Robust outcome prediction for intensive-care patients.
Missing data are a major plague of medical databases in general, and of Intensive Care Unit databases in particular. The time pressure of work in an Intensive Care Unit pushes the physicians to omit randomly or selectively record data. These different omission strategies give rise to different patterns of missing data and the recommended approach of completing the database using median imputation and fitting a logistic regression model can lead to significant biases. This paper applies a new classification method, called robust Bayes classifier, which does not rely on any particular assumption about the pattern of missing data and compares it to the median imputation approach using a database of 324 Intensive Care Unit patients.
Ramoni
M
M
Children's Hospital Informatics Program, Harvard Medical School, Boston MA, USA. marco_ramoni@harvard.edu
Sebastiani
P
P
Dybowski
R
R
D001499:Bayes Theorem; D003661:Decision Support Techniques; D004635:Emergency Medicine; D006305:Health Status Indicators; D006801:Humans; D007362:Intensive Care Units; D015233:Models, Statistical; D012680:Sensitivity and Specificity