1. 1 ODIE Toolkit NCBO Council Talk December 18, 2007 Rebecca Crowley crowleyrs@upmc.edu

2. Slide # 2/35 Outline Overview of the Project Aims, People, Organization, Domain, Philosophy Specific Aims from a use case approach Information Extraction Ontology Enrichment First steps, synergies, and year 1work, working together

3. Slide # 3/35 Project Overview Funded by National Cancer Center Develop tools for Information extraction from clinical text using ontologies Enrichment of ontologies using clinical text Project Period: 9/27/2007 – 7/31/2011 Collaboration with National Center for Biomedical Ontology Subcontract to Stanford (consultation on Bioportal) Subcontract to Mayo (Terminologies, NLP)

4. Slide # 4/35 Specific Aims Specific Aim 1: Develop and evaluate methods for information extraction (IE) tasks using existing OBO ontologies, including: 1.Named Entity Recognition 2.Co-reference Resolution 3.Discourse Reasoning 4.Attribute Value Extraction Specific Aim 2: Develop and evaluate general methods for clinical-text mining to assist in ontology development, including: 1.Preprocessing 2.Concept Discovery and Clustering 3.Suggest taxonomic positioning and relationships 4.Specific Aim 3: Develop reusable software for performing information extraction and ontology development leveraging existing NCBO tools and compatible with NCBO architecture. Specific Aim 4: Enhance National Cancer Institute Thesaurus Ontology using the ODIE toolkit. Specific Aim 5: Test the ability of the resulting software and ontologies to address important translational research questions in hematologic cancers. Year 1 development goals

5. Slide # 5/35 Dual Proposal Goals

6. Slide # 6/35 People @pitt Wendy Chapman, co-I Rebecca Crowley, PI Preet Chaudhary, co-I Kaihong Liu, Graduate Student Kevin Mitchell, Architect Girish Chavan, Interfaces John Dowling, Annotation

7. Slide # 7/35 Organization AnnotationsAlgorithmsArchitecture Rebecca Crowley Wendy Chapman Kaihong Liu John Dowling Rebecca Crowley Wendy Chapman Kaihong Liu Kevin Mitchell Rebecca Crowley Kevin Mitchell Girish Chavan Develop manually annotated sets for training and testing Consider and test existing algorithms; design, implement and test new algorithms Develop and implement architecture

8. Slide # 8/35 Domain Will attempt to develop general tools whenever possible Priorities for evaluation of components in : Radiology and pathology reports NCIT as well as other clinically relevant OBO ontologies Cancer domains (including hematologic oncology)

9. Slide # 9/35 Toolkit for developers of NLP applications and ontologies Support interaction and experimentation Package systems at the conclusion of working with ODIE Foster cycle of enrichment and extraction needed to advance development of NLP systems Ontology enrichment as opposed to denovo development Human-machine collaboration as opposed to fully automated learning Philosophy

10. Slide # 10/35 Specific Aims Specific Aim 1: Develop and evaluate methods for information extraction (IE) tasks using existing OBO ontologies, including: 1.Named Entity Recognition 2.Co-reference Resolution 3.Discourse Reasoning 4.Attribute Value Extraction Specific Aim 2: Develop and evaluate general methods for clinical-text mining to assist in ontology development, including: 1.Preprocessing 2.Concept Discovery and Clustering 3.Suggest taxonomic positioning and relationships 4.Specific Aim 3: Develop reusable software for performing information extraction and ontology development leveraging existing NCBO tools and compatible with NCBO architecture. Specific Aim 4: Enhance National Cancer Institute Thesaurus Ontology using the ODIE toolkit. Specific Aim 5: Test the ability of the resulting software and ontologies to address important translational research questions in hematologic cancers. Key ODIE Functionality

11. Slide # 11/35 Named Entity Recognition User has clinical documents one or more ontology (and/or) one or more lexical resources (synonyms, POS) (optionally) a reference standard of human annotations User wants to determine degree of coverage of different ontologies with text determine degree of overlap in annotations generated between ontologies (optionally) test accuracy of NER with different ontologies to choose ‘best’ ontology to annotate text with tag existing document set with concepts from ontology (optionally using the synonyms from their synonym source if not in ontology) System produces annotated clinical documents and descriptive statistics

12. Slide # 12/35 Named Entity Recognition Clinical Document OntologyLexical Resource Metathesaurus (synonyms) SPECIALIST (POS information)

13. Slide # 13/35 Named Entity Recognition View Annotated Concepts From A Single Ontology

14. Slide # 14/35 Named Entity Recognition Compare Annotations from Multiple Ontologies

15. Slide # 15/35 Co-reference Resolution User has clinical documents with NER annotations one or more ontology (optionally) a reference standard of co-reference annotations User wants to visualize co-references detected using one or more ontologies (optionally) test accuracy of CR with different ontologies to choose ontology for annotations tag existing document set with co-references from ontology System produces annotated clinical documents and descriptive statistics

16. Slide # 16/35 Co-reference Resolution

17. Slide # 17/35 Discourse Reasoning User has a set of clinical documents with NER and CR annotations a set of information models about those documents User wants to determine which information model (or parts of them) should be used for which clinical document

18. Slide # 18/35 Discourse Reasoning BRAIN, RIGHT PARIETAL, STEROTACTIC BIOPSY: Mucinous Adenocarcinoma, consistent with previous history of colon primary BRAIN Site Morphology COLON Location Grade Size TNM Stage

19. Slide # 19/35 Attribute Value Extraction User has clinical documents with NER, CR, DR annotations information model of specific subset of documents Wants to extract attributes and value from clinical text conforming to model Analyze data using common tools possible later search for particular cases

