CHIFIR: Cytology and Histopathology Invasive Fungal Infection Reports

Surveillance of invasive fungal infection (IFI) in clinical settings is a
laborious process requiring a detailed review of patient medical history. One
of the key sources of clinical information is cytology and histopathology
reports: pathologist-produced free-text reports outlining the macroscopic and
microscopic structure of a specimen. The data was generated to facilitate the
development of an automated tool for the detection of IFI. The Cytology and
Histopathology Invasive Fungal Infection Reports (CHIFIR) corpus contains 283
de-identified reports annotated by infectious diseases physicians for
terminology relevant to the IFI diagnosis. These include IFI-specific concepts
and certainty cues, as well as relations between them. We release the
annotation schema and the original reports along with the corresponding
annotation files. We anticipate the CHIFIR corpus to be useful in the
development and validation of named entity recognition and relation extraction
methods with a focus on clinical data. Such methods can be instrumental in
processing other types of clinical documentation (radiology reports, clinical
notes, nursing notes) with various downstream tasks in mind.

临床侵袭性真菌感染（Invasive Fungal Infection, IFI）的监测是一项耗时费力的工作，需要对患者的病史进行细致审阅。临床信息的核心来源之一为细胞病理学与组织病理学报告：即病理学家撰写的自由文本报告，详细阐述样本的大体形态与微观结构特征。
本数据集的构建旨在助力侵袭性真菌感染自动化检测工具的研发。
细胞病理学与组织病理学侵袭性真菌感染报告（Cytology and Histopathology Invasive Fungal Infection Reports, CHIFIR）语料库包含283份经去标识化处理的报告，由感染病科医师针对与IFI诊断相关的专业术语开展标注，标注内容涵盖IFI专属概念、确定性提示线索，以及各概念间的关联关系。
本次发布同步提供标注方案、原始报告及对应的标注文件。
我们预期CHIFIR语料库能够为聚焦临床数据的命名实体识别与关系抽取方法的研发与验证提供支撑，此类方法还可辅助处理其他类型的临床文档（如放射学报告、临床病历、护理记录），并适配各类下游任务场景。