Using phrases and document metadata to improve topic modeling of clinical reports.

ABSTRACT: Probabilistic topic models provide an unsupervised method for analyzing unstructured text, which have the potential to be integrated into clinical automatic summarization systems. Clinical documents are accompanied by metadata in a patient's medical history and frequently contains multiword concepts that can be valuable for accurately interpreting the included text. While existing methods have attempted to address these problems individually, we present a unified model for free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts. In the proposed model, phrases are represented by chained n-grams and a Dirichlet hyper-parameter is weighted by both document-level and patient-level context. This method and three other Latent Di

SUBMITTER: Speier W 

PROVIDER: S-EPMC4902330 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature