diagnostic criteria and clinical guidelines standardization to automate case classification

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

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

(1) AERO for encoding guidelines

AND

AustralasianSociety of Clinical Immunology

and Allergy

National Institute for Health and Clinical

Excellence

BrightonCollaboration

(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 airwayswelling finding

skin and mucosalchanges

involvement of the skin and/or mucosal tissue

(e.g. generalized hives, itching or flushing,

swollen lips-tongue-uvula)

persistent dizzinesscollapse

difficulty talkinghoarse voice

wheeze orpersistent cough

difficult/noisy breathing

swelling of the tongueswelling/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

DERM

OTO

LOG

ICAL

MUC

OSA

LCA

RDIO

VASC

ULAR

RESP

IRAT

ORY

OTH

ERS

OR

OR

OR

OR

OR

AND

OR

OR

OR

OR

measured hypotensionOR

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)

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

(2) AERO for adverse event report based diagnosis

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

Free text partof the report

MedDRA encodedstructured data

Example VAERS report

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

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

Current status

• Created MedDRA –> Brighton mapping, in OWL, covering anaphylaxis guideline

• Tested classification of VAERS reports

(3) AERO for data integration

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)

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

Link to ontology terms

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

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.

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

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