20. Slide # 20/35 Attribute Value Extraction Histologic Type Clark’s Level Breslow Depth Mitoses Ulcer Perineural Invasion Angiolymphatic Invasion Regression

21. Slide # 21/35 Attribute Value Extraction Histologic Type – Superficial Spreading Clark’s Level – IV Breslow Depth – 1.75 mm Mitoses – Greater than 2 per HLP Ulcer – None Perineural Invasion – None Angiolymphatic Invasion – None Regression - None

22. Slide # 22/35 Ontology Enrichment User has clinical documents Ontology User wants to identify potential candidate concepts from the documents to include in the ontology Visualized in a manner to ease search and recognition of presence of absence of those concepts in the ontology Suggestions for where in taxonomy the concept should be placed Suggestions for relationships

23. Slide # 23/35 Ontology Enrichment Breast, Left, Excisional Biopsy: Mucinous Carcinoma Breast, Right, Lumpectomy: Infiltrating Ductal Carcinoma Breast, Left: Invasive Ductal Carcinoma Breast, Left, Excisional Biopsy: Malignant Phylloides Tumor Tumor shows osseous and lipomatous metaplasia Ductal Breast Carcinoma Breast Carcinoma Malignant Breast Neoplasm Breast Neoplasm Breast Disorder Disease or Disorder Invasive Ductal Carcinoma

24. Slide # 24/35 Concept Discovery Breast, Left, Excisional Biopsy: Mucinous Carcinoma Breast, Right, Lumpectomy: Infiltrating Ductal Carcinoma Breast, Left: Invasive Ductal Carcinoma Breast, Left, Excisional Biopsy: Malignant Phylloides Tumor Tumor shows osseous and lipomatous metaplasia Ductal Breast Carcinoma Breast Carcinoma Malignant Breast Neoplasm Breast Neoplasm Breast Disorder Disease or Disorder Invasive Ductal Carcinoma

25. Slide # 25/35 Taxonomic Positioning Breast, Left, Excisional Biopsy: Mucinous Carcinoma Breast, Right, Lumpectomy: Infiltrating Ductal Carcinoma Breast, Left: Invasive Ductal Carcinoma Breast, Left, Excisional Biopsy: Malignant Phylloides Tumor Tumor shows osseous and lipomatous metaplasia Ductal Breast Carcinoma Breast Carcinoma Malignant Breast Neoplasm Breast Neoplasm Breast Disorder Disease or Disorder Invasive Ductal Carcinoma Mucinous Carcinoma Malignant Phylloides Tumor

26. Slide # 26/35 Relationships Breast, Left, Excisional Biopsy: Mucinous Carcinoma Breast, Right, Lumpectomy: Infiltrating Ductal Carcinoma Breast, Left: Invasive Ductal Carcinoma Breast, Left, Excisional Biopsy: Malignant Phylloides Tumor Tumor shows osseous and lipomatous metaplasia Ductal Breast Carcinoma Breast Carcinoma Malignant Breast Neoplasm Breast Neoplasm Breast Disorder Disease or Disorder Invasive Ductal Carcinoma Mucinous Carcinoma Malignant Phylloides Tumor has-Finding Metaplasia Osseous metaplasia Lipomatous metaplasia Cartilageous metaplasia Morphologic Finding

27. Slide # 27/35 First Steps Use cases Survey of Bioportal, LexBio, GATE and UIMA Survey of ontology enrichment techniques Architectural assumptions and notional architecture Started discussions with Stanford and Mayo Delineated first year work Annotation software and document sets

28. Slide # 28/35 Architecture Decisions The primary goal of ODIE is to serve as a workbench for building and refining text processing pipelines and ontologies. Information retrieval is not a primary goal. However ODIE may have a rudimentary search feature for annotated document collections. ODIE Toolkit will be a desktop application. ODIE UI will be based on the Eclipse Rich Client Platform. ODIE will use UIMA as the Language Engineering Platform. GATE processing resources will be usable in ODIE by wrapping them in UIMA TAEs. UIMA is highly configurable using xml descriptor files. Better documentation, community support. We will use GATE in first year for rapid prototyping and manual annotation ODIE will have the ability to easily import and use UIMA TAEs developed by others. This may be expanded to GATE processing resources. ODIE will allow for packaging a pipeline for deployment in a production environment.

29. Slide # 29/35 Notional Architecture

30. Slide # 30/35 Synergies: Ontrez Ontrez ODIE Information Retrieval Range of inputs Other kinds of annotation Information Extraction Ontology Enrichment Clinical Documents Annotation Named Entity Recognition Enhance annotation of Ontrez? Use inference and indexing on clinical documents?

31. Slide # 31/35 Synergies: Mayo NER and Co-reference resolution Clustering, discovery of synonyms LexGrid Using similar tools, focused on larger range of document types More – to be explored

32. Slide # 32/35 First Year Work NER and co-reference modules Concept discovery Develop manually annotated reference standards for NER and CR Focus on testing and developing algorithms ODIE 1.0 will include basic architecture and modules for NER, CR and concept discovery, statistics

33. Slide # 33/35 Working Together Work with Mayo to scope first year collaboration (NER, CR, synonym discovery) Decisions regarding terminology access Better define what NCBO resources we will use

34. Slide # 34/35 Working Together SourceForge site, ODIE website and Wiki All our meetings are open and we are happy to arrange teleconferences Mondays 2-4 pm (EST) Schedule visits with Mayo and Stanford for early spring ’08 Anticipate providing monthly progress updates at the ODIE website starting in January ‘08 Other ideas? What’s the expectation of the Council?

35. 35 Questions? Comments?