# Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction

arXiv: https://arxiv.org/abs/1803.04837 · HTML: https://mlsanity.com/paper/1803.04837/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 7 of 7)

- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review](https://mlsanity.com/paper/2306.12834/) (2023)
- [Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem](https://mlsanity.com/paper/2301.01772/) (2023)
- [Time-Aware Attention for Enhanced Electronic Health Records Modeling](https://mlsanity.com/paper/2507.14847/) (2025)
- [The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records](https://mlsanity.com/paper/2603.14003/) (2026)
- [CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction](https://mlsanity.com/paper/2204.13847/) (2022)
- [Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention](https://mlsanity.com/paper/2308.12388/) (2023)

## Abstract (arXiv)

The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.