Patient clustering with uncoded text in electronic medical records.

Ricardo Henao, Jared Murray, Geoffrey Ginsburg, Lawrence Carin, Joseph E. Lucas

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)592-599
Number of pages8
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - Jan 1 2013
Externally publishedYes


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