GOSSIS-1-eICU, the eICU-CRD subset of the Global Open Source Severity of Illness Score (GOSSIS-1) dataset

GOSSIS-1 is a modern, free, open-source in-hospital mortality prediction
algorithm for critical care patients, achieving excellent discrimination and
calibration across three countries (Australia, New Zealand and the USA).
GOSSIS-1 was developed on two large datasets of critical care patients. This
project contains the USA subset of patients derived from the eICU
Collaborative Research Database (eICU-CRD). The dataset, which we call
GOSSIS-1-eICU, consists of 131,051 unique patients from 204 hospitals from ICU
admissions discharged in 2014-15. The code to create the dataset from eICU-CRD
and generate GOSSIS-1 predictions are also available. This project contains:
1) the derived dataset from eICU-CRD, 2) the dataset with required missing
data imputed and 3) the GOSSIS-1 in-hospital predictions (probabilities. The
`patientunitstayid` and `hospitalid` eICU-CRD identifiers are included to
allowing linking back to eICU-CRD. Training and test sets are identified to
allow for direct comparisons of performance.

GOSSIS-1是一款现代化、免费开源的重症患者院内死亡率预测算法，在澳大利亚、新西兰与美国三个国家均展现出优异的区分度与校准性能。该算法基于两个大型重症患者数据集开发。本项目包含从eICU协作研究数据库（eICU Collaborative Research Database，eICU-CRD）中衍生的美国患者子集，我们将该数据集命名为GOSSIS-1-eICU，其涵盖2014至2015年期间来自204家医院的131051名独特重症监护病房（Intensive Care Unit，ICU）住院患者数据。此外，本项目还提供了从eICU-CRD构建该数据集以及生成GOSSIS-1预测结果的代码。本项目包含三部分内容：1）从eICU-CRD衍生的原始数据集；2）完成必要缺失数据插补后的数据集；3）GOSSIS-1的院内死亡预测概率结果。数据中包含eICU-CRD的`patientunitstayid`与`hospitalid`标识符，便于研究者回溯至原始eICU-CRD数据库。同时，数据集已划分训练集与测试集，可直接用于模型性能的横向对比。