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.

翻译：简要病程摘要（Brief Hospital Course, BHC）是对患者整个住院过程的简明总结，嵌入在出院小结中，由负责患者整体医疗的高级临床医生撰写。自动从住院文档中生成此类摘要的方法，将极大地减轻临床医生在高压环境下（如患者入院和出院时）手动整理文档的负担。从住院病程中自动生成摘要是一项复杂的多文档摘要任务，因为原始笔记在住院期间从不同视角（如护理、医生、放射科）撰写而成。我们展示了多种用于BHC摘要的方法，验证了深度学习摘要模型在抽取式与生成式摘要场景中的性能。此外，我们测试了一种新型集成抽取式与生成式摘要模型，该模型融合了医学概念本体（SNOMED）作为临床引导信号，并在两个真实临床数据集中展现出优越性能。