Phenotype Annotations for Patient Notes in the MIMIC-III Database

A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much of the information is only present in the patient notes. Therefore, it is critical to develop robust algorithms to infer patients' conditions and treatments from their written notes.

We introduce a dataset for patient phenotyping, a task that is defined as the identification whether a patient has a given phenotype (also referred to as indication) based on their patient note. Patient notes of MIMIC-III, a dataset collected from Intensive Care Units of a large tertiary care hospital in Boston, were manually annotated for the presence of several high-context phenotypes relevant to treatment and risk of re-hospitalization.

Each note has been annotated by two expert human annotators (one clinical researcher and one resident physician). Annotated phenotypes include treatment non-adherence, chronic pain, advanced/metastatic cancer, as well as 10 other phenotypes. This dataset can be utilized for academic and industrial research in medicine and computer science, particularly within the field of medical natural language processing. 

在电子健康记录（EHRs）的二次分析中，识别调查中的患者队列是一项至关重要的步骤。尽管 EHRs 中包含旨在代表患者可能患有疾病和接受的治疗的医学账单代码，但大量信息仅存在于患者笔记中。因此，开发能够从患者书面笔记中推断患者状况和治疗的稳健算法至关重要。本研究所引入的数据集旨在对患者表型进行分类，该任务定义为根据患者的患者笔记确定患者是否具有特定的表型（亦称指示）。来自波士顿一家大型三级护理医院重症监护单元的 MIMIC-III 数据集中的患者笔记已被手动标注，以识别与治疗和再次住院风险相关的多个高情境表型。每个笔记均由两位专家人类标注者（一名临床研究人员和一名住院医师）进行标注。标注的表型包括治疗依从性差、慢性疼痛、晚期/转移性癌症，以及另外 10 种表型。此数据集可用于医学和计算机科学领域的学术及工业研究，特别是在医学自然语言处理领域。