Detection of Hospital Acquired Infections in sparse and noisy Swedish patient records : A machine learning approach using Naïve Bayes, Support Vector Machines and C4.5
Claudia Ehrentraut, Hideyuki Tanushi, Hercules Dalianis, Jörg Tiedmann · 2012
Hospital Acquired Infections (HAI) pose a significant risk on patients’ health while their surveillance is an additional work load for hospital medical staff and hospital management. Our overall aim is to build a system which reliably retrieves all patient records which potentially include HAI, to reduce the burden of manually checking patient records by the hospital staff. In other words, we emphasize recall when detecting HAI (aiming at 100%) with the highest precision possible. The present study is of experimental nature, focusing on the application of Naïve Bayes (NB), Support Vector Machines (SVM) and a C4.5 Decision Tree as well as different preprocessing methods to the problem, and the evaluation of the efficiency of this approach. The three machine learning algorithms are applied in three drafted classification tasks: binary classification, two-step classification and multi-class classification. Our machine learning approach is presented as an alternative to rule-based systems which are