迈向 ELISE 项目中具有进化性的开放式儿科重症监护数据集。
Towards an Evolutionary Open Pediatric Intensive Care Dataset in the ELISE Project.
机构信息
Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, Germany.
Big Data in Medicine, Department of Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany.
出版信息
Stud Health Technol Inform. 2022 Jun 29;295:100-103. doi: 10.3233/SHTI220670.
BACKGROUND
To embrace the need for freely accessible training data sets originating from the real world, in the ELISE project, we integrate source data from a pediatric intensive care unit and provide it to researchers.
OBJECTIVE
We present our vision, initial results and steps on a trail towards an evolutionary open pediatric intensive care data set.
METHODS
Our evolution plan for the data set comprises three steps. The final data set will include raw clinical data and labels on critical outcomes such as organ dysfunction and sepsis, generated automatically by computerized and well-evaluated methods.
RESULTS
First step resulted in an initial version data set available in a central repository.
CONCLUSIONS
Our approach has great potential to provide a comprehensive open intensive care data set labeled for critical pediatric outcomes and, thus, contributing to overcome the current lack of real-world pediatric intensive care data usable for training data-driven algorithms.
背景
为了满足对源自真实世界的免费可访问训练数据集的需求，在 ELISE 项目中，我们整合了儿科重症监护病房的源数据，并将其提供给研究人员。
目的
我们提出了我们的愿景、初步结果以及朝着开发进化型开放儿科重症监护数据集的方向迈出的第一步。
方法
我们的数据集演进计划包括三个步骤。最终数据集将包括原始临床数据和由计算机生成并经过良好评估的方法自动标记的关键结果（如器官功能障碍和败血症）的标签。
结果
第一步产生了一个可在中央存储库中获得的初始版本数据集。
结论
我们的方法具有提供全面的开放重症监护数据集的巨大潜力，这些数据集标记了关键儿科结果，从而有助于克服当前缺乏可用于训练数据驱动算法的真实世界儿科重症监护数据的问题。
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