# Extractive Summarization of EHR Discharge Notes

arXiv: https://arxiv.org/abs/1810.12085 · HTML: https://mlsanity.com/paper/1810.12085/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation](https://mlsanity.com/paper/2204.00447/) (2022)
- [Learning to Revise References for Faithful Summarization](https://mlsanity.com/paper/2204.10290/) (2022)
- [Attention-based Clinical Note Summarization](https://mlsanity.com/paper/2104.08942/) (2021)
- [A Method to Automate the Discharge Summary Hospital Course for Neurology Patients](https://mlsanity.com/paper/2305.06416/) (2023)
- [Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes](https://mlsanity.com/paper/2308.08494/) (2023)
- [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)
- [Neural Summarization of Electronic Health Records](https://mlsanity.com/paper/2305.15222/) (2023)
- [Onco-Retriever: Generative Classifier for Retrieval of EHR Records in Oncology](https://mlsanity.com/paper/2404.06680/) (2024)
- [Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?](https://mlsanity.com/paper/2303.06002/) (2023)

## Abstract (arXiv)

Patient summarization is essential for clinicians to provide coordinated care and practice effective communication. Automated summarization has the potential to save time, standardize notes, aid clinical decision making, and reduce medical errors. Here we provide an upper bound on extractive summarization of discharge notes and develop an LSTM model to sequentially label topics of history of present illness notes. We achieve an F1 score of 0.876, which indicates that this model can be employed to create a dataset for evaluation of extractive summarization methods.