Presented by:Aleksandar ZivaljevicAuckland 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

Agenda

1. Problem definition2. Study findings3. Proposed solution4. Proposed technique5. Stage of development

Shape and form of clinical informationCurrent 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

Case studiesProject 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 datasetsProposed 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

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)

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)

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

Source: OpenEHR foundation 2015 (www.openehr.org/ckm)

Framework

1

3

2

4Project diagram. Phases of the project are numbered as 1, 2, 3 and 4. Subject to change.

Phases of the current project

Phase 1Transform 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

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 terminologyYes

Use data/information level methods to find the element in terminology

No

Element found with certainty?

Yes

Add element name to ontology under the

conceptNo

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 ConceptCC example: Blood Pressure

Element – a concept or an elementElement 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 elements3) Use information level methods matching between the potential combination of elements in the archetype and combination of elements in the SNOMED

Issue: SNOMED serverPotential 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

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

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.