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Supporting Non-NLP Experts in Creating Annotation Schemas for Extraction of Clinical Concepts 

Supporting Non-NLP Experts in Creating Annotation Schemas for Extraction of Clinical Concepts . Wendy W. Chapman, PhD. Department of Biomedical Informatics University of Utah. Motivation. Common information models Enable interoperability Guide non-NLP experts in developing NLP apps.

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Supporting Non-NLP Experts in Creating Annotation Schemas for Extraction of Clinical Concepts 

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  1. Supporting Non-NLP Experts in Creating Annotation Schemas for Extraction of Clinical Concepts  Wendy W. Chapman, PhD Department of Biomedical Informatics University of Utah

  2. Motivation Common information models • Enableinteroperability • Guide non-NLP experts in developing NLP apps

  3. Creating Domain Knowledge Bases for NLP User Domain Knowledge Base Schema Ontology Knowledge Author Domain Ontologies Modifier Ontology

  4. Creating Domain Knowledge Bases for NLP User Domain Knowledge Base Schema Ontology Knowledge Author Domain Ontologies Modifier Ontology

  5. Schema Ontology Semantic types (from cTAKES type system) • Allergy • Disease/Disorder • Encounter • Finding • Medication • Procedure/Intervention • Research Activity • Sign/ Symptom • Social History • Test

  6. Schema Ontology: Elements

  7. Schema Ontology: Relationships

  8. Creating Domain Knowledge Bases for NLP User Domain Knowledge Base Schema Ontology Knowledge Author Domain Ontologies Modifier Ontology

  9. Modifier Ontology Allowable modifiers For each clinical element Modifiers are important for interpreting text • Chest radiograph confirms pneumonia • Family history of pneumonia • No evidence of pneumonia

  10. Modifier Ontology Semantic Modifiers Linguistic Modifiers Negation Uncertainty Conditional Experiencer Future Historical • Dosage • Duration • Form • Frequency • Route • Status Change • Anatomic location • …

  11. Modifier Ontology • Value Sets • NegEx and ConText lexicon • Value sets from existing ontologies/vocabularies • Labels for different languages • English • Swedish • German • French

  12. ACTION: Forward CLOSURE: Because_Group Backward Patient_GroupACTION: Forward CLOSURE: Because_Group Backward Patient_Group Bidirectional But_Group Terminate SecondaryTo_Group Which_Group CATEGORY: DefiniteExistence ITEMS: Although DefiniteNegativeExistence As a part from Equivocality As a cause for Experiencer (n ~400) Future Historical Indication ProbableExistence ProbableNegatedExistence PseudoExperiencer PseudoFuture PseudoHistorical PseudoNegation ConText Elements

  13. Modifier Ontology Types of modifiers Linguistic expressions Actions Translations

  14. Modifier Ontology: Elements

  15. Schema Ontology Imports Modifier Ontology Medications • Type • Dose • Frequency • Route Diagnosis • Negation • Uncertainty • Severity • History • Experiencer

  16. Creating Domain Knowledge Bases for NLP User Domain Knowledge Base Schema Ontology Knowledge Author Domain Ontologies Modifier Ontology

  17. Domain Knowledge Base for NLP Instance of schema ontology Clinical elements from a particular domain

  18. Synonyms Misspellings Regular expressions

  19. Creating Domain Knowledge Bases for NLP User Domain Knowledge Base Schema Ontology Knowledge Author Domain Ontologies Modifier Ontology

  20. Map NLP Output to User KB Controlled Vocabs Dry cough Productive cough Cough Hacking cough Bloody cough User’s Concepts Cough Dyspnea Infiltrate on CXR Wheezing Fever Cervical Lymphadenopathy Which concepts?

  21. Attribute-values Temp 38.0C Low-grade temperature User’s Concepts Cough Dyspnea Infiltrate on CXR Wheezing Fever Cervical Lymphadenopathy What values?

  22. “Family history of colon cancer” Knowledge Author Disease: colon cancer Experiencer: family Negation: no Historical: yes NLP Schema Domain Ontology

  23. Knowledge Author B Scuba, F Fana, Liqin Wang, Mingyuan Zhang, Y Liu, M Kong, F Drews • Front end interface for users • Back end • Schema ontology • Modifier ontology • Output • Domain ontology • Schema for NLP system

  24. African American Adult Questions | Discussion

  25. Ibuprofen

  26. Ibuprofen p.o.

  27. No family history of colon cancer Linguistic modifiers

  28. Suggests synonyms

  29. Acknowledgments BLU Lab Collaborators • Lee Christensen • Melissa Tharp • Mike Conway • Danielle Mowery • Bill Scuba • Milan Kovacevich • Dieter Hillert • Samir Abdelrahman • Leah Willis • Bob Angell • Harry Hochheiser • Jan Wiebe • Rebecca Hwa • Guergana Savova • Noemie Elhadad • Michael Matheny • Rob El-Kareh • Ruth Reeves • Qing Zeng • Guy Divita • Frank Drews • SumithraVellupilai • Maria Kvist • Maria Skeppstedt • AronHenrikkson • Brian Chapman • David Carrell • Sascha Dublin • Zia Agha • StephaneMeystre • Scott DuVall • Jianlin Shi

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