GIM, a dataset for predicting patient deterioration in the General Internal Medicine ward

The Data Science and Advanced Analytics (DSAA) team at Unity Health Toronto has developed and evaluated advanced patient monitoring and decision support systems to improve the efficiency, accuracy, and timeliness of clinical decision-making on the General Internal Medicine (GIM) inpatient ward at St. Michael’s Hospital. The GIM dataset was created through this work, and is comprised of de-identified health related data associated with over 22,000 patient encounters for 14,000 unique patients who were admitted under the GIM service at St. Michael’s Hospital between 2011 and 2019. The dataset was sourced from three distinct systems (Electronic Health Records, the Admit Discharge Transfer System and the Medication Administration Check System). Pre-processed datasets aggregating observations into fixed time windows are provided for convenience. A raw untransformed data set is also provided for researchers who wish to apply their own data transformations and includes demographics and outcome tables from the processed data. Patient outcomes available include ICU transfer, death, palliative entry, palliative discharge, and hospital discharge.

多伦多Unity Health的Data Science and Advanced Analytics（DSAA）团队已研发并评估了先进的病人监护与决策支持系统，旨在提升圣迈克尔医院的普通内科（GIM）住院病房临床决策的效率、准确性与及时性。该GIM数据集由此工作所创建，包含与超过22,000次病人就诊相关的14,000名独特患者的脱敏健康数据。数据集来源于三个不同的系统（电子健康记录、入院出院转院系统及药物管理检查系统）。为方便起见，提供了将观察结果汇总至固定时间窗口的预处理数据集。同时，还提供了原始未转换数据集，供研究人员应用自身的数据转换方法，其中包含人口统计学和结果表等经过处理的数据。可供查询的患者结果包括ICU转院、死亡、姑息治疗入院、姑息治疗出院及医院出院。