MIMIC-IV-Ext-BHC: Labeled Clinical Notes Dataset for Hospital Course Summarization

This dataset presents a curated collection of preprocessed and labeled
clinical notes derived from the MIMIC-IV-Note database. The primary aim of
this resource is to facilitate the development and training of machine
learning models focused on summarizing brief hospital courses (BHC) from
clinical discharge notes.

The dataset contains 270,033 meticulously cleaned and standardized clinical
notes containing an average token length of 2,267, ensuring usability for
machine learning (ML) applications. Each clinical note is paired with a
corresponding BHC summary, providing a robust foundation for supervised
learning tasks. The preprocessing pipeline employed uses regular expressions
to address common issues in the raw clinical text, such as special characters,
extraneous whitespace, inconsistent formatting, and irrelevant text, to
produce a high-quality, structured dataset with separated clinical note
sections through appropriate headings.

By offering this resource, we aim to support healthcare professionals and
researchers in their efforts to enhance patient care through the automation of
BHC summarization. This dataset is ideal for exploring various NLP techniques,
developing predictive models, and improving the efficiency and accuracy of
clinical documentation practices. We invite the research community to utilize
this dataset to advance the field of medical informatics and contribute to
better health outcomes.

本数据集为源自MIMIC-IV-Note数据库的经预处理与标注的精选临床笔记合集。本资源的核心目标是助力开发与训练聚焦于从临床出院笔记中生成住院简要病程（Brief Hospital Courses, BHC）摘要的机器学习模型。

本数据集包含270033份经过精细清理与标准化处理的临床笔记，平均Token长度为2267，可满足机器学习（Machine Learning, ML）应用的使用需求。每份临床笔记均配有对应的BHC摘要，为监督学习任务提供了坚实的基础支撑。本次采用的预处理流水线通过正则表达式处理原始临床文本中的常见问题，包括特殊字符、多余空格、格式不一致及无关文本等，最终生成高质量结构化数据集，通过规范标题实现临床笔记各章节的清晰分离。

本数据集的发布旨在助力医疗从业者与研究者通过BHC摘要自动化技术提升患者照护水平。本数据集非常适合用于探索各类自然语言处理（Natural Language Processing, NLP）技术、开发预测模型，以及提升临床文档记录工作的效率与准确性。我们诚挚邀请学术界使用本数据集，以推动医学信息学领域的发展，并助力实现更优质的健康结局。

搜集汇总

背景与挑战

背景概述

MIMIC-IV-Ext-BHC 是一个专门用于医院病程总结的标记临床笔记数据集，包含270,033条经过清洗和标准化的临床笔记，每条笔记都配有对应的简要医院病程（BHC）总结，平均输入标记长度为2,267。该数据集旨在支持机器学习模型的开发和训练，特别是用于自动化临床文档总结，以提高医疗护理效率。