SCRIPT X2B8 Dataset: per-day clinical features to model successful next-day extubation

Criteria to identify patients who are ready to be liberated from mechanical
ventilation are imprecise, often resulting in prolonged mechanical ventilation
or reintubation, both of which are associated with adverse outcomes. We sought
to determine whether machine learning applied to the electronic health record
could predict extubation success. With the X2B8 dataset, we provide cleaned
next-day extubation labels for an internal cohort of 696 patients and 9,828
ICU days, and an external test cohort of 333 patients and 2,835 ICU days. With
an eye towards future deployment of a model that could be used during daily
clinical rounds, we aggregated data from 37 clinical features from midnight to
8 AM. Data have been deidentified per Health Insurance Portability and
Accountability Act (HIPAA) Safe Harbor rules. We use this dataset in a
manuscript examining different machine learning models to predict successful
extubation (see Usage section for details) and share it for others to work
with.