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SSynCAHD: Notes from the Second workshop – Ghent March 27th, 2015 (SVEPM)

Standardising Syndromic Classification in Animal Health Data (SSynCAHD) 1

SSynCAHD

Standardising Syndromic Classification in Animal Health Data (SSynCAHD)

Fernanda DóreaFlavie Vial

Céline DupuyAnn Lindberg

Crawford Revie

SSynCAHD

WORKSHOP GOALS

• What participants can expect

– Update on the results of the data inventory and inventory of ontologies

– Learning a bit more about ontologies and their practical applications

– Participation in the decisions regarding the next steps

• What we expect from participants

– Active discussion regarding next steps and opportunities for engagement of the VSS community



SSynCAHD

Today’s workshop

20 minutes Quick review of ontologies and their use

40 minutes Project progress so far:• Competency questions• Data inventory• Inventory of existing ontologies

30 minutes Coffee break

30 minutes The road ahead

60 minutes Discussion: plan the next stages

SSynCAHD

Quick review of the idea and progress so far:



SSynCAHD

Defining the goal

• Promote the use of standards for classification of syndromic data

– NOT reinvent data recording standards already developed

• Promote comparability of methods and results by having standard syndromic classes

– Focus NOT on data recording, but on TRANSLATING data to syndromic groups

• Develop tools for syndromic data classification which do not require recoding of existing data

SSynCAHD

Current SS systems:Knowledge rich

Serological tests for Brucellosis

Histologicalexamination on

sample from uterus

Salmonella serotyping on vaginal swab

Microbial growth on vaginal swab

PCR test for Brucellosis

Syndrome = Reproductive





We don’t need smarter data,

But to get the right data to the right place

So that smart applications can do their work

Consensus and standards for recording the data?

Consensus and standards for USING data (classification)



SSynCAHD

Controlled vocabularies

• Require data to be manually coded

• Ignore relationships between the terms included in the vocabulary

SSynCAHD

Ontologies



SSynCAHD

Pluto

Pluto is a dwarf planet. …

The solar system

SSynCAHD

Pluto

Celestial bodies

Dwarf planet

Planet

Is a

Star

B

Rigel

G

Sun

Orbits around

Solar system

Has CenterContains:Is a: PLANETandOrbits around: SUN



SSynCAHD

” Fundamentally ontology is about reaching agreements on what things means and putting it in a machine-processable form”

SSynCAHD

Organ System

• Reproductive

• Female Genitalia

• Vulva

• Vagina

• Female Internal Reproduct. Organs

• Uterus

• Ovary

• Male External Genitalia

• Male Internal Reproductive Organs

• OTHER SYSTEMS

Clinical Signs

• Reproductive

• Abortion

• Conception failure

• OTHER SYNDROMES...

Threats

• Non-Infectious Threats

• Metabolic Disorders

• Infectious agents

• Brucella sp

• Brucella abortus

• Brucella suis

• Brucella canis

• Neospora caninum

• BVD

• IBR

Lab Test Ordered

• UnspecificEtiology

• BacterialGrowth

• Histology

• SerologicalTests

• Brucellosis

• BVDV

• PathogenIdentification

• Brucellosis

• BVDV

• Salmonella

TestResults

• Serology

• Agent Identification

• Serological Result

Abattoir finding

• Ante-Mortem Inspection

• Post-Mortem Inspection

• Partial Condemnations

• Total condemnations

Ontologies in VSS:

”the boxes” (entities)



Organ System

•Reproductive

•Female Genitalia

•Vulva

•Vagina

•Female Internal Reproducitive Organs

•Uterus

•Ovary

•Male External Genitalia

•Male Internal Reproductive Organs

•OTHER SYSTEMS

Clinical Signs

•Reproductive

•Abortion

•Conception failure

•OTHER SYNDROMES...

Threats•Non-Infectious Threats

•Metabolic Disorders

•Infectious agents

•Brucella sp

•Brucella abortus

•Brucella suis

•Brucella canis

•Neospora caninum

•BVD

•IBR

LabTest Ordered

•UnspecificEtiology

•BacterialGrowth

•Histology

•SerologicalTests

•Brucellosis

•BVDV

•PathogenIdentification

•Brucellosis

•BVDV

•Salmonella

Test Results

•Serology

•Agent Identification

•Serological Result

Abattoir finding

•Ante-Mortem Inspection

•Post-Mortem Inspection

•Partial Condemnations

•Total condemnations

IdentifiesCauses

Affects

Identifies

Related to

Reflected as Causes

Suspicions Cases

Focus on the “rules” (relationships)

SSynCAHD

Practical applications – why use standards?

–Consensus and comparability: share common understanding of the structure of information among people

–Transparency: make domain assumptions explicit



SSynCAHD

Added value – why build an ontology for these standards?

