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MoST: A System To Semantically Map Clinical Model Data to SNOMED-CT

Semantic Mining Conference on SNOMED-CT Oct 1-3 2006. MoST: A System To Semantically Map Clinical Model Data to SNOMED-CT. Rahil Qamar, Alan Rector Medical Informatics Group Department of Computer Science University of Manchester Manchester, U.K. qamarr@cs.man.ac.uk , rector@cs.man.ac.uk.

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MoST: A System To Semantically Map Clinical Model Data to SNOMED-CT

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  1. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 MoST: A System To Semantically Map Clinical Model Data to SNOMED-CT Rahil Qamar, Alan Rector Medical Informatics Group Department of Computer Science University of Manchester Manchester, U.K. qamarr@cs.man.ac.uk, rector@cs.man.ac.uk

  2. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 Outline • Background • Clinical Models – Archetype Models • Clinical Terminologies - SNOMED-CT (need for further explanation??) • Lexical and Semantic Mapping • The Model Standardisation using Terminology (MoST) System • Results • Issues

  3. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 BACKGROUND .. (1) • Data standards are basic building blocks for achieving data interoperability. • Data interoperability enables information systems to be interoperable. • Interoperable information systems are vital for reducing medical errors and increasing care efficiency. So, we need systems that can produce and work with standardised data. This means we first need to map data to some standard terminology!

  4. Health Data Repository Terminology Files Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 BACKGROUND .. (2) • DATA SOURCES • HL7 V3 Messages • Templates (Data-entry forms) • Archetypes • Narratives • EHRs • Medical documents • ……. • TERMINOLOGY SOURCES • SNOMED-CT • GALEN • ICD9, ICD10, … • LOINC • GO • ….

  5. Middleware Applications Establish references Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 BACKGROUND .. (2) • TERMINOLOGY SOURCES • SNOMED-CT • GALEN • ICD9, ICD10, … • LOINC • GO • …. • DATA SOURCES • HL7 V3 Messages • Templates (Data-entry forms) • Archetypes • Narratives • EHRs • Medical documents • ……. Standardised Health Data Repository Terminology Files

  6. Middleware Applications Establish references Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 BACKGROUND .. (2) • TERMINOLOGY SOURCES • SNOMED-CT • GALEN • ICD9, ICD10, … • LOINC • GO • …. • DATA SOURCES • HL7 V3 Messages • Templates (Data-entry forms) • Archetypes • Narratives • EHRs • Medical documents • ……. Standardised Health Data Repository Terminology Files

  7. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 Archetype Models • Computable expressions of a domain content model. • Expressions in the form of structured constraint statements inherited from openEHR Reference Model. openEHR Archetype Models Taken from the Ocean Informatics website : http://www.oceaninformatics.biz/

  8. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 Example : Barthel Index Archetype Model Java Archetype Editor developed at Department of Biomedical Engineering, Linkoping University, Sweden

  9. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 Barthel Index – Terminology Section From SNOMED CT Jan 2006

  10. Term Mapped to SNOMED Code Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 Autopsy Examination – Terminology Section

  11. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 Autopsy Examination – Terminology Section with enhanced intelligence - ‘intended meaning’ SNOMED CT Categories Jan 2006

  12. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 The Model Standardisation using Terminology (MoST) System METHODOLOGY Clinical Data Model E.g.: Archetypes or HL7 v3 messages Generalised hierarchy E.g. Archetype in MoST XML Model transformation input PRE-FILTERED RESULTS POST-FILTERED RESULTS Non-context Methods (1) EMT-P, UMLS, and Lexical Lookup Context Methods (2) UMLS, and Training dataset Lookup Filter Methods (Results from 1 and 2 get filtered for semantic appropriateness) output UMLS GATE/ WordNet Training Dataset SNOMED-CT Intended meaning document (IM Doc) Candidate Mappings Candidate terms with metadata

  13. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 PRE-FILTERED RESULTS • SNOMED-CT results prior to being filtered Archetype Terms  SNOMED-CT Concepts (1..1 matches) • 50 archetype terms did not find any match in SNOMED resulting in an overall high recall value of 89.47% • A manual inspection by the clinical modelers gave an estimated precision of 82.35%. Good value but not reliable at this point!

  14. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 POST-FILTERED RESULTS • SNOMED-CT results after being filtered Archetype Terms  SNOMED-CT Concepts (1..1 matches) • Of the 425 SNOMED codes only 385 were found to be relevant by the MoST system resulting in a precision of 90.58%. • The precision improved as most of the irrelevant results were eliminated resulting in a smaller but better final result set. • This result set was displayed to the clinical modeler as candidate mappings.

