1. Cornerstone I: Representing Knowledge From Data to Knowledge Through Concept-Oriented Terminologies James J. Cimino

2. The first step on the path to knowledge is getting things by their right names. -Chinese saying

3. Overview What is “data to knowledge”? Knowledge representation choices Knowledge-based terminology efforts Medical Entities Dictionary Proof of concepts

4. What is “data to knowledge”? Start with patient data in the medical record Enhance knowledge by: –gaining a better understanding of the patient –learning relevant knowledge –bringing smart systems to bear to apply knowledge –discovering new knowledge from health data

5. Knowledge Representation Terminology for representing symbols Format for arranging the symbols

6. Knowledge Representation Choices Guideline implementation

7. Guideline Implementation Starren and Xie, SCAMC, 1994 National Cholesterol Education Panel Guideline

8. Measure Cholesterol & Assess Risk Factors Cholesterol 200 to 239 Cholesterol <200 Cholesterol >239 HDL >35, <2 Risks HDL <35 or 2 Risks Provide dietary information Reevaluate in 2 years Cholesterol 200 to 239 HDL >35, <2 Risks

9. Guideline Implementation Starren and Xie, SCAMC, 1994 National Cholesterol Education Panel Guideline Three representations: –PROLOG (first-order logic)

10. NCEP Guideline in PROLOG rule_j(PID):- check_lab(PID,hdl,HDL,_),!, HDL >= 35, total_risk(PID,Risk),!, Risk < 2, check_lab(PID,cholesterol), C,_), C >= 200, C =< 239, print_rule_j.

11. Guideline Implementation Starren and Xie, SCAMC, 1994 National Cholesterol Education Panel Guideline Three representations: –PROLOG (first-order logic) –CLASSIC (frames)

12. NCEP Guideline in CLASSIC (CL-DEFINE-CONCEPT ‘C-PATIENT ‘(AND (ALL CHOL (AND INTEGER (MIN 200) (MAX 239))))) (CL-DEFINE-CONCEPT ‘G-PATIENT ‘(AND C-PATIENT LOW-RISK-PATIENT (ALL HDL (AND INTEGER (MIN 35)))))

13. Guideline Implementation Starren and Xie, SCAMC, 1994 National Cholesterol Education Panel Guideline Three representations: –PROLOG (first-order logic) –CLASSIC (frames) –CLIPS (production rules)

14. NCEP Guideline in CLIPS (defrule C2G2J “Rules to reach box J” ?f1 <- (calculated-patient (state c) (done no) (hdl ?hdl) (name ?name) (test (>= ?hdl 35)) => (printout “Patient “ ?name “needs treatment”)

15. Guideline Implementation Starren and Xie, SCAMC, 1994 National Cholesterol Education Panel Guideline Three representations: –PROLOG (first-order logic) –CLASSIC (frames) –CLIPS (production rules) “All three representations proved adequate for encoding the guideline”

16. Knowledge Representation Choices Guideline implementation Terminologic knowledge

17. Terminology Representation Choices Frame-based

18. Frame-Based Representation Serum Glucose Test is-a:Lab Test Measures:Glucose Specimen:Serum Units:“mg/dl”

19. Terminology Representation Choices Frame-based Terminology Representation Choices Semantic network

20. Semantic Network Representation Serum Glucose Test Chemical is-a Lab Test is-a Body Substance is-a Serum Glucose

21. Terminology Representation Choices Frame-based Semantic network Terminology Representation Choices Conceptual graphs

22. Conceptual Graph Representation [Serum Glucose Test] - (is-a) -> [Lab Test] (measures) -> [Glucose] (specimen) -> [Serum]

23. Terminology Representation Choices Frame-based Semantic network Conceptual graphs Terminology Representation Choices

24. Knowledge Representation Choices Guideline implementation Terminologic knowledge

25. Knowledge Representation Terminology for representing symbols Format for arranging the symbols Terminology and format for representing terminologic knowledge

26. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991

27. Conceptual graphs to model findings increased_uptake site femur site_attr right during bone_phase

28. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993

29. GALEN project conditions grammatically haveLocation bodyparts fractures sensibly haveLocation bones femurs sensiblyAndNecessarily haveDivision neck

30. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993

31. Conceptual graphs and SNOMED Pain + Chest + Radiation to + Left + Arm (located in) -> [Chest] (radiating to) -> [Arm] -> (with laterality) -> [Left] [Pain] -

32. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993 Lindberg, Humphreys, McCray, Methods 1993

33. Unified Medical Language System Lexical group String Concept String Lexical group

34. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993 Lindberg, Humphreys, McCray, Methods 1993 Rocha, Huff, et al., CBM, 1994

35. VOSER A server architecture for managing terminologic knowledege

36. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993 Lindberg, Humphreys, McCray, Methods 1993 Rocha, Huff, et al., CBM, 1994 Campbell, Cohn, Chute, et al., SCAMC 1996

37. Convergent Medical Terminology SNOMED/Kaiser/Mayo Galapagos

38. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993 Lindberg, Humphreys, McCray, Methods 1993 Rocha, Huff, et al., CBM, 1994 Campbell, Cohn, Chute, et al., SCAMC 1996 Brown, O’Neil and Price, Methods, 1997

