# XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications

arXiv: https://arxiv.org/abs/2402.02258 · HTML: https://mlsanity.com/paper/2402.02258/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2024.

## Built on by (most cited first, 6 of 6)

- [The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records](https://mlsanity.com/paper/2603.14003/) (2026)
- [A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation](https://mlsanity.com/paper/2604.21623/) (2026)
- [Chain-of-Influence: Tracing Interdependencies Across Time and Features in Clinical Predictive Modelings](https://mlsanity.com/paper/2510.09895/) (2025)
- [Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling](https://mlsanity.com/paper/2510.17211/) (2025)
- [Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes](https://mlsanity.com/paper/2510.20414/) (2025)
- [DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction](https://mlsanity.com/paper/2606.09140/) (2026)

## Abstract (arXiv)

Adverse clinical events related to unsafe care are among the top ten causes of death in the U.S. Accurate modeling and prediction of clinical events from electronic health records (EHRs) play a crucial role in patient safety enhancement. An example is modeling de facto care pathways that characterize common step-by-step plans for treatment or care. However, clinical event data pose several unique challenges, including the irregularity of time intervals between consecutive events, the existence of cycles, periodicity, multi-scale event interactions, and the high computational costs associated with long event sequences. Existing neural temporal point processes (TPPs) methods do not effectively capture the multi-scale nature of event interactions, which is common in many real-world clinical applications. To address these issues, we propose the cross-temporal-scale transformer (XTSFormer), specifically designed for irregularly timed event data. Our model consists of two vital components: a novel Feature-based Cycle-aware Time Positional Encoding (FCPE) that adeptly captures the cyclical nature of time, and a hierarchical multi-scale temporal attention mechanism, where different temporal scales are determined by a bottom-up clustering approach. Extensive experiments on several real-world EHR datasets show that our XTSFormer outperforms multiple baseline methods. The code is available at https://github.com/spatialdatasciencegroup/XTSFormer.