Rebecca Crowley crowleyrs@upmc.edu
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 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
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 Funded by National Cancer Center
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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)
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Year 1 development goals
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.
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Wendy Chapman, co-I
Rebecca Crowley, PI
Preet Chaudhary, co-I
Kaihong Liu, Graduate Student
Kevin Mitchell, Architect
Girish Chavan, Interfaces
John Dowling, Annotation
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Annotations
Develop manually annotated sets for training and testing
Rebecca Crowley
Wendy Chapman
Kaihong Liu
John Dowling
Algorithms Architecture
Consider and test existing algorithms; design, implement and test new algorithms
Develop and implement architecture
Rebecca Crowley
Wendy Chapman
Kaihong Liu
Kevin Mitchell
Rebecca Crowley
Kevin Mitchell
Girish Chavan
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 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)
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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
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Key ODIE Functionality
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.
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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
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Clinical Document
Reproductive System Neoplasm Neoplasm
Prostate Neoplasm
Malignant Prostate Neoplasm
Malignant Prostate Neoplasm
Invasive Prostate Carcinoma
Prostatic adenocarcinoma
Ontology Lexical Resource
Metathesaurus (synonyms)
SPECIALIST (POS information)
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View Annotated Concepts From A Single Ontology
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Compare Annotations from Multiple Ontologies
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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
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Reproductive System Neoplasm Neoplasm
Prostate Neoplasm
Malignant Prostate Neoplasm
Malignant Prostate Neoplasm
Invasive Prostate Carcinoma
Prostatic adenocarcinoma
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 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
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BRAIN, RIGHT PARIETAL, STEROTACTIC BIOPSY:
Mucinous Adenocarcinoma, consistent with previous history of colon primary
BRAIN
Site
Morphology
COLON
Location
Grade
Size
TNM Stage
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 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
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Histologic Type
Clark’s Level
Breslow Depth
Mitoses
Ulcer
Perineural Invasion
Angiolymphatic Invasion
Regression
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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
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Regression - None

 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
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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
Disease or Disorder
Breast Disorder
Breast Neoplasm
Malignant Breast Neoplasm
Breast Carcinoma
Ductal Breast Carcinoma
Invasive Ductal Carcinoma
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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
Disease or Disorder
Breast Disorder
Breast Neoplasm
Malignant Breast Neoplasm
Breast Carcinoma
Ductal Breast Carcinoma
Invasive Ductal Carcinoma
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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
Disease or Disorder
Breast Disorder
Breast Neoplasm
Malignant Breast Neoplasm
Breast Carcinoma
Ductal Breast Carcinoma
Invasive Ductal Carcinoma
Mucinous Carcinoma
Malignant Phylloides Tumor
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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
Disease or Disorder
Breast Disorder
Breast Neoplasm
Malignant Breast Neoplasm
Breast Carcinoma
Ductal Breast Carcinoma
Invasive Ductal Carcinoma
Mucinous Carcinoma
Malignant Phylloides Tumor has-Finding
Morphologic Finding
Metaplasia
Osseous metaplasia
Lipomatous metaplasia
Cartilageous metaplasia
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 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
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The primary goal of ODIE is to serve as a workbench for building and refining text processing pipelines and ontologies.
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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.
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• Information Retrieval
• Range of inputs
Ontrez
ODIE
• Annotation
• Named Entity Recognition
• Enhance annotation of Ontrez?
• Use inference and indexing on clinical documents?
• Other kinds of annotation
• Information Extraction
• Ontology Enrichment
• Clinical Documents
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 NER and Co-reference resolution
 Clustering, discovery of synonyms
 LexGrid
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Using similar tools, focused on larger range of document types
More – to be explored
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 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
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 Work with Mayo to scope first year collaboration (NER, CR, synonym discovery)
 Decisions regarding terminology access
 Better define what NCBO resources we will use
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 SourceForge site, ODIE website and Wiki
 All our meetings are open and we are happy to arrange teleconferences
 Mondays 2-4 pm (EST)
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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?
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