# Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records

arXiv: https://arxiv.org/abs/2503.06096 · HTML: https://mlsanity.com/paper/2503.06096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 109 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression](https://mlsanity.com/paper/2202.09101/) (2022)
- [The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.06369/) (2022)
- [SurvivalGAN: Generating Time-to-Event Data for Survival Analysis](https://mlsanity.com/paper/2302.12749/) (2023)
- [Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV](https://mlsanity.com/paper/2208.08655/) (2022)
- [Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models](https://mlsanity.com/paper/2303.12281/) (2023)
- [Synthetic Data: Revisiting the Privacy-Utility Trade-off](https://mlsanity.com/paper/2407.07926/) (2024)
- [Understanding The Effectiveness of Lossy Compression in Machine Learning Training Sets](https://mlsanity.com/paper/2403.15953/) (2024)
- [Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach](https://mlsanity.com/paper/2011.14032/) (2020)
- [CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare](https://mlsanity.com/paper/2410.16872/) (2024)
- [Improving Prediction-Based Lossy Compression Dramatically via Ratio-Quality Modeling](https://mlsanity.com/paper/2111.09815/) (2021)
- [Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation](https://mlsanity.com/paper/2410.16811/) (2024)
- [Explainable Machine Learning System for Predicting Chronic Kidney Disease in High-Risk Cardiovascular Patients](https://mlsanity.com/paper/2404.11148/) (2024)

## Built on by (most cited first, 1 of 1)

- [Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records](https://mlsanity.com/paper/2606.08903/) (2026)

## Abstract (arXiv)

Access to real-world healthcare data is limited by stringent privacy regulations and data imbalances, hindering advancements in research and clinical applications. Synthetic data presents a promising solution, yet existing methods often fail to ensure the realism, utility, and calibration essential for robust survival analysis. Here, we introduce Masked Clinical Modelling (MCM), an attention-based framework capable of generating high-fidelity synthetic datasets that preserve critical clinical insights, such as hazard ratios, while enhancing survival model calibration. Unlike traditional statistical methods like SMOTE and machine learning models such as VAEs, MCM supports both standalone dataset synthesis for reproducibility and conditional simulation for targeted augmentation, addressing diverse research needs. Validated on a chronic kidney disease electronic health records dataset, MCM reduced the general calibration loss over the entire dataset by 15%; and MCM reduced a mean calibration loss by 9% across 10 clinically stratified subgroups, outperforming 15 alternative methods. By bridging data accessibility with translational utility, MCM advances the precision of healthcare models, promoting more efficient use of scarce healthcare resources.