The Essentials of Knowledge Representation in Healthcare
Delve into the transformation of data into knowledge through terminologies, guidelines, and representations in healthcare. Understand the importance of correctly representing symbols and terminologic knowledge. Explore various knowledge-based terminology efforts and implementations.
The Essentials of Knowledge Representation in Healthcare
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Presentation Transcript
Cornerstone I: Representing Knowledge From Data to Knowledge Through Concept-Oriented Terminologies James J. Cimino
The first step on the path to knowledge is getting things by their right names. -Chinese saying
Overview • What is “data to knowledge”? • Knowledge representation choices • Knowledge-based terminology efforts • Medical Entities Dictionary • Proof of concepts
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
Knowledge Representation • Terminology for representing symbols • Format for arranging the symbols
Knowledge Representation Choices • Guideline implementation
Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline
Cholesterol 200 to 239 Cholesterol <200 Cholesterol >239 Cholesterol 200 to 239 HDL <35 or 2 Risks HDL >35, <2 Risks HDL >35, <2 Risks Provide dietary information Reevaluate in 2 years National Cholesterol Education Panel Guideline Measure Cholesterol & Assess Risk Factors
Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline • Three representations: • PROLOG (first-order logic)
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.
Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline • Three representations: • PROLOG (first-order logic) • CLASSIC (frames)
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)))))
Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline • Three representations: • PROLOG (first-order logic) • CLASSIC (frames) • CLIPS (production rules)
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”)
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”
Knowledge Representation Choices • Guideline implementation • Terminologic knowledge
Terminology Representation Choices • Frame-based
Frame-Based Representation Serum Glucose Test is-a: Lab Test Measures: Glucose Specimen: Serum Units: “mg/dl”
Terminology Representation Choices Terminology Representation Choices • Frame-based • Semantic network
Chemical Lab Test Body Substance is-a is-a is-a Glucose Serum specimen measures Semantic Network Representation Serum Glucose Test
Terminology Representation Choices Terminology Representation Choices • Frame-based • Semantic network • Conceptual graphs
Conceptual Graph Representation [Serum Glucose Test] - (is-a) -> [Lab Test] (measures) -> [Glucose] (specimen) -> [Serum]
Terminology Representation Choices Terminology Representation Choices • Frame-based • Semantic network • Conceptual graphs
Knowledge Representation Choices • Guideline implementation • Terminologic knowledge
Knowledge Representation • Terminology for representing symbols • Format for arranging the symbols • Terminology and format for representing terminologic knowledge
Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991
femur increased_uptake right site site_attr during bone_phase Jochen Bernauer, SCAMC, 1991 • Conceptual graphs to model findings
Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993
Rector, Nolan and Glowinski, SCAMC, 1993 • GALEN project conditions grammatically haveLocation bodyparts fractures sensibly haveLocation bones femurs sensiblyAndNecessarily haveDivision neck
Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993
Campbell and Musen, SCAMC, 1993 • Conceptual graphs and SNOMED • Pain + Chest + Radiation to + Left + Arm [Pain] - (located in) -> [Chest] (radiating to) -> [Arm] -> (with laterality) -> [Left]
Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993
Lexical group String String Lindberg, Humphreys, McCray, Methods 1993 • Unified Medical Language System Concept Lexical group String String
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
Rocha, Huff, et al., CBM, 1994 • VOSER • A server architecture for managing terminologic knowledege
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
Campbell, Cohn, Chute, et al., SCAMC 1996 • Convergent Medical Terminology • SNOMED/Kaiser/Mayo • Galapagos
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
Brown, O’Neil and Price, Methods, 1997 • Read Codes • Representation with GALEN model
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
Spackman, Campbell, and Côte, SCAMC 1997 • SNOMED RT (Reference Terminology) • Convergent Medical Terminology • Description Logic Format
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
Huff, Rocha, McDonald, et al., JAMIA 1998 • Logical Observations, Identfiers, Names and Codes (LOINC) 4764-5 | GLUCOSE^3H POST 100 G GLUCOSE PO | SCNC | PT | SER/PLAS | QN|
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
Drug Class International Package Identifiers is-a Not-Fully-Specified Drug is-a Ingredient Class is-a Clinical Drug is-a is-a is-a Composite Clinical Drug Trademark Drug is-a is-a Pharmacy System Knowledge Base Vendors Country-Specific Packaged Product Ingredient Manufactured Components Composite Trademark Drug
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
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)
MED Data Structures • Semantic network
Substance Laboratory Specimen Event Chemical Anatomic Substance Plasma Specimen Diagnostic Procedure Substance Sampled Plasma Laboratory Test Laboratory Procedure Has Specimen Carbo- hydrate Bioactive Substance CHEM-7 Part of Glucose Substance Measured MED Semantic Network Medical Entity Plasma Glucose
