# Recent Advances in Predictive Modeling with Electronic Health Records

arXiv: https://arxiv.org/abs/2402.01077 · HTML: https://mlsanity.com/paper/2402.01077/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context](https://mlsanity.com/paper/1911.12216/) (2019)
- [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)
- [StageNet: Stage-Aware Neural Networks for Health Risk Prediction](https://mlsanity.com/paper/2001.10054/) (2020)
- [RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data](https://mlsanity.com/paper/1807.08820/) (2018)
- [MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records](https://mlsanity.com/paper/2102.02340/) (2021)
- [EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders](https://mlsanity.com/paper/2012.10020/) (2020)
- [How to Leverage Multimodal EHR Data for Better Medical Predictions?](https://mlsanity.com/paper/2110.15763/) (2021)
- [PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning](https://mlsanity.com/paper/2211.01761/) (2022)
- [Hierarchical Pretraining on Multimodal Electronic Health Records](https://mlsanity.com/paper/2310.07871/) (2023)
- [Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions](https://mlsanity.com/paper/2401.11252/) (2024)
- [ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection](https://mlsanity.com/paper/2105.03037/) (2021)

## Built on by (most cited first, 11 of 11)

- [EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.00036/) (2024)
- [MEDMKG: Benchmarking Medical Knowledge Exploitation with Multimodal Knowledge Graph](https://mlsanity.com/paper/2505.17214/) (2025)
- [Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records](https://mlsanity.com/paper/2506.03068/) (2025)
- [PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning](https://mlsanity.com/paper/2601.16414/) (2026)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics](https://mlsanity.com/paper/2602.19661/) (2026)
- [Revealing Interconnections between Diseases: from Statistical Methods to Large Language Models](https://mlsanity.com/paper/2510.04888/) (2025)
- [Ensemble Deep Learning Models for Early Detection of Meningitis in ICU: Multi-center Study](https://mlsanity.com/paper/2510.15218/) (2025)
- [Early GVHD Prediction in Liver Transplantation via Multi-Modal Deep Learning on Imbalanced EHR Data](https://mlsanity.com/paper/2511.11623/) (2025)
- [A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study](https://mlsanity.com/paper/2603.24828/) (2026)
- [ATHENA: Knowledge-guided agentic neural architecture search for AutoFormer-based electronic health record modeling](https://mlsanity.com/paper/2608.21712/) (2026)

## Abstract (arXiv)

The development of electronic health records (EHR) systems has enabled the collection of a vast amount of digitized patient data. However, utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. With the advancements in machine learning techniques, deep learning has demonstrated its superiority in various applications, including healthcare. This survey systematically reviews recent advances in deep learning-based predictive models using EHR data. Specifically, we begin by introducing the background of EHR data and providing a mathematical definition of the predictive modeling task. We then categorize and summarize predictive deep models from multiple perspectives. Furthermore, we present benchmarks and toolkits relevant to predictive modeling in healthcare. Finally, we conclude this survey by discussing open challenges and suggesting promising directions for future research.