# Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models

arXiv: https://arxiv.org/abs/2211.07126 · HTML: https://mlsanity.com/paper/2211.07126/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 66 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [Asking and Answering Questions to Evaluate the Factual Consistency of Summaries](https://mlsanity.com/paper/2004.04228/) (2020)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://mlsanity.com/paper/1907.12461/) (2019)
- [A Survey of Evaluation Metrics Used for NLG Systems](https://mlsanity.com/paper/2008.12009/) (2020)
- [GSum: A General Framework for Guided Neural Abstractive Summarization](https://mlsanity.com/paper/2010.08014/) (2020)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [Question Answering as an Automatic Evaluation Metric for News Article Summarization](https://mlsanity.com/paper/1906.00318/) (2019)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [Searching for Effective Neural Extractive Summarization: What Works and What's Next](https://mlsanity.com/paper/1907.03491/) (2019)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [The Summary Loop: Learning to Write Abstractive Summaries Without Examples](https://mlsanity.com/paper/2105.05361/) (2021)
- [Reducing Quantity Hallucinations in Abstractive Summarization](https://mlsanity.com/paper/2009.13312/) (2020)
- [Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset](https://mlsanity.com/paper/2006.07332/) (2020)
- [Estimating Redundancy in Clinical Text](https://mlsanity.com/paper/2105.11832/) (2021)
- [MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation](https://mlsanity.com/paper/1907.07322/) (2019)

## Built on by (most cited first, 18 of 18)

- [PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records](https://mlsanity.com/paper/2307.02006/) (2023)
- [A dataset and benchmark for hospital course summarization with adapted large language models](https://mlsanity.com/paper/2403.05720/) (2024)
- [BioMamba: Domain-Adaptive Biomedical Language Models](https://mlsanity.com/paper/2408.02600/) (2024)
- [WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV](https://mlsanity.com/paper/2405.11255/) (2024)
- [SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded Entity Retrieval](https://mlsanity.com/paper/2401.02369/) (2024)
- [UF-HOBI at "Discharge Me!": A Hybrid Solution for Discharge Summary Generation Through Prompt-based Tuning of GatorTronGPT Models](https://mlsanity.com/paper/2407.15359/) (2024)
- [Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models](https://mlsanity.com/paper/2504.19061/) (2025)
- [Neural Summarization of Electronic Health Records](https://mlsanity.com/paper/2305.15222/) (2023)
- [Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade](https://mlsanity.com/paper/2311.07609/) (2023)
- [Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation](https://mlsanity.com/paper/2401.01469/) (2024)
- [Domain-specific Guided Summarization for Mental Health Posts](https://mlsanity.com/paper/2411.01485/) (2024)
- [MedHal: a Synthetic Dataset for Medical Hallucination Detection](https://mlsanity.com/paper/2504.08596/) (2025)
- [Large Language Model-Based Generation of Discharge Summaries](https://mlsanity.com/paper/2512.06812/) (2025)
- [Towards Conditioning Clinical Text Generation for User Control](https://mlsanity.com/paper/2502.17571/) (2025)
- [Query-Guided Self-Supervised Summarization of Nursing Notes](https://mlsanity.com/paper/2407.04125/) (2024)
- [Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital](https://mlsanity.com/paper/2604.19774/) (2026)
- [Vectors Are Not Neutral: Sensitive-Information Inference from Exported LLM Representations in Summarization](https://mlsanity.com/paper/2605.26433/) (2026)
- [Hidden Data Challenges in Clinical Machine Learning](https://mlsanity.com/paper/2610.13350/) (2026)

## Abstract (arXiv)

Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets.