A novel approach to generating CER hypotheses based on mining clinical data

Comparative effectiveness research (CER) is a scientific method of investigating the effectiveness of alternative intervention methods. In a CER study, clinical researchers typically start with a CER hypothesis, and aim to evaluate it by applying a series of medical statistical methods. Traditionally, the CER hypotheses are defined manually by clinical researchers. This makes the task of hypothesis generation very time-consuming and the quality of hypothesis heavily dependent on the researchers' skills. Recently, with more electronic medical data being collected, it is highly promising to apply the computerized method for discovering CER hypotheses from clinical data sets. In this poster, we proposes a novel approach to automatically generating CER hypotheses based on mining clinical data, and presents a case study showing that the approach can facilitate clinical researchers to identify potentially valuable hypotheses and eventually define high quality CER studies.