Li Ding Cheng, Thermeau Terry, Chute Christopher, Liu Hongfang
Mayo Clinic, Rochester, MN 55901, USA.
AMIA Jt Summits Transl Sci Proc. 2014 Apr 7;2014:43-9. eCollection 2014.
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.
随着电子病历（EMR）的迅速增长，利用机器学习和数据挖掘技术从EMR数据中自动提取模式或规则的需求日益增加。在这项工作中，我们应用无监督统计模型——潜在狄利克雷分配（LDA），对罗切斯特流行病学项目（REP）中的患者诊断组进行聚类。初步结果表明，由于其无监督性质和强大的解释能力，LDA在流行病学以及其他生物医学研究中具有广泛应用的潜力。
