# TAPER: Time-Aware Patient EHR Representation

arXiv: https://arxiv.org/abs/1908.03971 · HTML: https://mlsanity.com/paper/1908.03971/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2019.

## Built on by (most cited first, 22 of 22)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs](https://mlsanity.com/paper/2112.05195/) (2021)
- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [Multi-Label Clinical Time-Series Generation via Conditional GAN](https://mlsanity.com/paper/2204.04797/) (2022)
- [Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction](https://mlsanity.com/paper/2106.04751/) (2021)
- [Multimodal data matters: language model pre-training over structured and unstructured electronic health records](https://mlsanity.com/paper/2201.10113/) (2022)
- [ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling](https://mlsanity.com/paper/2504.07373/) (2025)
- [Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives](https://mlsanity.com/paper/2111.04898/) (2021)
- [sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion](https://mlsanity.com/paper/2211.17121/) (2022)
- [Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence](https://mlsanity.com/paper/2402.05802/) (2024)
- [Inadequacy of common stochastic neural networks for reliable clinical decision support](https://mlsanity.com/paper/2401.13657/) (2024)
- [SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis](https://mlsanity.com/paper/2511.11935/) (2025)
- [BERT Meets Relational DB: Contextual Representations of Relational Databases](https://mlsanity.com/paper/2104.14914/) (2021)
- [Enriching Unsupervised User Embedding via Medical Concepts](https://mlsanity.com/paper/2203.10627/) (2022)
- [Assessing mortality prediction through different representation models based on concepts extracted from clinical notes](https://mlsanity.com/paper/2207.10872/) (2022)
- [Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section](https://mlsanity.com/paper/2307.07051/) (2023)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [A Study into patient similarity through representation learning from medical records](https://mlsanity.com/paper/2104.14229/) (2021)
- [Applying unsupervised keyphrase methods on concepts extracted from discharge sheets](https://mlsanity.com/paper/2303.08928/) (2023)
- [Application of Transformers based methods in Electronic Medical Records: A Systematic Literature Review](https://mlsanity.com/paper/2304.02768/) (2023)
- [On Preserving the Knowledge of Long Clinical Texts](https://mlsanity.com/paper/2311.01571/) (2023)
- [Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.18270/) (2026)

## Abstract (arXiv)

Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes. They are preempted by medical conditions the patient may have, and are typically jotted down by medical staff. Accompanying codes are notes containing valuable information about patients beyond the structured information contained in electronic health records. We use transformer networks and the recently proposed BERT language model to embed these data streams into a unified vector representation. The presented approach effectively encodes a patient's visit data into a single distributed representation, which can be used for downstream tasks. Our model demonstrates superior performance and generalization on mortality, readmission and length of stay tasks using the publicly available MIMIC-III ICU dataset. Code avaialble at https://github.com/sajaddarabi/TAPER-EHR