Assessing pneumonia identification from time-ordered narrative reports.

In this paper, we present a natural language processing system that can be used in hospital surveillance applications with the purpose of identifying patients with pneumonia. For this purpose, we built a sequence of supervised classifiers, where the dataset corresponding to each classifier consists of a restricted set of time-ordered narrative reports. In this way the pneumonia surveillance application will be able to invoke the most suitable classifier for each patient based on the period of time that has elapsed since the patient was admitted into the hospital. Our system achieves significantly better results when compared with a baseline previously proposed for pneumonia identification.