title: Building bridges across electronic health record systems through inferred phenotypic topics.
abstract: OBJECTIVE
  Data in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems.
  
  
  METHODS
  We adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using (1) the similarity of topics, (2) the stability of a patient population across topics, and (3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: (1) Northwestern Memorial Hospital (NMH) and (2) Vanderbilt University Medical Center (VUMC).
  
  
  RESULTS
  The method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems.
  
  
  CONCLUSIONS
  Phenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.
authors.affiliation: Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA. Electronic address: you.chen@vanderbilt.edu.
authors.forename: You
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Dept. of Electrical & Computer Engineering, University of Texas, Austin, TX, USA.
authors.forename: Joydeep
authors.initials: J
authors.lastname: Ghosh
authors.affiliation: Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.
authors.forename: Cosmin Adrian
authors.initials: CA
authors.lastname: Bejan
authors.affiliation: Dept. of Computer Science, University of Illinois at Urbana-Champagne, Champaign, IL, USA.
authors.forename: Carl A
authors.initials: CA
authors.lastname: Gunter
authors.affiliation: Dept. of Computer Science, University of Illinois at Urbana-Champagne, Champaign, IL, USA.
authors.forename: Siddharth
authors.initials: S
authors.lastname: Gupta
authors.affiliation: School of Medicine, Northwestern University, Chicago, IL, USA.
authors.forename: Abel
authors.initials: A
authors.lastname: Kho
authors.affiliation: School of Medicine, Northwestern University, Chicago, IL, USA.
authors.forename: David
authors.initials: D
authors.lastname: Liebovitz
authors.affiliation: School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
authors.forename: Jimeng
authors.initials: J
authors.lastname: Sun
authors.affiliation: Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA; Department of Medicine, Vanderbilt University, Nashville, TN, USA.
authors.forename: Joshua
authors.initials: J
authors.lastname: Denny
authors.affiliation: Dept. of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA; Dept. of Electrical Engineering & Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, USA.
authors.forename: Bradley
authors.initials: B
authors.lastname: Malin
mesh_terms: D057286:Electronic Health Records / Q000145:classification / Q000458:organization & administration*; D016247:Information Storage and Retrieval / Q000379:methods*; D000069550:Machine Learning; D008498:Medical Record Linkage / Q000379:methods*; D009323:Natural Language Processing; D010641:Phenotype; D014481:United States; D018875:Vocabulary, Controlled
keywords: Clinical phenotype modeling; Computers and information processing; Data mining; Electronic medical records; Medical information systems; Pattern recognition
references.citation: Biometrics. 1989 Mar;45(1):255-68
references.pmid: 2720055
references.citation: AMIA Annu Symp Proc. 2003;:1065
references.pmid: 14728568
references.citation: Stud Health Technol Inform. 2000;77:784-9
references.pmid: 11187660
references.citation: Bioinformatics. 2010 May 1;26(9):1205-10
references.pmid: 20335276
references.citation: J Am Med Inform Assoc. 2013 Dec;20(e2):e341-8
references.pmid: 24190931
references.citation: J Am Med Inform Assoc. 2013 Jun;20(e1):e147-54
references.pmid: 23531748
references.citation: Stud Health Technol Inform. 2007;129(Pt 1):576-80
references.pmid: 17911782
references.citation: J Healthc Inf Manag. 2001 Summer;15(2):99-105
references.pmid: 11452582
references.citation: Front Genet. 2014 Jun 17;5:184
references.pmid: 24987407
references.citation: J Am Med Inform Assoc. 2013 Dec;20(e2):e306-10
references.pmid: 23956016
references.citation: J Biomed Inform. 2014 Feb;47:39-57
references.pmid: 24076435
references.citation: Methods Inf Med. 2009;48(1):38-44
references.pmid: 19151882
references.citation: BMC Health Serv Res. 2012;12:105
references.pmid: 22545646
references.citation: Nat Biotechnol. 2013 Dec;31(12):1102-10
references.pmid: 24270849
references.citation: Ann Intern Med. 2009 Sep 1;151(5):359-60
references.pmid: 19638404
references.citation: Ann Epidemiol. 2013 Mar;23(3):136-42
references.pmid: 23313264
references.citation: J Biomed Inform. 2010 Dec;43(6):914-23
references.pmid: 20688191
references.citation: Health Aff (Millwood). 2014 Jul;33(7):1155-62
references.pmid: 25006141
references.citation: AMIA Annu Symp Proc. 2012;2012:93-102
references.pmid: 23304277
references.citation: BMJ Open. 2013 Jun 20;3(6):null
references.pmid: 23794587
references.citation: J Am Med Inform Assoc. 2013 Dec;20(e2):e206-11
references.pmid: 24302669
references.citation: PLoS One. 2011;6(1):e15603
references.pmid: 21283751
references.citation: Int J Med Inform. 2014 Jul;83(7):495-506
references.pmid: 24845147
references.citation: Methods Inf Med. 2007;46(6):679-85
references.pmid: 18066419
references.citation: Clin Lab Med. 2008 Mar;28(1):55-71, vi
references.pmid: 18194718
references.citation: J Biomed Inform. 2014 Dec;52:260-70
references.pmid: 25048351
references.citation: J Am Med Inform Assoc. 2013 Jan 1;20(1):144-51
references.pmid: 22733976
references.citation: Stud Health Technol Inform. 2008;136:229-34
references.pmid: 18487736
references.citation: J Biomed Inform. 2012 Dec;45(6):1185-90
references.pmid: 22925724
references.citation: J Am Med Inform Assoc. 2013 Dec;20(e2):e243-52
references.pmid: 23837993
references.citation: Med Care. 2005 May;43(5):480-5
references.pmid: 15838413
references.citation: IEEE Trans Dependable Secure Comput. 2012 May;9(3):332-344
references.pmid: 24489520
references.citation: N Engl J Med. 2009 Apr 16;360(16):1628-38
references.pmid: 19321858
references.citation: J Biomed Inform. 2014 Dec;52:199-211
references.pmid: 25038555
references.citation: BMC Bioinformatics. 2014;15 Suppl 6:S3
references.pmid: 25078762
references.citation: Sci Transl Med. 2011 Apr 20;3(79):79re1
references.pmid: 21508311
references.citation: PLoS One. 2014;9(6):e99825
references.pmid: 24941260
references.citation: J Healthc Inf Manag. 2001 Summer;15(2):155-64
references.pmid: 11452577
references.citation: PLoS One. 2013;8(6):e66341
references.pmid: 23826094