1. Health Natural Language Processing Center
(hNLP Center)
center.healthnlp.org
Guergana K. Savova, PhD
Associate Professor
Boston Children’s Hospital
Harvard Medical School
Noemie Elhadad, PhD
Associate Professor
Columbia University
Martha Palmer, PhD
Professor
University of Colorado, Boulder

2. Support National Institutes of Health
National Library of Medicine
National Institute of General Medical Sciences
National Cancer Institute
Office of the National Coordinator of Healthcare Technology
Support – Mayo Clinic, Boston Children’s Hospital
Institutions contributing de-identified clinical notes
Cincinnati's Children’s Hospital and Medical Center
Mayo Clinic
UPMC
Beth Israel
Current funding – NIGMS R01GM (Extended Methods and Software Development for Health NLP)

3. hNLP Center: Mission
Support health language-related education, research and technology development by creating and sharing curated linguistic textual resources based on the principle that broad access to data drives innovation.

4. hNLP Center: Organizational Structure
Not-for-profit consortium of members
Advisory Committee to strategically guide its trajectory
Rebecca Jacobson, UPMC
Piet de Groen, University of Minnesota/Mayo Clinic
Mark Liberman, University of Pennsylvania/Linguistic Data Consortium
John Brownstein, Boston Children’s Hospital/Harvard Medical School
Dina Demner-Fushman, National Library of Medicine
John Pestian, Cincinnati’s Children’s Hospital and Medical Center
Fee-based membership to ensure its sustainability
Industry members obtain a commercial license

5. hNLP Center: Activities
Provide a repository and data curation, distribution and management point for health-related language resources
Support sponsored research programs and health-related language-based technology evaluations
Engage in collaborations with US and foreign researchers, institutions and data centers
Host and participate in various workshops.

6. hNLP Center: Roadmap through 2020
Distribution of about 2M words of clinical text with layers of
linguistic gold annotation (constituency trees, dependency trees, coreference, temporal relations, events)
domain gold annotations (clinical entities with mappings to ontologies, clinical data elements).
These datasets have already been created with funding from the National Institutes of Health
Portions released for shared tasks to advance science
CLEF/ShARe (
SemEval Analysis of Clinical Text ( ;
SemEval Clinical TempEval ( )

7. Sample Gold Annotations: Layers
The patient underwent a CT scan in April which did not reveal lesions in his liver.
Boundary Detection …
The patient underwent a CT scan in April which did not reveal lesions in his liver.
The
patient
underwent a CT
scan in
April
which
did
not
reveal
lesions
his
liver . -
undergo do
lesion DT NN
VBD IN
NNP
WDT RB VB
NNS
PRP$
Tokenization
Normalization
Part-of-speech Tagger
CT scan
Lesion
Liver
Procedure
Disease / Disorder
Anatomy
UMLS ID: C
UMLS ID: C
UMLS ID: C
Biomedical
End-Use
Entity Recognition
Chunking NP VP PP …
undergo.01 ( A1.patient; A2.scan; AM-TEMP.in )
reveal.01 ( A0.scan; R-A0.which; AM-NEG.not; A1.lesions; AM-LOC.in ) …
Constituency Parsing S NP DT NN
VP …
Dependency Parsing
SRL
CT scan
Lesion
Liver
Negated: no
Negated: yes
Subject: patient --
Entity Properties
Biomedical
End-Use
UMLS Relation
locationOf ( lesions, liver )
Event, Temp. Expr.
CT scan
April
Reveal
Lesions
April CONTAINS CT scan
CT scan CONTAINS lesions
Temporal Relation
Coreference
identity ( the patient, his )

8. Questions?