Patient ranking with temporally annotated data.

Modern medical information systems enable the collection of massive temporal health data. Albeit these data have great potentials for advancing medical research, the data exploration and extraction of useful knowledge present significant challenges. In this work, we develop a new pattern matching technique which aims to facilitate the discovery of clinically useful knowledge from large temporal datasets. Our approach receives in input a set of temporal patterns modeling specific events of interest (e.g., doctor's knowledge, symptoms of diseases) and it returns data instances matching these patterns (e.g., patients exhibiting the specified symptoms). The resulting instances are ranked according to a significance score based on the p-value. Our experimental evaluations on a real-world dataset demonstrate the efficiency and effectiveness of our approach.