# Transformer-based Time-to-Event Prediction for Chronic Kidney Disease Deterioration

arXiv: https://arxiv.org/abs/2306.05779 · HTML: https://mlsanity.com/paper/2306.05779/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep Contextual Clinical Prediction with Reverse Distillation](https://mlsanity.com/paper/2007.05611/) (2020)
- [An explainable Transformer-based deep learning model for the prediction of incident heart failure](https://mlsanity.com/paper/2101.11359/) (2021)
- [TRACE: Early Detection of Chronic Kidney Disease Onset with Transformer-Enhanced Feature Embedding](https://mlsanity.com/paper/2012.03729/) (2020)

## Built on by (most cited first, 4 of 4)

- [ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data](https://mlsanity.com/paper/2605.21963/) (2026)
- [Position: Stop Chasing the C-index when Evaluating Survival Analysis Models](https://mlsanity.com/paper/2506.02075/) (2025)
- [ICU Bloodstream Infection Prediction: A Transformer-Based Approach for EHR Analysis](https://mlsanity.com/paper/2405.00819/) (2024)
- [Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling](https://mlsanity.com/paper/2510.17211/) (2025)

## Abstract (arXiv)

Deep-learning techniques, particularly the transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. While previous methods have mainly focused on fixed-time risk prediction, time-to-event prediction (also known as survival analysis) is often more appropriate for clinical scenarios. Here, we present a novel deep-learning architecture we named STRAFE, a generalizable survival analysis transformer-based architecture for electronic health records. The performance of STRAFE was evaluated using a real-world claim dataset of over 130,000 individuals with stage 3 chronic kidney disease (CKD) and was found to outperform other time-to-event prediction algorithms in predicting the exact time of deterioration to stage 5. Additionally, STRAFE was found to outperform binary outcome algorithms in predicting fixed-time risk, possibly due to its ability to train on censored data. We show that STRAFE predictions can improve the positive predictive value of high-risk patients by 3-fold, demonstrating possible usage to improve targeting for intervention programs. Finally, we suggest a novel visualization approach to predictions on a per-patient basis. In conclusion, STRAFE is a cutting-edge time-to-event prediction algorithm that has the potential to enhance risk predictions in large claims datasets.