Patient clustering with uncoded text in electronic medical records.

Journal Article

We propose a mixture model for text data designed to capture underlying structure in the history of present illness section of electronic medical records data. Additionally, we propose a method to induce bias that leads to more homogeneous sets of diagnoses for patients in each cluster. We apply our model to a collection of electronic records from an emergency department and compare our results to three other relevant models in order to assess performance. Results using standard metrics demonstrate that patient clusters from our model are more homogeneous when compared to others, and qualitative analyses suggest that our approach leads to interpretable patient sub-populations when applied to real data. Finally, we demonstrate an example of our patient clustering model to identify adverse drug events.

Duke Authors

Cited Authors

  • Henao, R; Murray, J; Ginsburg, G; Carin, L; Lucas, JE

Published Date

  • January 2013

Published In

Volume / Issue

  • 2013 /

Start / End Page

  • 592 - 599

PubMed ID

  • 24551361

Electronic International Standard Serial Number (EISSN)

  • 1942-597X

Language

  • eng