a solution for data access issues faced in biomedical projects

1. Presented by:
Aleksandar Zivaljevic
Auckland Bioengineering Institute
Annotation of clinical datasets
using openEHR Archetypes
A solution for data access issues faced in biomedical projects
Authors:
Aleksandar Zivaljevic, Koray Atalag, Jim Warren,
Mike Cooling, David Nickerson, Peter Hunter
Prepared for HINZ 2015

2. Agenda
1. Problem definition
2. Study findings
3. Proposed solution
4. Proposed technique
5. Stage of development

3. Shape and form of clinical information
Current practice:
•Clinical information is collected and stored in
disparate and diverse formats
•Not much attention is put on potential reuse of
information
As a result:
•Required information is difficult to find
•Manual mapping by experts is required for
making use of information
•Clinical data is not directly machine
processable, impacting interoperability and reuse
of information
Impact on projects:
•Increased project time
•Increased project cost
•Validity of projects is impacted
List of clinical dataset repositories:
https://accelerate.ucsf.edu/research/celdac

4. Case studies
Project Declared issue Resolution
@neurIST complex information processing tool-chain for the integrated
management of cerebral aneurysms [3]
Workload (timeframes
/ cost)
Manual mapping based tool called @neuInfo was
developed and used as an infrastructure component
that facilitates data access.
Computational modelling and evaluation of cardiovascular response under pulsatile
impeller pump support [2]
Research validity None, as no clinical data has been used in modelling.
euHeart: personalized and integrated cardiac care using patient-specific
cardiovascular modelling [1]
Research validity None. Project hasn’t validated the models as access
to clinical data was not available.
Clinically Oriented Translational Cancer Multilevel Modelling: The ContraCancrum
Project [4]
Workload
(timeframes / cost)
Clinicians participating in the project manually
prepare clinical data in accordance to the prescribed
model and upload that data using web portal.
Ontology Based Data Management Systems for Post-Genomic Clinical Trials
within a European Grid Infrastructure for Cancer Research [5]
Workload (timeframes
/ cost)
Querying engine that is capable of returning clinical
data as if it originates from a single source created.
Manual mapping involved in preparing the data.
Comparative Effectiveness Research [6] Research validity None. However, development of data mapping
applications is suggested as a necessary step.
Findings:
•Evidence found that disparate forms that clinical information is stored in has negative effects
on the (bioengineering) research projects
•There is a need for a computer based system for discovery of clinical information concepts
within clinical datasets
Proposed solution:
•We propose that standards based information systems, openEHR Archetypes in particular,
be used as metadata of clinical datasets and that they are allocated to datasets through the
process of annotation

5. Standards based information models
openEHR Archetypes
openEHR Archetypes are:
•Standards based information models
•“Maximum datasets” for a given clinical concept
•“Ontologies of information” as opposed to “ontologies
of reality”
•Created by domain experts
•Building blocks for larger archetypes
(earlier called templates)
•Composite clinical information models,
built of Reference Models (RMs)

6. Current annotation method
Issues:
•Concept attributes are being annotated, not clinical concepts which are normally
composite in nature
•Discovery of data is relatively easy, while discovery of information is not (information
is usually contained in composite concept, not just an attribute)
•Corresponding systems have to be aware of all ontologies used to annotate
concepts
Terminology – SNOMED, ICD, LOINC
(for illustration purposes only)

7. Proposed annotation method
Benefits:
•Complete clinical concepts that are contained in the dataset are being annotated,
not just individual attributes
•Discovery of information becomes possible

8. Source: OpenEHR foundation 2015
(www.openehr.org/ckm)
Framework
1
3
2
4
Project diagram. Phases of the project are numbered as 1, 2, 3 and 4. Subject to change.
Phases of the current project

9. Phase 1
Transform openEHR Archetype into ontology of reality
Source: OpenEHR foundation 2015 (www.openehr.org/ckm)
ADLS
Find composite
concepts
Find simple
concepts
Code all found
terms
Enlarge and
enrich found terms
Assemble
ontology of reality

