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

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 Naive Bayes (NB), Support Vector Machines (SVM) and a C4.5 Decision Tree to the problem and the evaluation of the efficiency of this approach. The three classifiers showed an overall similar performance. SVM yielded the best recall value, 89.8%, for records that contain HAI. We present a machine learning approach as an alternative to rule-based systems which are more common in this task. The classifiers were applied on a small and noisy dataset, generating results which pinpoint the potentials of using learning algorithms for detecting HAI. Further research will have to focus on optimizing the performance of the classifiers and to test them on larger datasets.