1 / 28

10,000 foot view of what I am working on

10,000 foot view of what I am working on. Wendy W. Chapman, PhD. Biomedical Language Understanding. University of Pittsburgh. Dept of Biomedical Informatics. Background. U of Utah. Wisconsin. U of Utah. U of Pittsburgh. 1992. 1994. 2000. 2003. BA Linguistics. Post-doc BMI.

giza
Download Presentation

10,000 foot view of what I am working on

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 10,000 foot view of what I am working on Wendy W. Chapman, PhD Biomedical Language Understanding University of Pittsburgh Dept of Biomedical Informatics

  2. Background U of Utah Wisconsin U of Utah U of Pittsburgh 1992 1994 2000 2003 BA Linguistics Post-doc BMI Faculty DBMI PhD Medical Informatics Chinese Literature

  3. Biomedical Language Understanding Henk Harkema, Danielle Mowery, Mike Conway, Lee Christensen, Qi Li, Wendy Chapman www.dbmi.pitt.edu/blulab

  4. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  5. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  6. Topaz Harkema, B Chapman, Hwa • Named entity recognition • Maps UMLS concepts to higher-level concepts • Contextual property assignment (ConText) • Existence (affirmed, negated) • Experiencer (patient, other) • Historicity (current, historical) • Realis (actual, non-specific/hypothetical) • Certainty (uncertain, certain) • Reason for exam (yes, no) • Quality of exam (diagnostic, limited)

  7. ConText: Determine Values for Contextual Properties Clinical condition: Cough Negation: Negated scope Patient deniescoughbut complains of headache. No change in the patient’s chest pain. trigger term termination term pseudo-trigger term

  8. ConText: Historical scope Past history of pneumoniapresenting today with cough and fever. termination term trigger term Clinical condition: Pneumonia Temporality: Historical

  9. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  10. Haug, Schleyer Onyx Eight mesial might have a slight translucency Semantic Models Syntactic Analyzer Onyx Semantic Analyzer Context-free Grammar Training Corpus At (translucency, numberEight) & surfaceOf (numberEight, mesial) & stateOf (translucency, possible)

  11. Knowledge-rich Frame-based Mapping Semantic Network Probabilistic Frames - Frame slots map to semantic network - Relationships between slots are probabilistic

  12. Annotation Interface Semantic Model with active learning and help from Onyx Templates

  13. Speech  NLP  Chart Dental Exams Number one Is missing. Two is fine. Caries on Tooth 3. Onyx Titus Schleyer, Lee Christensen, Peter Haug, Jeannie Irwin, Henk Harkema

  14. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  15. Ontology Development-Information Extraction (ODIE) Rebecca Crowley, Mayo Clinic, Stanford NCBO Information Extraction Use ontology to improve IE from text Ontology Text Ontology Enrichment Use IE to find new concepts and relationships to add Surgical Pathology Chest Radiography

  16. View Overlap of Ontologies

  17. Suggest Concepts

  18. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  19. Results Review/Error Analysis

  20. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  21. Schema for Clinical Condition Properties Wiebe, Jordan, Mowery, Harkema Properties of Condition Concept Existence Yes, No Experiencer Patient, Other Change Unmarked, Unchanging, Changing, Increasing, Decreasing, Improving, Worsening, Recurrence Intermittent Unmarked, Yes, No Certainty Unmarked, High, Moderate, Low Mental State Yes, No Generalized/ Conditional Yes, No Current Visit Relation Before, Meets_Overlaps, After

  22. Schema for Temporal Relations Time Words Points, Durations Ordering Words Precedes, During, Follows Aspectual Words Initiation, Continuation, Culmination

  23. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  24. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  25. Application Ontology for Syndromic Surveillance Consensus of developers/users across country Conway, Buckeridge

  26. BLU Lab NLP Sampler Topaz Onyx Ontology Enrichment Results Review/ Error Analysis Anaphoric Reference Temporality Schema NLP Repository Ontology For Syndromic Surveillance

  27. Anaphoric Reference in Clinical ReportsCrowey, Savova, Zeng • Adapted MUC schema for clinical reports • Three experts annotated 180 reports Five types—Mayo, UPMC • identity • part/whole • set/subset • Characterize anaphoric reference in reports • Train/test resolution algorithms

More Related