# Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

arXiv: https://arxiv.org/abs/1810.04793 · HTML: https://mlsanity.com/paper/1810.04793/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2018.

## Built on by (most cited first, 30 of 30)

- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks](https://mlsanity.com/paper/2111.08585/) (2021)
- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Multi-Label Clinical Time-Series Generation via Conditional GAN](https://mlsanity.com/paper/2204.04797/) (2022)
- [From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?](https://mlsanity.com/paper/2403.11894/) (2024)
- [SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction](https://mlsanity.com/paper/2405.09039/) (2024)
- [Challenges for Reinforcement Learning in Healthcare](https://mlsanity.com/paper/2103.05612/) (2021)
- [Clinical Risk Prediction Using Language Models: Benefits And Considerations](https://mlsanity.com/paper/2312.03742/) (2023)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations](https://mlsanity.com/paper/2207.01873/) (2022)
- [Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder](https://mlsanity.com/paper/2105.09428/) (2021)
- [Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes](https://mlsanity.com/paper/2111.06152/) (2021)
- [Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data](https://mlsanity.com/paper/2103.00482/) (2021)
- [Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning](https://mlsanity.com/paper/2104.01787/) (2021)
- [Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction](https://mlsanity.com/paper/2110.04598/) (2021)
- [Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift](https://mlsanity.com/paper/2507.08617/) (2025)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks](https://mlsanity.com/paper/2112.07239/) (2021)
- [Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data](https://mlsanity.com/paper/2207.11382/) (2022)
- [Personalized Event Prediction for Electronic Health Records](https://mlsanity.com/paper/2308.11013/) (2023)
- [LLM-based Content Classification Approach for GitHub Repositories by the README Files](https://mlsanity.com/paper/2507.21899/) (2025)
- [A Study into patient similarity through representation learning from medical records](https://mlsanity.com/paper/2104.14229/) (2021)
- [Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?](https://mlsanity.com/paper/2303.06002/) (2023)
- [Machine Learning for Administrative Health Records: A Systematic Review of Techniques and Applications](https://mlsanity.com/paper/2308.14216/) (2023)
- [Efficient Observation Time Window Segmentation for Administrative Data Machine Learning](https://mlsanity.com/paper/2401.16537/) (2024)
- [Static and multivariate-temporal attentive fusion transformer for readmission risk prediction](https://mlsanity.com/paper/2407.11096/) (2024)
- [Towards Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects](https://mlsanity.com/paper/2502.04899/) (2025)

## Abstract (arXiv)

The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient health histories in tasks such as understanding disease correlations and predicting health outcomes. However, the heterogeneity, sparsity, noise, and bias in this data present many complex challenges. This complexity makes it difficult to translate potentially relevant information into machine learning algorithms. In this paper, we propose a computational framework, Patient2Vec, to learn an interpretable deep representation of longitudinal EHR data which is personalized for each patient. To evaluate this approach, we apply it to the prediction of future hospitalizations using real EHR data and compare its predictive performance with baseline methods. Patient2Vec produces a vector space with meaningful structure and it achieves an AUC around 0.799 outperforming baseline methods. In the end, the learned feature importance can be visualized and interpreted at both the individual and population levels to bring clinical insights.