# ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling

arXiv: https://arxiv.org/abs/2504.07373 · HTML: https://mlsanity.com/paper/2504.07373/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Should You Mask 15% in Masked Language Modeling?](https://mlsanity.com/paper/2202.08005/) (2022)
- [Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data](https://mlsanity.com/paper/2001.05295/) (2020)
- [CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks](https://mlsanity.com/paper/2111.08585/) (2021)
- [Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records](https://mlsanity.com/paper/2106.11360/) (2021)
- [Large Language Models are Powerful Electronic Health Record Encoders](https://mlsanity.com/paper/2502.17403/) (2025)
- [Clinical ModernBERT: An efficient and long context encoder for biomedical text](https://mlsanity.com/paper/2504.03964/) (2025)
- [TAPER: Time-Aware Patient EHR Representation](https://mlsanity.com/paper/1908.03971/) (2019)
- [Emergency Department Decision Support using Clinical Pseudo-notes](https://mlsanity.com/paper/2402.00160/) (2024)
- [Can Large Language Models abstract Medical Coded Language?](https://mlsanity.com/paper/2403.10822/) (2024)
- [Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning](https://mlsanity.com/paper/2406.13846/) (2024)
- [When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?](https://mlsanity.com/paper/2408.11854/) (2024)
- [Predictive Modeling with Temporal Graphical Representation on Electronic Health Records](https://mlsanity.com/paper/2405.03943/) (2024)

## Built on by (most cited first, 12 of 12)

- [A multimodal and temporal foundation model for virtual patient representations at healthcare system scale](https://mlsanity.com/paper/2604.18570/) (2026)
- [Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data](https://mlsanity.com/paper/2511.04158/) (2025)
- [Learning Clinical Representations Under Systematic Distribution Shift](https://mlsanity.com/paper/2603.07348/) (2026)
- [Discriminative Representation Learning for Clinical Prediction](https://mlsanity.com/paper/2603.20921/) (2026)
- [Learning Longitudinal Health Representations from EHR and Wearable Data](https://mlsanity.com/paper/2601.12227/) (2026)
- [Uncertainty-Aware Foundation Models for Clinical Data](https://mlsanity.com/paper/2604.04175/) (2026)
- [A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation](https://mlsanity.com/paper/2604.21623/) (2026)
- [Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning](https://mlsanity.com/paper/2606.24604/) (2026)
- [Event Fields: Learning Latent Event Structure for Waveform Foundation Models](https://mlsanity.com/paper/2605.08685/) (2026)
- [WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records](https://mlsanity.com/paper/2605.09765/) (2026)
- [AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models](https://mlsanity.com/paper/2605.17765/) (2026)
- [MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms](https://mlsanity.com/paper/2607.09749/) (2026)

## Abstract (arXiv)

The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFormer, an innovative transformer based architecture specifically designed to encode and leverage temporal dependencies in longitudinal patient data. ChronoFormer integrates temporal embeddings, hierarchical attention mechanisms, and domain specific masking techniques. Extensive experiments conducted on three benchmark tasks mortality prediction, readmission prediction, and long term comorbidity onset demonstrate substantial improvements over current state of the art methods. Furthermore, detailed analyses of attention patterns underscore ChronoFormer's capability to capture clinically meaningful long range temporal relationships.