Abstract:
The healthcare system is overburdened. There are just 1.34 doctors per 1000 patients in India.
Intensive Care imposes the highest burden on healthcare systems all across the globe. A significant fraction of lives lost and expenditure is attributed to complications such as hemodynamic
shock, which are reversible if treated early. Artificial Intelligence-based early predictions can
potentially save lives. Generalization across geographical contexts is a challenge. In this study,
we approach generalizable, real-time prediction of new-onset shock in the ICU vitals data. The
eICU dataset was used to develop generalizable models. We externally validated these models
on a cohort on the MIMIC III database consisting of 5 million patient hours and a pediatric ICU
in New Delhi with over 1.5 million patient hours. We predicted shock up to 8 hours in advance.
