1. BioNLP, Information Extraction from Radiology Reports
Emilia Apostolova
College of Computing and Digital Media
DePaul University

2. BioNLP – conferences and shared tasks
Pacific Symposium on Biocomputing
Intelligent Systems for Molecular Biology
Association for Computational Linguistics
North American Association for Computational Linguistics
BioNLP
BioCreative
TREC Genomics
IClef

3. Information Extraction (in BioMedicine)
The NLP Pipeline
Lexical Analysis – tokenization, morphological analysis, linguistic lexicons.
Syntactic Analysis – Part of Speech Tagging, Chunking, Parsing.
Semantic Analysis – Lexical Semantic Interpretation, Semantic Interpretation of Utterances.

4. NLP Pipeline Frameworks
GATE - General Architecture for Text Engineering.
Apache UIMA - Unstructured Information Management Application.
Geneways - a system for automatically extracting, analyzing, visualizing and integrating molecular pathway data from the research literature.
PASTA - Protein Structures and Information Extraction from Biological Texts.

5. Lexical Analysis - Tokenization
Segmenting text into linguistic tokens – words and sentences.
Abbreviations - The Study was conducted within the U.S.
Apostrophes - IL-10's cytokine synthesis inhibitory activity
Hyphenation - co-operate, cooperate
Multiple formats: 464, and
Sentence boundary detection - :, ;, -

6. Lexical Analysis – Morphological analysis
Link surface variants of a lexical element to its canonical base form. E.g. inflections (activat-es, activat-ed, activat-ing), derivations (activation).
Porter stemmer – lexicon-free approach. Finds longest match of a word to a a list of English derivational and inflectional suffixes.
Two-level morphology – a finite state based approach that applies a series of parallel transducers to input tokens. (fly -> flies)

7. Syntactic Level – Part of Speech Tagging
activation – POS noun, singular
activate – POS verb, present non-3d person singular
active – POS adjective
report?

8. Syntactic Level - Parsing
A natural language parser is a program that works out the grammatical structure of sentences, for instance, which groups of words go together (as "phrases") and which words are the subject or object of a verb.
The Stanford Dependency Parser - a Java implementation of probabilistic natural language parsers, trained on the Penn Treebank.

9. Semantic Level – Lexical Interpretation
Selectional Restrictions:
transitive verbs: inhibit [something], transcribe [something]
semantic restrictions: inhibit [Process], transcribe [Nucleic Acid]
Syntactically admissible, but semantically invalid:
to inhibit amino acids
to transcribe cell growth

10. Discourse Level - Pragmatics
Discourse referents; what entities does a given message refer to?
What background knowledge is needed to understand a given message?
How do the beliefs of speaker and hearer interact in the interpretation of a message?
What is a relevant answer to a given question?
Summarization, Translation, Dialog Systems, Natural Language Generation.

11. Lexical resources for (Bio)NLP
Princeton Wordnet
NLM UMLS lexicon and metathesaurus.
The Open Biomedical Ontologies

12. Text and Image Integration

13. Automatic Image Annotation

14. Automatic Image Annotation
Where? Woman (Population Group), Right breast (Body Part, Organ, or Organ Component)‏
How? Mammography (Diagnostic Procedure)‏
What? Calcification (Pathologic Function), Lesion (Finding), Carcinoma, Papillary (Neoplastic Process)‏

15. IE from Clinical Texts – Radiology and Pathology Reports
Northwestern University Medical School
Department of Radiology
Imaging Informatics

16. Radiology Reports

17. Sample Radiology Report
Patient Name: XXXXXXX, XXXXX
Medical Record Number: XXXXXXXXXX DOB: XXXX.XX.XX Sex: F
Accession Number: XXXXXXXX
Study Requested: DIG MAMMOGRAM SCREENING ( )
Scheduled Date and Time: XXXX.XX.XX 13:02:
Requesting Physician: XXXXXXX,
Reason for Exam: V76.12
Radiological Report
Comparison is made to previous exams dated XX/XX/XX.
CLINICAL HISTORY: Seventy-two year old woman for screening exam. Patient has a family history of breast cancer, sister age sixty years old. Patient has a history of a previous left breast benign biopsy.
TECHNIQUE: Mammograms were obtained using digital technique.
FINDINGS: There is dense fibroglandular tissue bilaterally. No dominant masses or clustered microcalcifications suggestive of malignancy are seen.
1. NO SPECIFIC FEATURES OF MALIGNANCY SEEN EITHER BREAST.
2. NO SIGNIFICANT CHANGE WHEN COMPARED WITH PRIOR STUDIES.
3. ANNUAL SCREENING MAMMOGRAM IS RECOMMENDED.
CODE (1): NEGATIVE
Attending Radiologist: XXXXXXX, MD
Date Signed off: XXXXXX, Transc. by: NS

18. NLP for Clinical Texts Document retrieval – case finding.
Subject recruitment – identify patients that can benefit from a study.
Surveillance – monitoring disease outbreaks.
Discovery of disease-drug associations.
Discovery of disease-finding associations.

19. IE from Radiology Reports
Automatic Section Segmentation
Demographics
History
Comparison
Technique
Findings
Impression
Recommendation
Sign off

20. Dataset
215,000 free-text radiology reports selected randomly from 3 million reports over period of 9 years and representing 24 different types of diagnostic procedures.

21. Method – Training Set
Hand-crafted rules for automatic extraction of a training set. Common boundary patterns: e.g. section Findings – text between known section headers and another known headings:
^ (finding | observation | discussion)s?:
^ (\W*)(finding | observation | discussion)s?(\W*)$
3,000 automatically segmented “high- confidence” radiology reports, containing all 8 sections of interest.

22. Method
Classification task - each sentence from a radiology report is assigned to one of 8 pre- defined report sections.

23. Sentence features used for training a classifier.
Sentence Orthography
Possible orthographic types are All Capitals, Mixed Case, or presence of a Header pattern, such as a phrase at the beginning of a line followed by a colon.
Previous Sentence Boundary
Formatting boundary separating the current and previous text sentences. Possible values are white space containing new lines, white space without new lines, non-alphabetic characters, or the beginning of the file.
Following Sentence Boundary
Formatting boundary separating the current and next text sentences. Possible values are white space containing new lines, white space without new lines, non-alphabetic characters, or the end of the file.
Cosine Vector Distance
Distance from the current sentence to each of the eight sections' word vectors.
Exact Header Match
This feature specifies if the sentence contains a header identified as belonging to one of the sections in the training data.

24. Work in Progress
Identify named entities within sections using a controlled vocabulary – findings, diseases, observations, anatomical organs, imaging modalities.
Negation Discovery.
Identify relationships between named entities of interest, for example what observations are associated with a diagnosis.
Use radiology report text to support automatic annotation of medical images.

25. Q/A