39. Read Codes Representation with GALEN model

40. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993 Lindberg, Humphreys, McCray, Methods 1993 Rocha, Huff, et al., CBM, 1994 Campbell, Cohn, Chute, et al., SCAMC 1996 Brown, O’Neil and Price, Methods, 1997 Spackman, Campbell, and Côte, SCAMC 1997

41. SNOMED RT (Reference Terminology) Convergent Medical Terminology Description Logic Format

42. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993 Lindberg, Humphreys, McCray, Methods 1993 Rocha, Huff, et al., CBM, 1994 Campbell, Cohn, Chute, et al., SCAMC 1996 Brown, O’Neil and Price, Methods, 1997 Spackman, Campbell, and Côte, SCAMC 1997 Huff, Rocha, McDonald, et al., JAMIA 1998

43. Logical Observations, Identfiers, Names and Codes (LOINC) 4764-5 | GLUCOSE^3H POST 100 G GLUCOSE PO | SCNC | PT | SER/PLAS | QN|

44. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993 Lindberg, Humphreys, McCray, Methods 1993 Rocha, Huff, et al., CBM, 1994 Campbell, Cohn, Chute, et al., SCAMC 1996 Brown, O’Neil and Price, Methods, 1997 Spackman, Campbell, and Côte, SCAMC 1997 Huff, Rocha, McDonald, et al., JAMIA 1998 Pharmacy system knowledge base vendors

45. Pharmacy System Knowledge Base Vendors Manufactured Components Country-Specific Packaged Product Ingredient Ingredient Class is-a Drug Class is-a Not-Fully-Specified Drug is-a Clinical Drug is-a Trademark Drug is-a International Package Identifiers is-a Composite Trademark Drug Composite Clinical Drug is-a

46. Knowledge-Based Terminology Efforts Jochen Bernauer, SCAMC, 1991 Rector, Nolan and Glowinski, SCAMC, 1993 Campbell and Musen, SCAMC, 1993 Lindberg, Humphreys, McCray, Methods 1993 Rocha, Huff, et al., CBM, 1994 Campbell, Cohn, Chute, et al., SCAMC 1996 Brown, O’Neil and Price, Methods, 1997 Spackman, Campbell, and Côte, SCAMC 1997 Huff, Rocha, McDonald, et al., JAMIA 1998 Pharmacy system knowledge base vendors

47. Medical Entities Dictionary (MED) New York Presbyterian Hospital 60,000 concepts (procs, results, drugs, probs) 208,242 synonyms 84,677 hierarchical links 113,906 semantic links 238,040 other attributes 66,404 translations (ICD9-CM, LOINC, MeSH, UMLS)

48. Central Controlled Terminology

49. MED Data Structures Semantic network

50. MED Semantic Network Medical Entity Plasma Glucose Laboratory Specimen Plasma Specimen Anatomic Substance Plasma Substance Sampled Part of Has Specimen Substance Measured Laboratory Procedure CHEM-7 Laboratory Test Event Diagnostic Procedure Substance Bioactive Substance Glucose Chemical Carbo- hydrate

51. MED Data Structures Semantic network MUMPS global

52. MED MUMPS Global ^med(1600) ^med(1600,1)..,4)..,5) <>..,6)..,7) <>..,8)..,12)..,14)..,16)..,17)..,20)..,23)..,50)..,138)..,156)..,161)

53. MED Data Structures Semantic network MUMPS global DB2

54. MED DB2 Tables 12341234 Entities 10 Name 20 UMLS 30 Part-of 40 Specimen Slots 1 10 2 10 2 20 2 30 Entity-Slots 1 10 Entity 2 10 C0001 2 40 1234 2 50 mg/dl Entity/Slot/Values 1 1 2 1 3 2 3 Ancestry

55. MED Data Structures Semantic network MUMPS global DB2 Unix

56. MED UNIX Data Structure 1600|SERUM GLUCOSE MEASUREMENT |1|C020241|4|32703|4|50000|12|GL UC|17|mg/dl|........

57. MED Data Structures Semantic network MUMPS global DB2 UNIX

58. Proof of Concepts Merging data and application knowledge

59. Merging Data and Application Knowledge Plasma Glucose Test Serum Glucose TestFingerstick Glucose Test Lab Test Intravascular Glucose Test Chem20 Display Lab Display Class-based, reusable lab summaries

60. DOP Summary

61. WebCIS Summary

62. Merging Data and Application Knowledge Plasma Glucose Test Serum Glucose TestFingerstick Glucose Test Lab Test Intravascular Glucose Test Chem20 Display Lab Display Class-based, reusable lab summaries Expert system for application maintenance

63. Proof of Concepts Merging data and application knowledge Smarter retrievals from the record

64. Smarter Retrievals from the Record Repository stores events and results Clinical problems at a different level of granularity Re-use knowledge to map from problems to clinical data Produce problem-specific views of the medical record

