An imputation-enhanced algorithm for ICU mortality prediction

ICU patients are vulnerable to in-ICU morbidities and mortality, making accurate systems for identifying at-risk patients a necessity for improving clinical care. Here, we present an improved model for predicting in-hospital mortality using data collected from the first 48 hours of a patient´s ICU stay. We generated predictive features for each patient using demographic data, the number of observations for each of 37 time-varying variables in hours 0-48 and 47-48 of the stay, and the last observed value for each variable. Missing data are a common problem in clinical data, and we therefore imputed missing values using the mean value for a patient´s age and gender group. After imputing the missing data, we trained a logistic regression using this feature set. We evaluated model performance using the two metrics from the 2012 PhysioNet/CinC Challenge; the first measured model accuracy using the minimum of sensitivity and positive predictive value (Event 1), and the second measured model calibration using the Hosmer-Lemeshow H statistic (Event 2). Our model obtained Event 1 and 2 scores of 0.516 and 14.4 for test set B and 0.482 and 51.7 for test set C, respectively, providing better estimates of in-hospital mortality risk than existing methods such as SAPS-I.