1. I nformation Extraction from Radiology Reports: System Design and Implementation Information Model System Architecture – UIMA Automatic Report Segmentation NER Negation Discovery Coreference Resolution, Relationship Discovery, Inference

2. Information Schemas DICOM SR AIM (Information and Image Markup)

6. Coding Scheme - Code Value - Description UMLS-2008AA,C0006141, Breast [Body Part, Organ, or Organ Component].

8. Overall System Design

9. Automatic Report Segmentation

10. SVM classifier achieved accuracy of over 0.9

11. NER Image ROI and Image Referents Discovery Imaging Observations and Characteristics Imaging Procedure Body Parts and Organs Findings and Abnormalities Persons, Dates, Times

12. Image and Image ROIs Referents

13. NER – OBA and MetaMap

16. Negation Discovery

17. The NegEx Algorithm A rule based system for the discovery of negation of findings and diseases in discharge summaries. A list of 35 negation phrases - negations preceding a term (e.g. not signs of, no evidence of, negative for ), negations following a term (e.g. declined, unlikely ), and what they refer to as ‘pseudo negations’ - false negations triggers such as double negatives or ambiguous negations (e.g. not necessarily, not rule out, not certain wether ). A 5-token window around the negation cue

18. BioScope Clinical free-texts (radiology reports), biological full papers, and biological paper abstracts from the GENIA corpus. Minimal retrocardiac opacity, likely atelectasis. Normal chest x-ray without radiographic evidence of residual bronchopulmonary dysplasia.

19. Coreference Resolution Coreference resolution is the process of determining whether two expressions in natural language refer to the same entity in the world. The largest lymph node is inferiorly positioned in the level IV and measures 29 mm in diameter. Just superior to this, there is a necrotic lymph node measuring 16 mm in size.

20. Relationship Discovery The goal of relationship extraction is to detect occurrences of a prespecified type of relationship between a pair of entities of given types. Associations between an imaging observation and imaging observation characteristics. Between an imaging observation and a body part or organ. Between imaging observations/characteristics and inferred diagnosis. Spatial relationships.

21. Inference Module Domain specific inference module that would have the ability to fill in gaps in the relationships between the named entities present in the report. The inference module will also be used to validate the output of both named entity and relationship discovery modules.

22. Q/A