A short, high-level presentation of my PhD work on the AERO project for ICBO 2013

1. DIAGNOSTIC CRITERIA AND
CLINICAL GUIDELINES
STANDARDIZATION TO
AUTOMATE CASE
CLASSIFICATION
Mélanie Courtot, Ph.D. candidate
Terry Fox Laboratory, BC Cancer Agency
International Conference on Biomedical Ontology, July 8th 2013

2. Key points
• The Adverse Event Reporting Ontology
(AERO) is to be used for
(1) Encoding guidelines
(2) Adverse event reports based
diagnosis
(3) Data integration

3. (1) AERO for encoding guidelines

4. AND
AustralasianSociety
of Clinical Immunology
and Allergy
National Institute for
Health and Clinical
Excellence
Brighton
Collaboration
(Level 1)
OR ONE OF
World Allergy Organization
AND/OR
AND/OR
VARIOUS COMBINATIONS
OF
generalized urticaria or
generalized erythema finding
angioedema finding
generalized pruritus
with skin rash finding
clinical diagnosis of
uncompensated shock
respiratory distress
diagnosis
bilateral wheeze finding
stridor finding
upper airway
swelling finding
skin and mucosal
changes
involvement of the skin
and/or mucosal tissue
(e.g. generalized hives,
itching or flushing,
swollen lips-tongue-uvula)
persistent dizziness
collapse
difficulty talking
hoarse voice
wheeze or
persistent cough
difficult/noisy breathing
swelling of the tongue
swelling/tightness in throat
pale and floppy
(young children)
circulation problem
(hypotension
and/or tachycardia)
breathing problem
(bronchospasm
with tachypnoea)
problems involving
the airway
(pharyngeal or laryngeal)
Reduced BP or symptoms of
end-organ dysfunction such
as hypotonia, incontinence
Respiratory symptoms such
as shortness of breath,
wheeze,cough,
stridor, hypoxemia
sudden gastrointestinal
syndromes such as
crampy abdominal
pain, vomiting
DERMOTOLOGICAL
MUCOSAL
CARDIO
VASCULAR
RESPIRATORYOTHERS
OR
OR
OR
OR
OR
AND
OR
OR
OR
OR
measured hypotension
OR

5. Clinical guidelines in AERO
• Surveillance Goals
• Provide a pattern to encode guidelines for adverse
event reporting following immunization
• Make this pattern applicable to any type of clinical
guideline
• Provide a means for the reports to be annotated with
diagnosis according to a specific guideline (and keep
track of which)
• Implementation Goals
• Encode the guideline in OWL
• Be able to infer correct classification (i.e., perform
accurate diagnosis)

6. Current status
• Pattern for anaphylaxis clinical
guideline according to the
Brighton Collaboration has
been implemented in OWL
• Colleague Dr. Jie Zheng has
modeled WHO malaria clinical
guidelines using the same pattern
Jie Zheng
University of
Pennsilvania
Brighton Collaboration: https://brightoncollaboration.org

7. (2) AERO for adverse event report based
diagnosis

8. VAERS dataset
• VAERS = Vaccine Adverse Event Reporting
System
• Administered by the Centers for Disease Control
and Prevention (CDC) and the Food and Drug
Administration (FDA) in the United States
• A spontaneous reporting system
• spontaneous reporting systems have issues with
underreporting and quality
• MedDRA (Medical Dictionary of Regulatory
Activities) is used to represent clinical findings

9. Free text part
of the report
MedDRA encoded
structured data
Example
VAERS report

10. Working with classified VAERS data
• Unclassified files available publicly
• Classified dataset available only upon
request
• FDA provided dataset of classified adverse
events following H1N1 immunization in
winter 2009-2010
• FDA classified reports according to the
Brighton case definitions

11. A test of ontology-based method
1. Map the current Brighton terms in AERO
to their MedDRA counterpart
2. Use a reasoner to classify the MedDRA-
annotated reports using the Brighton
criteria
3. Compare results with FDA classification
done by medical experts

12. Current status
• Created MedDRA –> Brighton
mapping, in OWL, covering
anaphylaxis guideline
• Tested classification of VAERS
reports

13. (3) AERO for data integration

14. The semantic web
• From a web of documents to a web of data
• HTML pages can’t be understood by machines;
humans have to manually follow hyperlinks
• Semantic web uses standard for data
representation, querying, vocabularies to link data
behind the scenes
• Use of Uniform Resources Identifiers (URIs) and
Resource Description Framework (RDF)

15. VAERS as linked data
• Transform the VAERS dataset in RDF to enable
better integration with existing linked data
• Avoids typical need to worry about resources’
structure (CSV, databases, XML)
• Approach
• VAERS reports are OWL individuals
• RDF is generated using FuXi (python) from a relational
database I constructed from VAERS flat files

16. Link to
ontology
terms

17. A simple example:
Change state code in VAERS to state URI in DBPedia
Query against DBPedia to help prepare call to Google
visualization API

18. Potential uses of query across
linked data sets
• Using the VAERS dataset
• Are there differences in the type of adverse events
between a live attenuated flu vaccine and a trivalent
inactivated one?
• Using another dataset: DrugBank
• Link to DrugBank based on drug mentions in text (e.g.
“Benadryl”)
• Retrieve therapeutic class from DrugBank
• In cases where therapeutic class is anti-allergic agents
infer that the patient may have had an allergic reaction.

19. Acknowledgements
• Alan Ruttenberg, Ryan Brinkman
• Jie Zheng, Chris Stoeckert
• Julie Lafleche, Lauren McDonald, Robert Pless,
Barbara Law, Jan Bonhoeffer, Jean-Paul Collet
• Oliver He, Yu Lin, Lindsay Cowell, Barry Smith, Albert
Goldfain

20. Mélanie Courtot mcourtot@gmail.com
http://purl.obolibrary.org/obo/aero