Development and Application of an Intensive Care Medical Data Set for Deep Learning

A large number of patient healthcare data have been collected in the process of diagnosis and treatment of intensive care medicine, which provides major benefits for patient safety and quality. Unfortunately, the application of medical data is greatly limited. Key barriers to the use of the data include difficulties in data extraction and cleaning, and the construction of high-quality data sets promotes the research of medical big data analysis. In China, there is few intensive care data set built by clinicians has been used for clinical outcome prediction. This study developed and evaluated an Intensive Care Medical (ICM) data set for critically care patients that can be used for deep learning. The ICM data set contained four types of data collected routinely in Chinese hospitals, including all-cause characteristics of administrative information, vital signs, laboratory tests, and intravenous medication records. A total of 17,291 ICU admissions involving 12,815 patients aged 14 years and older were extracted from the data set. Deep learning model achieved high accuracy for tasks in hospital mortality predicting (AUROC[area under the receiver operator curve] reach 0.8941). We believe that the ICM data set can be used to create accurate predictions for a variety of clinical scenarios.