Clinical documents are a repository of information about patients' conditions. However, this wealth of data is not properly tapped by the existing analysis tools. Dengue is one of the most widespread water borne diseases known today. Every year, dengue has been threatening lives the world over. Systems already developed have concentrated on extracting disorder mentions using dictionary look-up, or supervised learning methods. This project aims at performing Named Entity Recognition to extract disorder mentions, time expressions and other relevant features from clinical data. These can be used to build a model, which can in turn be used to predict the presence or absence of the disease, dengue. Further, we perform a frequency analysis which correlates the occurrence of dengue and the manifestation of its symptoms over the months. The system produces appreciable accuracy and serves as a valuable tool for medical experts.