title: Patient clustering with uncoded text in electronic medical records.
abstract: 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.
authors.affiliation: Duke University, Durham, NC.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Henao
authors.affiliation: Duke University, Durham, NC.
authors.forename: Jared
authors.initials: J
authors.lastname: Murray
authors.affiliation: Duke University, Durham, NC.
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Ginsburg
authors.affiliation: Duke University, Durham, NC.
authors.forename: Lawrence
authors.initials: L
authors.lastname: Carin
authors.affiliation: Quintiles, Durham, NC.
authors.forename: Joseph E
authors.initials: JE
authors.lastname: Lucas
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D064420:Drug-Related Side Effects and Adverse Reactions; D057286:Electronic Health Records; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D006801:Humans; D015233:Models, Statistical; D009323:Natural Language Processing; D060735:Pharmacovigilance