  15. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 RESULTS OVERVIEW Processing time : 60 sec + 30 sec without Spell Check Processing time : 70 sec + 40 sec with Spell Check (GSpell)

  16. SNOMED-CT ARCHETYPE Pre-filtered Candidates Post-filtered Candidates Autopsy Examination Body Structure Body Structure has_part Is_a Is_a Internal Examination Body system structure Body system structure Is_a System Respiratory system structure Respiratory system structure has_part Is_a Respiratory System Initial mapping Entire respiratory system Final candidate mapping Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 FILTERING PROCESS .. (1) Rule 1: If one input concept subsumes another then the subsuming concept is selected. Example from autopsy examination archetype for term ‘Respiratory System’

  17. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 FILTERING PROCESS .. (2) • Rule 2: If the input concepts are ‘disjoint’ with no common subsuming concept then all disjoint concepts are selected. Example from blood gas assessmentarchetype for archetype term ‘pH’ . Filtering input = {Hydrogen ion concentration, Past history of, ph+, pH measurement arterial} Hydrogen ion concentration (observable entity) is_a Fluid observable Past history of (context-dependent category) is_a Context-dependent categories ph+ (qualifier value) is_a skin reaction grades pH measurement arterial (procedure) is_a pH measurement From SNOMED-CT Jan 2006 Output = {Hydrogen ion concentration, Past history of, ph+, pH measurement arterial}

  18. SNOMED-CT ARCHETYPE Blood gas assessment SNOMED-CT Categories -> body structure -> finding -> disorder -> procedure -> observable entity -> morphologic abnormality -> .. has_part Arterial has_part pH Intended meaning SNOMED-CT ARCHETYPE Blood gas assessment Procedure Observable Entity Qualifier Value Is_a Is_a Is_a Laboratory Procedures Feature of entity Grading values has_part Arterial Laboratory Test Fluid Observable Skin reaction grades has_part Is_a Is_a Eval of acid-base balance Biochemical Test Hydrogen ion concentration ph+ pH Is_a Is_a pH measurement Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 FILTERING PROCESS .. (3) Rule 3: All results are filtered using the SNOMED categories stated in the IM Doc. Archetype term descriptions are also considered.

  19. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 ISSUES .. (1) • Difficult to find SNOMED matches for all archetype terms • Archetype modelers do not always have SNOMED in mind when modeling • No clear guidelines for categorising archetypes – Observation, Act, Evaluation, Instruction • Cannot be strict about categories in archetypes and their inheritance by child nodes. E.g. Observation archetype ‘Complete blood picture’ has ‘Haemoglobin’ with IM ‘finding’ or ‘procedure’. • SNOMED categories are not Gold Standard.

  20. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 ISSUES .. (2) • Ambiguous Intended Meaning E.g.: (1) Blood film archetype Complete blood picturehasElement haemoglobin (hasDescriptionmass concentration of haemoglobin) Intent: Haemoglobin (substance) or haemoglobin concentration (procedure)? E.g. (2) Autopsy Examination archetype Internal examinationhasElement SystemhasElement Gastro-intestinal system(hasDescriptionFindings about oesophagus, peritoneum, bowel, liver (including gallbladder) and omentum) Intent: Gastro-intestinal system (body structure) or findings in gastro-intestinal system (findings)?

  21. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 ISSUES .. (3) • Post-coordination • Needs help but can do about half unaided ->Dressing/undressing (Barthel Index) • Independent (on & off, dressing, wiping) -> Toilet Use (Barthel Index) • Grimace and cough/sneeze during airways suction -> Reflex response (Reflexes) • Occupation risk if drowsy -> Alert (Alert) • Reflex possibly present - markedly reduced -> Biceps (Reflexes) Is it possible to post coordinated these terms with some degree of clinical sense!

  22. Semantic Mining Conference on SNOMED-CT Oct 1-3 2006 Discussion • Methodology is scalable. Theoretically, can be applied to other data models and terminologies. E.g. HL7 v3 messages. • It is not sufficient to have lexical lookups to determine appropriate mappings. Semantic procedures are also needed. • Reliable external resources should be used wherever possible to augment the performance of the system. • Intelligence added to middleware such as IM’s also help improve performance significantly and can alter results.

  23. Thank you. Questions? Rahil Qamar, Alan Rector Medical Informatics Group Department of Computer Science University of Manchester Manchester, U.K. qamarr@cs.man.ac.uk, rector@cs.man.ac.uk

  24. Semantic Results • Blood gas assessment PvO2 (context: Venous) -> Mixed venous oxygen concentration measurement (procedure) (250558000) -> Measurement of venous partial pressure of oxygen (procedure) (250547009) -> Measurement of mixed venous partial pressure of oxygen (procedure) (167027009) • Visual acuity Perceive light (context: Visual acuity) -> Visual acuity perception of light - inaccurate projection (finding) (264943005) -> Visual acuity, no light perception (finding) (63063006) -> Visual acuity perception of light - accurate projection (finding) (264944004)

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