论文标题
出院摘要医院课程摘要患者电子健康记录文本具有临床概念的指导性深度训练的变压器模型
Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models
论文作者
论文摘要
简短的医院课程（BHC）摘要是整个医院相遇的简洁摘要，该摘要嵌入了出院摘要中，由负责患者整体护理的高级临床医生撰写。从住院文件中自动产生摘要的方法对于减少临床医生手动负担的汇总，在较高的时间压力下汇总文档以接纳和出院患者。从住院课程中自动产生这些摘要，是一项复杂的多文章摘要任务，因为在住院期间，源注释是从各种角度（例如护理，医生，放射学）编写的。我们展示了一系列用于BHC摘要的方法，证明了跨提取性和抽象摘要方案的深度学习摘要模型的性能。我们还测试了一种新型的集合提取性和抽象性摘要模型，该模型将医学概念本体（SNOMED）作为临床指导信号，并在2个现实世界中的临床数据集中显示出卓越的性能。
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.
