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.为加速儿科睡眠领域研究及其与健康的关联机制探索，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）联合推出NCH睡眠数据库（NCH Sleep DataBank）。该数据集涵盖2017至2019年间，于美国俄亥俄州哥伦布市NCH开展的3984项儿科睡眠研究，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）包含患者的生理信号、技术人员对睡眠阶段的评估结果，以及各类异常睡眠情况的描述。 本数据集的核心特色包括：（1）数据规模：体量庞大，适用于通过数据挖掘挖掘全新科学发现；（2）研究人群聚焦：明确以儿科患者为核心研究对象；（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的人工场景；（4）丰富配套临床数据：附带的560万条临床数据均提取自电子健康档案（Electronic Health Record, EHR），并被划分为就诊记录、用药信息、检测指标（如身体质量指数）、诊断结果与诊疗操作五大类别。 NCH睡眠数据库是推动自动化睡眠评分、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。