10. Phase 1 – detail (subject to change)
Find terminology
code for the CC
Add CC’s
terminology code to
ontology
Code listed in the
archetype or found
in step 1?
Yes
Find code in
SNOMED
No
More concepts?Yes
Find all CCs in the
Archetype
Find all elements
forming the CC
Add element’s
terminology code to
ontology under the
concept
More attributes?No Yes
Attribute listed in
terminology
Yes
Use data/information
level methods to find the
element in terminology
No
Element found with
certainty?
Yes
Add element name to
ontology under the
concept
No
End
Start
CC’s code found?
Yes
Use data/information
level methods to find the
CC in terminology
No
CC found with
certainty?
Yes Add CC’s name to
ontology
No
CC – Composite Concept
CC example: Blood Pressure
Element – a concept or an element
Element example: Systolic
Issue: Where is the list of composite
(clinical) concepts and their elements?
Potential solutions:
1) create a database that will be an
index of CCs described by archetypes,
RIMs, FHIR resources...
2) Guess from SNOMED – find first
common predecessor of all or majority of
elements
3) Use information level methods
matching between the potential
combination of elements in the
archetype and combination of elements
in the SNOMED
Issue: SNOMED server
Potential solution: Shrimp
Find out what the terminology code is by finding first
common parent for all or as many sub-concepts/elements.
Explore literature for other options
String based techniques:
· edit distances, n-gram similarity, prefixes and suffixes (Cohen, Ravikumar, & Fienberg, 2003)
Language based techniques:
· semantic expansion (Arias et al., 2012, p. 91) and semantic enrichment (Meziane, 2004, p. 217)
Constraints based techniques:
· data type of the element, value ranges that element's value can belong to and similar

11. Annotation of clinical datasets using
openEHR Archetypes
A solution for data access issues faced in biomedical projects
?
Presented by:
Aleksandar Zivaljevic (Alex)
Auckland Bioengineering Institute

12. References
[1] Smith N, Vecchi A de, McCormick M, Nordsletten D, Camara O, Frangi AF, Delingette H, Sermesant M, Relan J, Ayache N, Krueger MW,
Schulze WHW, Hose R, Valverde I, Beerbaum P, Staicu C, Siebes M, Spaan J, Hunter P, Weese J, Lehmann H, Chapelle D, Rezavi R.
euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling. Interface Focus. 2011;1(3):349–64.
[2] Shi Y, Brown AG, Lawford PV, Arndt A, Nuesser P, Hose DR. Computational modelling and evaluation of cardiovascular response under
pulsatile impeller pump support. Interface Focus. 2011;1(3):320–37.
[3] Villa-Uriol MC, Berti G, Hose DR, Marzo A, Chiarini A, Penrose J, Pozo J, Schmidt JG, Singh P, Lycett R, Larrabide I, Frangi AF. @neurIST
complex information processing toolchain for the integrated management of cerebral aneurysms. Interface Focus. 2011;1(3):308–19.
[4] Marias K, Sakkalis V, Roniotis A, Farmaki C, Stamatakos G, Dionysiou D, Giatili S, Uzunoglou N, Graf N, Bohle R, Messe E, Coveney PV,
Manos S, Wan S, Folarin A, Nagl S, Büchler P, Bardyn T, Reyes M, Clapworthy G, Mcfarlane N, Liu E, Bily T, Balek M, Karasek M, Bednar
V, Sabczynski J, Opfer R, Renisch S, Carlsen IC. Clinically Oriented Translational Cancer Multilevel Modeling: The ContraCancrum Project.
In: Dössel O, Schlegel WC, editors. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich,
Germany. Springer Berlin Heidelberg; 2009. p. 2124–7.
[5] Weiler G, Brochhausen M, Graf N, Schera F, Hoppe A, Kiefer S. Ontology Based Data Management Systems for Post-Genomic Clinical
Trials within a European Grid Infrastructure for Cancer Research. 29th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society, 2007 EMBS 2007. 2007. p. 6434–7.
[6] Sox HC. Comparative Effectiveness Research: A Progress Report. Ann Intern Med. 2010 Oct 5;153(7):469–72.