Data Mined Models for Predicting In-hospital Mortality of Emergency Admissions at Time of Hospital Admission Robert Steele
Robert JC Steele, Trevor Hillsgrove · 2019
Emergency admissions involve unplanned admissions for which admission must occur at the earliest possible point of time. Being able to predict in-hospital mortality at the point of admission has significant clinical importance. In this work we have drawn from a large state-wide dataset to train predictive models for in-hospital mortality of emergency admissions, that can be applicable at time of admission. A number of models demonstrate high predictive performance, with AUC scores of up to 0.861. The approach taken represents an alternative to models based upon rich clinical data and may offer a number of comparative advantages in terms of very early point-in-time applicability and broad generalizability.