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.多伦多联合健康（Unity Health Toronto）的数据科学与高级分析（Data Science and Advanced Analytics, DSAA）团队开发并评估了先进的患者监护与决策支持系统，旨在提升圣迈克尔医院（St. Michael's Hospital）普通内科（General Internal Medicine, GIM）住院病房的临床决策效率、准确性与及时性。本GIM数据集即基于此项工作构建，涵盖2011至2019年间在圣迈克尔医院接受普通内科诊疗的14000名独特患者的22000余次就诊相关的去标识化健康数据。该数据集来源于三个独立系统：电子健康记录（Electronic Health Records）、出入转科系统（Admit Discharge Transfer System）以及药物给药核查系统（Medication Administration Check System）。为方便使用，本次提供了将观测数据按固定时间窗口聚合后的预处理数据集；同时也为希望自主开展数据转换的研究者提供了原始未处理数据集，其中包含预处理数据的人口统计学特征与结局指标表格。可用的患者结局指标包括重症监护病房（Intensive Care Unit, ICU）转科、死亡、姑息治疗收治、姑息治疗出院以及医院出院。