Synthetic Electronic Health Record data generated at UCLH for the project : AI Septron

Synthetic data generated to represent the structure of data extracted from the UCLH Electronic Health Record. They are selected tables and fields from the OMOP Common Data Model v5.4 with concept_name columns added for readability.These synthetic data are based on the AI Septron project that aims to make a strong and accurate computer program that can identify the risk of sepsis and serious infections in children. AI Septron is run by Dr Sylvester Gomes a Consultant in Paediatric Emergency Medicine at Evelina London Children's Hospital.These are low fidelity synthetic data generated using datafaker. The columns are currently generated independently so any relationships between them may be nonsensical e.g. birth dates occurring after death dates.These data are artificially generated, any resemblance to real patients is coincidental.

本合成数据旨在复现从伦敦大学学院医院信托基金（UCLH）电子健康记录（Electronic Health Record, EHR）中提取的数据结构。该数据集选自OMOP通用数据模型（OMOP Common Data Model）v5.4中的部分表格与字段，并新增了concept_name列以提升可读性。本合成数据基于AI Septron项目构建，该项目旨在开发一款高性能且精准的计算机程序，用于识别儿童败血症与严重感染的发病风险。AI Septron项目由伦敦伊芙琳儿童医院儿科急诊医学顾问西尔维斯特·戈麦斯博士主导。本数据集为使用datafaker生成的低保真度合成数据。当前各列均为独立生成，因此列间可能存在不合逻辑的关联，例如出生日期晚于死亡日期。本数据集为人工生成，与真实患者的任何相似均为偶然。