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Patient clustering with uncoded text in electronic medical records.

Publication ,  Journal Article
Henao, R; Murray, J; Ginsburg, G; Carin, L; Lucas, JE
Published in: AMIA Annu Symp Proc
2013

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 Scholars

Published In

AMIA Annu Symp Proc

EISSN

1942-597X

Publication Date

2013

Volume

2013

Start / End Page

592 / 599

Location

United States

Related Subject Headings

  • Pharmacovigilance
  • Natural Language Processing
  • Models, Statistical
  • Humans
  • Emergency Service, Hospital
  • Electronic Health Records
  • Drug-Related Side Effects and Adverse Reactions
  • Cluster Analysis
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Henao, R., Murray, J., Ginsburg, G., Carin, L., & Lucas, J. E. (2013). Patient clustering with uncoded text in electronic medical records. AMIA Annu Symp Proc, 2013, 592–599.
Henao, Ricardo, Jared Murray, Geoffrey Ginsburg, Lawrence Carin, and Joseph E. Lucas. “Patient clustering with uncoded text in electronic medical records.AMIA Annu Symp Proc 2013 (2013): 592–99.
Henao R, Murray J, Ginsburg G, Carin L, Lucas JE. Patient clustering with uncoded text in electronic medical records. AMIA Annu Symp Proc. 2013;2013:592–9.
Henao, Ricardo, et al. “Patient clustering with uncoded text in electronic medical records.AMIA Annu Symp Proc, vol. 2013, 2013, pp. 592–99.
Henao R, Murray J, Ginsburg G, Carin L, Lucas JE. Patient clustering with uncoded text in electronic medical records. AMIA Annu Symp Proc. 2013;2013:592–599.

Published In

AMIA Annu Symp Proc

EISSN

1942-597X

Publication Date

2013

Volume

2013

Start / End Page

592 / 599

Location

United States

Related Subject Headings

  • Pharmacovigilance
  • Natural Language Processing
  • Models, Statistical
  • Humans
  • Emergency Service, Hospital
  • Electronic Health Records
  • Drug-Related Side Effects and Adverse Reactions
  • Cluster Analysis
  • Algorithms