• Consensus and comparability: share common understanding of the structure of information among people AND software agents

• Not just the information, but the structure of the information(relationships or “rules”)

• Reuse and analysis of domain knowledge

– Reasoning for consistent and semi-automated growth of the ontology, and automated update of their software applications

• Particular gain to the surveillance community: secondary use of data

SSynCAHD

Why should I be involved?

• The solution needs to be problem oriented, in order to work for all

• Build a body of “community knowledge” that develops from all to all

• Project outcomes: not only the ontology but applications (more on this later)



SSynCAHD

Practical development

• Step 1– Determine the domain and the scope

• Step 2– Reuse of existing ontologies

• Step 3– Enumerate important terms in the ontology

• Step 4– Define the classes and the class hierarchy

– Top-down x bottom-up x combination

SSynCAHD

Competency questions

• Competency questions:

– Domain: veterinary syndromic surveillance

– To describe the terms and relations necessary to detect the occurrence of animal health events of interest for surveillance though recorded animal health data.

– To allow standardisation of syndromic classification of animal health data without the need for common coding practices, acknowledging the differences in data recording practices across animal health domains, institutions and countries.

– To be freely available

– Built by the veterinary syndromic surveillance community and maintained by a group of curators



SSynCAHD

Practical development

• Step 1– Determine the domain and the scope

• Step 2– Reuse of existing ontologies

• Step 3– Enumerate important terms in the ontology

• Step 4– Define the classes and the class hierarchy

– Top-down x bottom-up x combination

Inventory of biomedicalontologies

Data inventory

SSynCAHD

DATA INVENTORY

Part I: data description



SSynCAHD

Origin

Belgium 7Canada 2Finland 1France 7

Italy 2Sweden 3

Switzerland 2USA 3Total 27

SSynCAHD

Data types

Abattoir 4Clinical 4

Laboratory 6Market 1

Mortality 3Production-Milk 2

Production-Registry 1Production-

Reproduction 4Rendering 1Syndromic 1

Total 27



SSynCAHD

System implemented

No 18Partially 1Yes 8Total 27

SSynCAHD

Species

Cattle 19Horses 3Poultry 6

Pigs 5Pets 2

Small Ruminants 6

Many/ ”All” 5Single 13

More than one 9Total 27



SSynCAHD

Syndromes

Abortion 3Abortion, Circ.Haematop.Hepatic, Eyes-Ears-Others, Integumentary, GIT, MBS, Mastitis, Musculoskeletal, Nervous, Reproductive, Respiratory, Systemic, Urinary, AntimResistance, Fallen Stock, GIT-Macroparasites, GIT-Microparasites, GIT-Bacterial tests, GIT-Viral Tests 1

Abortion/Repro, Diarrhea/GI, Neurologic, Respiratory, Sudden Death 1

Abortion; Musculoskeletal; Nervous; Reproductive; Respiratory; hyperthermia 1

All common disease syndromes 1Ear/nose/throat/mouth, skin, gastrointestinal, respiratory, hemorrhagic, neurologic, muculoskeletal, udder, non-ambulatory, ticks, reproductive/urinary, vesicular, dead, thin body condition, other 1FACET1: CNS, Cardiovascular, Coagulation, Endocrine, Eye, FAD, GIT, Hemopoietic, Hepatic, Integument, Lymphoid, Mastitis, Musculoskeletal, Pancreas, Reproductive, Respiratory, Skin, Toxicology, Urinary, Vector-borne. FACET2: CNS, FAD, GIT, Integument, Musculoskeletal, Reproductive, Respiratory, Urinary, Vector-borne. FACET3: Abortion. FACET4: Multisystemic. FACET5: Zoonotic, Zoonoti-Inlueza. 1

Fallen stock 2

fertility indicators 2

For swine: pneumonia, septicaemia, erysipelas, tuberculosis, central nervous system, pyrexia, dead.For bovine: pneumonia, toxaemia, mastitis, emaciation, moribund, pyrexia, septicaemia, central nervous system, deadFor poultry: tuberculosis, septicaemia, leucosis, airsacculitis, synovitis, tumors, cadavers

1Healthy, general, behaviour, metabolic/poisoning, locomotor, Non-infectious, Mortality, Circul.Hepatic.Hematop, Respiratory, GIT, systemic, abortion, reproductive, nervous, integumentary, urinary, eyes/ears/others 1

Milk production (drop) 2

Not classified yet 5

Proportion of condemnations 1

respiratory, GIT, musculoskeletal, nervous, reproductive, urinary, Others 1

Septicemia; lameness 1

Stillbirth, Fallen stock 1

Yes, but not given 1

SSynCAHD

Specificity

No 13Yes 14Total 27

Classification needed

Cases 6Suspicions 10Suspicions and cases 11

Total 27

Can detect:



