Development and assessment of a machine learning tool for predicting emergency admission in Scotland

ABSTRACT Avoiding emergency hospital admission (EA) is advantageous to individual health and the healthcare system. We develop a statistical model estimating risk of EA for most of the Scottish population (> 4.8M individuals) using electronic health records, such as hospital episodes and prescribing activity. We demonstrate good predictive accuracy (AUROC 0.80), calibration and temporal stability. We find strong prediction of respiratory and metabolic EA, show a substantial risk contribution from socioeconomic decile, and highlight an important problem in model updating. Our work constitutes a rare example of a population-scale machine learning score to be deployed in a healthcare setting.