Discovering Associations Among Diagnosis Groups Using Topic Modeling

With the rapid growth of electronic medical records (EMR), there is an increasing need of automatically extract patterns or rules from EMR data with machine learning and data mining technqiues. In this work, we applied unsupervised statistical model, latent Dirichlet allocations (LDA), to cluster patient diagnoics groups from Rochester Epidemiology Projects (REP). The initial results show that LDA holds the potential for broad application in epidemiogloy as well as other biomedical studies due to its unsupervised nature and great interpretive power.