Creating Dynamic Groupers Using Overrepresentation of Clinical Terms
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Presented at Epic's Research Advisory Council, April 3, 2014, Verona, WI See a novel approach to query expansion based on pre-existing structured information within the EHR. Presenters adopted over-representation analysis to find statistically significant associations among the clinical terms extracted from Clarity reports. The study population consisted of over 7,000 patients and their 12 million observations - including labs, medications, phenotypes, diseases, and procedures. See the detailed findings and discuss computational and terminology challenges.
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Creating Dynamic Groupers Using Overrepresentation of Clinical Terms
- 1. Creating Dynamic Groupers Using Overrepresentation of Clinical Terms Tomasz Adamusiak MD PhD Froedtert & Medical College of Wisconsin
- 2. 2
- 3. Conflict of interest disclosure Tomasz Adamusiak has no real or apparent conflicts of interest to report 3
- 4. Learning objectives • Recognize the value of structured clinical information • Identify computational and terminology challenges in big data analytics • Evaluate how this approach applies to different use cases 4
- 5. What is a grouper? Lists of specific values derived from standard vocabularies used to define clinical concepts, e.g. patients with diabetes • SNOMED CT concepts • ICD-9/10 codes • EDG terms • CQM Value Sets 5
- 6. Diabetes: Eye Exam CMS eMeasure: CMS131v2 Value Set Name Diabetes Type Grouping Steward National Committee for Quality Assurance Program CMS,MU2 EP Update 2013-06-14 … … … 190330002 Diabetes mellitus, juvenile type, with hyperosmolar coma (disorder) SNOMEDCT 250 Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled ICD9CM E10.10 Type 1 diabetes mellitus with ketoacidosis without coma ICD10CM 6
- 7. Mining associations in EHR data Diabetes mellitus Yes No Glucohemoglobin measurement Yes 1509 5442 No 881 99 7 Positive association Background reference
- 8. Dynamic = expansion + association 8 CPT-4 83036 ICD10 E08-E13
- 10. Transformation in ClinMiner https://clinminer.hmgc.mcw.edu user:epicdemo pass:epicdemo 10 This image by Tomasz Adamusiak is licensed under a CC BY 3.0 US license ClinMiner is a non-commercial, prototype software
- 11. Pilot: test all possible diabetes associations 11 8k patients 12M observations Labs (CPT-4/LOINC) Medications (RxNorm) Problems (ICD-9) Procedures (CPT-4) 18 764 terms 162 significant associations
- 12. Summarize, but normalize per patient 1 + 1 = 1 12 Parent Concepts ICD-10-CM
- 13. Relatively straightforward in ICD 13 Parent Concepts ICD-10-CM
- 14. Caveat: flat hierarchy results in disconnected clinical contexts Q: All tuberculosis codes • 010-018.99 TUBERCULOSIS • 137 Late effects of tuberculosis • 647.3 Tuberculosis complicating pregnancy childbirth or the puerperium 14
- 15. Expansion has to take into account multiple inheritance in SNOMED CT 15 SNOMED CT Parent Concepts
- 16. Pieter Brueghel the Elder (1526/1530–1569) [Public domain], via Wikimedia Commons In pursuit of a single language 16
- 17. Integrating terminologies with UMLS Donald A.B. Lindberg, M.D. Clinical Terminologies UMLS 17
- 18. UMLS is ideal for integration of heterogeneous clinical data • Single entry point to MU terminologies • Cross-walk between MU terms • Terminology-agnostic • Text-mining 18
- 19. UMLS Exanthema C0015230 SNOMED CT ICD-10-CM UMLS establishes equivalence mappings across biomedical terminologies SNOMED CT rash NOS ICD-10:R21 Cutaneous eruption SCT:112625008 Eruption SCT:1806006
- 20. UMLS Exanthema C0015230 SNOMED CT ICD-10-CM UMLS establishes equivalence mappings across biomedical terminologies SNOMED CT Cutaneous eruption SCT:112625008 rash NOS ICD-10:R21 Eruption SCT:1806006
- 21. 6o of terminological Kevin Bacon Acute myocardial infarction Myocardial ischemia Vascular Diseases Disorder of soft tissue Collagen Diseases Connective Tissue Diseases Epidermal and dermal conditions Skin and subcutaneous tissue disorders Dermatologic disorders 21
- 22. Expansion limited to MU terminologies and by semantic type 22 Finding Disease or Syndrome Ignore
- 23. Open issue: cycles due to subtle differences in meaning 23 Immune System Endocrine System
- 24. Expansion in UMLS across MU sources 24 Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled ICD-9 ICD-10 SNOMED CT NDF-RT Situation with explicit context Metabolic diseases roots:
- 25. Statistical methods for establishing over/under-representation • Serial contingency tables • Chi-squared test with Bonferroni correction • RR estimate of effect size • Test diabetes in all 18 764 concept pairs 25
- 26. EHR-based association rule mining Diabetes mellitus (C0011849) Yes No Glucohemoglobin measurement (C0202054) Yes 1509 5442 No 881 99 26 Positive association Background reference
- 27. Other positive associations • C0785704 Blood glucose monitoring equipment • C0935929 Antidiabetics • C0304870 Insulin, Long-Acting • C0770893 Metformin hydrochloride • C0011882 Diabetic Neuropathies • C0011880 Diabetic Ketoacidosis • C0011884 Diabetic Retinopathy 27 Expansion generalization on class or system level
- 28. A non-representative control background can bias the findings Diabetes inversely associated with • C1314183 Special EEG tests • C0242953 Barbiturate hypnotic • C0064636 lamotrigine • C1719410 Epilepsy and recurrent seizures 28
- 29. Open issue: reconciling lab orders with results Clinical Laboratory Hemoglobin A1c/Hemoglobin .total in Blood by HPLC LOINC:17856-6 Hemoglobin; glycosylated (A1C) CPT-4:83036 29
- 30. Challenges • Availability of correctly and exhaustively coded data • Expansion with multiple inheritance memory intensive • Testing all possible (180M) combinations computationally expensive 30
- 31. What can we learn from other industries? 31
- 32. Thank You! Tomasz Adamusiak MD PhD Human and Molecular Genetics Center Medical College of Wisconsin tomasz@mcw.edu @7omasz For more information • Next-generation phenotyping using the Unified Medical Language System (UMLS). Adamusiak T, Shimoyama N, Shimoyama M, JMIR Med Inform. doi:10.2196/medinform.3172 • EHR-based phenome wide association study in pancreatic cancer. Adamusiak T, Shimoyama M, AMIA Summits Transl Sci Proc. 2014 (in press)