# Natural Language Generation for Electronic Health Records

arXiv: https://arxiv.org/abs/1806.01353 · HTML: https://mlsanity.com/paper/1806.01353/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2018.

## Built on by (most cited first, 27 of 27)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://mlsanity.com/paper/2304.03086/) (2023)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges](https://mlsanity.com/paper/2407.00116/) (2024)
- [Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes](https://mlsanity.com/paper/2104.13498/) (2021)
- [A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data](https://mlsanity.com/paper/2411.12274/) (2024)
- [Leveraging Summary Guidance on Medical Report Summarization](https://mlsanity.com/paper/2302.04001/) (2023)
- [Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model](https://mlsanity.com/paper/2304.02169/) (2023)
- [TimEHR: Image-based Time Series Generation for Electronic Health Records](https://mlsanity.com/paper/2402.06318/) (2024)
- [SimSUM: Simulated Benchmark with Structured and Unstructured Medical Records](https://mlsanity.com/paper/2409.08936/) (2024)
- [LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training](https://mlsanity.com/paper/2112.01404/) (2021)
- [Autocompletion of Chief Complaints in the Electronic Health Records using Large Language Models](https://mlsanity.com/paper/2401.06088/) (2024)
- [Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer](https://mlsanity.com/paper/2502.20719/) (2025)
- [Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan](https://mlsanity.com/paper/2209.10041/) (2022)
- [Generating Medical Prescriptions with Conditional Transformer](https://mlsanity.com/paper/2310.19727/) (2023)
- [Synthetic4Health: Generating Annotated Synthetic Clinical Letters](https://mlsanity.com/paper/2409.09501/) (2024)
- [Natural Language Generation in Healthcare: A Review of Methods and Applications](https://mlsanity.com/paper/2505.04073/) (2025)
- [Large Language Model-Based Generation of Discharge Summaries](https://mlsanity.com/paper/2512.06812/) (2025)
- [Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data](https://mlsanity.com/paper/2207.05194/) (2022)
- [Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples](https://mlsanity.com/paper/2210.08548/) (2022)
- [MCRAGE: Synthetic Healthcare Data for Fairness](https://mlsanity.com/paper/2310.18430/) (2023)
- [From Natural Language to Simulations: Applying GPT-3 Codex to Automate Simulation Modeling of Logistics Systems](https://mlsanity.com/paper/2202.12107/) (2022)
- [Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?](https://mlsanity.com/paper/2303.06002/) (2023)
- [A Biomedical Entity Extraction Pipeline for Oncology Health Records in Portuguese](https://mlsanity.com/paper/2304.08999/) (2023)
- [AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework](https://mlsanity.com/paper/2406.13947/) (2024)
- [The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data -- a Systematic Review](https://mlsanity.com/paper/2602.06609/) (2026)
- [Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis](https://mlsanity.com/paper/2606.19183/) (2026)

## Abstract (arXiv)

A variety of methods existing for generating synthetic electronic health records (EHRs), but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness or progress notes. Here, we use the encoder-decoder model, a deep learning algorithm that features in many contemporary machine translation systems, to generate synthetic chief complaints from discrete variables in EHRs, like age group, gender, and discharge diagnosis. After being trained end-to-end on authentic records, the model can generate realistic chief complaint text that preserves much of the epidemiological information in the original data. As a side effect of the model's optimization goal, these synthetic chief complaints are also free of relatively uncommon abbreviation and misspellings, and they include none of the personally-identifiable information (PII) that was in the training data, suggesting it may be used to support the de-identification of text in EHRs. When combined with algorithms like generative adversarial networks (GANs), our model could be used to generate fully-synthetic EHRs, facilitating data sharing between healthcare providers and researchers and improving our ability to develop machine learning methods tailored to the information in healthcare data.