Topical Cluster Discovery in Semistructured Healthcare Data

We propose an approach to clustering XML-based corpora of healthcare documents by their latent topic similarity. Our approach is a two-step process. Initially, the latent topic distributions of the input healthcare documents are inferred, by performing collapsed Gibbs sampling and parameter estimation under an XML topic model. Subsequently, the inferred distributions are grouped through established clustering techniques.

• Publication year

  2018

• Venue

  International Conference on Wirtschaftsinformatik

• Publication date

  2018-12-01

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1109/WI.2018.00014
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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