NCH Sleep DataBank: A Large Collection of Real-world Pediatric Sleep Studies with Longitudinal Clinical Data

In order to accelerate research on pediatric sleep and its connection to
health, Nationwide Children's Hospital (NCH) and Carnegie Mellon University
(CMU) introduce the NCH Sleep DataBank. This dataset has 3,984 pediatric sleep
studies on 3,673 unique patients conducted at NCH in Columbus, Ohio, USA
between 2017 and 2019, along with the patient's longitudinal clinical data.
The published Polysomnography (PSG) contains the patient's physiological
signals as well as the technician's assessment of the sleep stages and
descriptions of additional irregularities.

The novelties of this dataset include: (1) Size **:** Its large size is
suitable for discovering new scientific insights via data mining, (2) ****
Patient population **:** It explicitly focuses on pediatric patients, (3)
Clinical setting **:** The sleep studies were gathered in the real-world
clinical setting at NCH as opposed to, for example, in a controlled clinical
trial, and (4) Rich set of clinical data **:** The accompanying 5.6 million
records of clinical data are extracted from the Electronic Health Record
(EHR), and are separated into encounters, medications, measurements (e.g. body
mass index), diagnoses, and procedures.

The NCH Sleep DataBank is a valuable resource for advancing automatic sleep
scoring and real-time sleep disorder prediction, among many other potential
scientific discoveries. Accompanying code in Python to assist users in
interacting with the dataset is published on GitHub.

为推进儿童睡眠及其与健康关联的相关研究，美国全国儿童医院（Nationwide Children's Hospital, NCH）与卡内基·梅隆大学（Carnegie Mellon University, CMU）发布了NCH睡眠数据库（NCH Sleep DataBank）。
该数据集包含2017年至2019年间，于美国俄亥俄州哥伦布市的NCH完成的3984项儿童睡眠研究，涉及3673名独立患者，同时附带患者的纵向临床数据。已公开的多导睡眠图（Polysomnography, PSG）记录包含患者的生理信号、技术人员对睡眠阶段的评估，以及对各类异常情况的描述。

本数据集的创新亮点包括：（1）规模优势：其庞大的数据体量适用于通过数据挖掘挖掘全新科学发现；（2）聚焦特定人群：明确以儿童患者为研究对象；（3）真实临床场景：睡眠研究数据采集自NCH的真实临床环境，而非诸如受控临床试验等人工受控场景；（4）丰富配套临床数据：附带的560万条临床数据提取自电子健康档案（Electronic Health Record, EHR），并分为就诊记录、用药信息、测量指标（如身体质量指数）、诊断结果与诊疗操作五大类。

NCH睡眠数据库是推进自动睡眠评分、实时睡眠障碍预测等诸多潜在科学研究的宝贵资源。配套的Python交互代码已在GitHub平台公开发布。