65. Chest X ray Congestive Heart Failure Intravascular CK Test Creatine Kinase Chest X ray 2 View Cardiac Enzyme Angina Lab :1/1/99 Cardiac Enzyme Test Radiology :2/23/99 Chest X Ray Radiology :2/28/96 Head CT Lab :12/28/96 Sickle Cell Test Admission :3/14/96 Stroke Admission :2/14/98 Angina Lab :1/1/99 Blood Type Test Radiology :2/1/97 Knee X Ray Concept-oriented (Heart) Heart Disease Chest Discharge :1/15/99 CHF CHF Discharge :1/15/99 CHF CHF Admission :2/14/98 Angina Lab :1/1/99 Cardiac Enzyme Test Radiology :2/23/99 Chest X Ray

70. Proof of Concepts Merging data and application knowledge Smarter retrievals from the record “Just-in-Time” education

71. “Just-in-time” Education Medline button Infobuttons

82. “Just-in-time” Education Medline button Infobuttons Text-to-Web

93. Medline button Infobuttons Text-to-Web “Just-in-time” Education DXplain Medline Cholesterol Guideline Dietary Interactions PDR Micromedex Clinical Info System Webpath CHORUS Radiol Museum of South Bank Laboratory Test Results Medication Orders X-ray Reports ICD9

94. Proof of Concepts Merging data and application knowledge Smarter retrievals from the record “Just-in-Time” education Expert systems

95. Expert Systems Hripcsak, et al., Ann. Int. Med., 1995

96. Identify chest x-ray reports suspicious for 6 clinical conditions to trigger alerts MethodSensSpec Laypersons22-47%97-99% Radiologists73-98%96-99% Internists68-98%97-99% Keyword51-79%79-92% NLP/MED/Rule-based 81% 98%

97. Expert Systems Hripcsak, et al., Ann. Int. Med., 1995 Clinical decision support system

98. Clinical Decision Support System Data monitor runs rules against incoming reports Tuberculosis cultures come back 4-8 weeks later One day, hundreds of TB alerts came in

99. What Happened to the Tuberculosis Alert? No Growth Medical Logic Module No Growth to Date 

100. No Growth after... How We Outsmarted the Lab No Growth No Growth after 48 Hours No Growth after 72 Hours “No Growth” Results No Growth after 24 Hours No Growth to Date Medical Logic Module 

101. Expert Systems Hripcsak, et al., Ann. Int. Med., 1995 Clinical decision support system DXplain Button

102. Elhanan, et al., SCAMC 1997 Convert of test results to clinical findings Pass findings to DXplain Cholesterol Hypercholesterolemia Abnormalities of Serum Cholesterol Serum Serum Specimen Serum Cholesterol Test

107. Expert Systems Hripcsak, et al., Ann. Int. Med., 1995 Clinical decision support system DXplain Button

108. Proof of Concepts Merging data and application knowledge Smarter retrievals from the record “Just-in-Time” education Expert systems Data mining

109. Data Mining Wilcox and Hripcsak, SCAMC 1997

111. Data Mining Wilcox and Hripcsak, SCAMC 1997 Wilcox and Hripcsak, SCAMC 1998

112. Compare traditional coding methods with NLP to identify conditions in a set of patient records (x-ray reports) MethodSensSpec Laypersons 36% 86% Expert-coded cases27-37%95-98% ICD-9-coded cases12-29%86-90% Physicians 85% 98% NLP/MED/Rule-based 81% 98% Wilcox and Hripcsak, SCAMC 1998

113. Data Mining Wilcox and Hripcsak, SCAMC 1997 Wilcox and Hripcsak, SCAMC 1998

114. Proof of Concepts Merging data and application knowledge Smarter retrievals from the record “Just-in-Time” education Expert systems Data mining Database maintenance and use

115. Database Maintenance and Use Tables, columns, events all modeled in the MED Allows linkage of data model to controlled terminology Terminologies can be reused Impact of terminology changes on data model can be tracked

116. Proof of Concepts Merging data and application knowledge Smarter retrievals from the record “Just-in-Time” education Expert systems Data mining Database maintenance and use Terminology maintenance and use

117. Terminology Maintenance and Use Integrating terminologies from merging hospitals Automated update of medication terminology Detection of errors and inconsistencies

118. Proof of Concepts Merging data and application knowledge Smarter retrievals from the record “Just-in-Time” education Expert systems Data mining Database maintenance and use Terminology maintenance and use

119. Is it Worth the Trouble? Meed: noun 1 archaic : an earned reward or wage 2 : a fitting return or recompense Date: before 12th century Etymology: from Old English: MED

120. Summary Putting knowledge in your terminology gets you: –Better ways to get knowledge out of your EMR –Better ways to get knowledge out of resources –Better ways to use other knowledge bases –Bettter ways to use terminology –Better ways to manage applications –Better ways to manage data and terminology Representation scheme is less important Desiderata for controlled terminology

121. Desiderata Desirable qualities for terminology

122. Desiderata Desirable qualities for terminology “Go placidly amid the noise and haste, and remember what peace there may be in silence.” “I’d rather be sailing”