title: Discovering associations among diagnosis groups using topic modeling.
abstract: 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.
authors.affiliation: Mayo Clinic, Rochester, MN 55901, USA.
authors.forename: Ding Cheng
authors.initials: DC
authors.lastname: Li
authors.affiliation: Mayo Clinic, Rochester, MN 55901, USA.
authors.forename: Terry
authors.initials: T
authors.lastname: Thermeau
authors.affiliation: Mayo Clinic, Rochester, MN 55901, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Chute
authors.affiliation: Mayo Clinic, Rochester, MN 55901, USA.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu