authors also show that ARF outperforms other tabular synthesizers in terms of utility, privacy, generalization, and computational efficiency, especially when dimensionality and variable complexity are reduced.
arXiv:2606. 08903v1 Announce Type: new Abstract: Synthetic healthcare data are widely proposed as privacy-preserving substitutes for real patient data, yet their evaluation remains dominated by statistical similarity and predictive performance that do not reflect clinical validity.
arXiv:2606. 00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models.
Synthetic healthcare data are widely proposed as privacy-preserving substitutes for real patient data, yet their evaluation remains dominated by statistical similarity and predictive performance that do not reflect clinical validity. We introduce a multi-dimensional evaluation framework grounded in epidemiology, assessing descriptive fidelity, clinical utility, and structural validity, corresponding to descriptive, predictive, and causal questions.
The paper presents a scalable method for meta‑learning interpretable decision trees by generating synthetic pre‑training data. It samples near‑optimal trees to create large, realistic datasets and uses the MetaTree transformer architecture to match the performance of real‑world pre‑training or costly optimal tree training. This approach cuts computational costs, offers flexible data generation, and supports efficient, scalable meta‑learning of decision trees.
arXiv:2608. 06265v1 Announce Type: new Abstract: Synthetic clinical benchmarks for enterprise AI agents can pass existing utility checks and still remain structurally unrealistic, especially in privacy-sensitive healthcare settings where operational data are hard to access.
arXiv:2605. 17758v2 Announce Type: replace Abstract: Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information.
arXiv:2607. 19524v1 Announce Type: cross Abstract: Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted data sharing, client heterogeneity, class imbalance, and the lack of realistic tabular electronic health record (EHR) benchmarks.