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Ontology-based Diagnostic Decision Support for Radiology Charles E. - PDF document

MIE 2014 Ontology-based Diagnostic Decision Support for Radiology Charles E. Kahn, Jr., MD, MS Medical College of Wisconsin Milwaukee, Wisconsin, USA Goals Apply an ontology of disorders and associated imaging findings for differential


  1. MIE 2014 Ontology-based Diagnostic Decision Support for Radiology Charles E. Kahn, Jr., MD, MS Medical College of Wisconsin Milwaukee, Wisconsin, USA

  2. Goals • Apply an ontology of disorders and associated imaging findings for differential diagnosis • Develop user interface and web service API for knowledge query • Demonstrate valid application of the ontology’s knowledge

  3. Radiology Gamuts Ontology (RGO) • Large and growing knowledge model ▫ Diseases ▫ Imaging observations • Constructed from several differential diagnosis references • Serves as a form of "computable knowledge" to aid in radiological diagnosis

  4. Ontology Structure • Entity ▫ disorder chronic hepatitis ▫ intervention steroid therapy ▫ observation misty mesentery • Relation ▫ subsumption (“is a”) ▫ causality (“may cause”)

  5. Radiology Gamuts Ontology • Standardized knowledge representation ▫ Normalized entity names + synonyms ▫ Ability to reason over domain • Interoperability / knowledge integration ▫ Mappings to other ontologies ▫ Knowledge-based applications

  6. Gamuts Ontology • Content ▫ 16,822 terms ▫ 1,755 subsumption relations ▫ 53,638 causal relations • User interface ▫ Web site ( www.gamuts.net ) ▫ RESTful web service / JSON

  7. Imaging findings and diseases Causal relationships gamuts.net

  8. Differential Diagnosis (“DDx”) • Identify the cause(s) of a set of findings ▫ Each diagnosis explains all imaging findings that have been asserted • “Discriminators” ▫ Union of findings of each listed diagnosis • exclude asserted findings • exclude findings common to all listed diagnoses ▫ Additional findings that can reduce the DDx list

  9. DDx Example • Finding ▫ hypertelorism • Diagnosis ▫ 151 diagnoses ▫ “Aarskog syndrome” to “XXXY syndrome” • Discriminators ▫ 976 discriminators ▫ including acro-osteolysis, abnormal sternum, clubfoot, tracheomalacia, Wormian bones

  10. DDx Example: 2nd finding • Findings ▫ hypertelorism ▫ abnormal sternum • Diagnosis (disorders that cause both findings) ▫ Brachmann-de Lange syndrome ▫ Rubinstein-Taybi syndrome ▫ cleidocranial dysostosis ▫ Seckel syndrome ▫ Noonan syndrome ▫ XXXXY syndrome • Discriminators

  11. DDx Example: 3rd finding • Findings ▫ hypertelorism ▫ abnormal sternum ▫ Wormian bones • Diagnosis (disorders that cause all 3 findings) ▫ cleidocranial dysostosis • Discriminators ▫ (none)

  12. Gamuts DDx • User interfaces ▫ Web site ( gamuts.net/ddx ) ▫ Web service (see gamuts.net/dev )

  13. gamuts.net/ddx

  14. gamuts.net/ddx

  15. "finding": [ "hypertelorism", "Wormian bones" ], "diagnosis": [ "aminopterin fetopathy", "cleidocranial dysostosis", "metaphyseal chondrodysplasia Jansen type", "normal variant", "Ritscher-Shinzel syndrome", "Schinzel-Giedion syndrome", "sclerosteosis" ], "discriminator": [ { "name": "abnormal fibula", "n": "1" }, { "name": "widespread predominantly medullary osteosclerosis", "n": "1" }

  16. Mappings • RadLex • SNOMED CT • OrphaNet Rare Disease Ontology (ORDO) • Bone Dysplasia Ontology (BDO)

  17. Conclusions • Working system for differential diagnosis • Valid application of diagnostic knowledge • Requires diagnoses to cause all asserted findings

  18. Future Work • Test on clinical cases • Apply “transitive closure” over ontology • Integrate with NLP tools • Incorporate probabilistic knowledge + reasoning

  19. Thank you !

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