SSynCAHD

DATA INVENTORY

Part II: structuring the information

SSynCAHD

Laboratory data – test REQUESTS

Laboratorytest requested

Identifypathogendirectly

Detectsimmune

response to pathogen

Histo-pathological

test

Samplesubmitted

Tissue samples

Non-specificsample (blood/

serum; earnotch, etc)

Pathogen Organ system

Identifies NO pathogen



SSynCAHD

Laboratory data – test REQUESTS



Detectsimmune


Hispto-pathological

test

Samplesubmitted

Tissue samples

Non-specificsample (blood/

serum; earnotch, etc)


Identifies NO pathogen

SyndromeReason for submission

Species Age

SSynCAHD

Laboratory data – test RESULTS



Detectsimmune


Hispto-pathological

test

Samplesubmitted

Tissue samples

Non-specificsample (blood/ serum/ plasma; ear notch, etc)


Identify pathogen

Positive immune response

Pathologicalfindings

Lab results




SSynCAHD

Laboratory data6

+

-

- Variety already in the project- Every country likely to have

- In general already computerized- Very structured

- Suspicions and cases- Variety of boxes and rules

- No clinical signs information - No true syndromes (in reality organ systems)

SSynCAHD

CLINICAL data



Detectsimmune


Hispto-pathological

test

Samplesubmitted

Tissue samples



Identify pathogen



Lab results


Clinical signs Collection of signsMedication

Classification: Suspicions and

cases

Currentversus history


Post mortem

Syndrome

Less structure, lots of text

mining

Truly syndromic

data



SSynCAHD

ABATTOIR data



Detectsimmune


Hispto-pathological

test

Samplesubmitted

Tissue samples



Identify pathogen



Lab results



Post mortem

Syndrome

Organ system

Classification: Suspicions

(and cases?)

STRUCTURED data

Post mortem

SSynCAHD



Detectsimmune


Hispto-pathological

test

Samplesubmitted

Tissue samples



Identify pathogen



Lab results



Post mortem

Syndrome

Cases of syndrome”mortality”

IF REASON NOT KNOWN: No need

of classification

Post mortem

MORTALITY data

animal movement and renderingMortality/

Still birth

Suspicions of othersyndromes if data

=post mortem



SSynCAHD

PRODUCTION data



Detectsimmune


Hispto-pathological

test

Samplesubmitted

Tissue samples



Identify pathogen



Lab results


Productionindicators Milk yieldMedication

Syndrome

Suspicions: unspecificsyndromes

Structuredbut very

unspectific

Fertility indicators

SSynCAHD

Laboratory data = PILOT ??



Detectsimmune


Hispto-pathological

test

Samplesubmitted

Tissue samples



Ontology availability???

Identify pathogen



Lab results




SSynCAHD

INVENTORY OF ONTOLOGIES

SSynCAHD

Desired features

• Language (OWL)

• OBO Foundry– Open

– Orthogonal

– be developed in a collaborative effort

– use common relations that are unambiguously defined

– provide procedures for user feedback and for identifying successive versions

– clearly bounded subject-matter.



SSynCAHD

Laboratory data = GOOD PILOT (base)??



Detectsimmune


Hispto-pathological

test

Samplesubmitted

Tissue samples



Ontology not available

Available for humans

Ontology available and on OBO

Identify pathogen



Lab results


SSynCAHD

PILOT case and data

Reuse existingontologies

Build on the ontology

Boxes

Focus on rules



SSynCAHD

ONTOLOGY DESIGN PATTERNS (ODP)

SSynCAHD

Ontology design patterns

• ”cookbook recipes”

• Removes the need for knowledge experts to have anyontology skills



SSynCAHD


Populated by domain experts

SSynCAHD


• No work lost – any piece already useful

• Should therefore start with a piece with high (re) use potential



SSynCAHD

ODP tutorial

• For those interested:

–Uppsala, April 21st to 23rd

–Free attendance

SSynCAHD

THE ROAD AHEAD



SSynCAHD

Ontology development

Break work into small problems

(ODPs?)

Fill in knowledge

Ontology growth and maintenance

Data and problem driven

Data

Expert opinion

OBO Foundry principles

Collaborators

Provide dataset sample

Fill from data

Expert input

Workshops and Expert review

Ontology use

Existing ontologies and standards

Expert review

SSynCAHD

Project outputs

• Ontology

• Applications using the ontology

–Data classification routines using free software

–Knowledge gathered on VSS classification so far applied to next projects



SSynCAHD

Next steps

• ODP Tutorial (April)

• End of planning phase (July 2015)

• Apply for more funding

–Swedish Innovation Agency – October 2015

–Other countries individually?

–EFSA support?

SSynCAHD

THANK YOU!

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