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An Ontology of Therapies. Claudio Eccher - eHealth Research Unit, FBK, Trento Antonella Ferro - Medical Oncology Unit, S. Chiara Hospital, Trento Domenico Pisanelli – Laboratory for Applied Ontology, CNR, Rome. eHealth 2009– Istanbul, 24th September 2009. Background.

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an ontology of therapies

An OntologyofTherapies

Claudio Eccher - eHealth Research Unit, FBK, Trento

Antonella Ferro - Medical Oncology Unit, S. Chiara Hospital, Trento

DomenicoPisanelli – Laboratory for Applied Ontology, CNR, Rome

eHealth 2009– Istanbul, 24th September 2009

background
Background
  • Medicine is a very complex domain from the point of view of modeling and representing intended meaning:
    • different activity domains, scientific granularities, and user requirements for the same service;
    • ambiguous terminology (polysemy).
  • Ontologies are nowadays considered as the basic infrastructure for achieving semantic interoperability between information systems.
  • Semantic interoperability hinges on shared vocabularies whose semantics is described by ontologies in an unambiguous and machine-processable form.
the oncocure project
The Oncocure Project
  • The Oncocure project, started in 2007, aims to:
    • design, and develop a prescriptive DSS based on breast cancer protocol encoded in Asbru;
    • integrate the DSS with a legacy oncological web-based EPR in use in the Medical Oncology Unit (MOU) of the S.Chiara Hospital of Trento (Northern Italy), in order to provide automatic support at the point of care.
  • One of the most challenging tasks in building a DSS and integrating it into the clinical workflow is to bridge the gap between the EPR and the DSS.
    • Cancer protocol require temporal and taxonomic abstractions, especially regarding therapies (e.g., anthracyclines in adjuvant).
slide4
AIM
  • Starting from the necessities of unambiguously define therapy-related concepts in the Oncocure project, we designed an axiomatic ontology for medical therapies, focused especially on oncologic therapies.
  • Axiomatic ontologies allow the explicit representation of ontological commitments related to terms to:
    • Facilitate meaning negotiation among agents;
    • Clarify and model the negotiation process itself.
which ontologies are we talking about

Formal Ontologies

classes, istances, roles

Domain-independent Ontologies

objects, events, processes, parts

Reference Ontologies

medicine, agriculture, law

Domain Ontologies

biomedical instruments,

drugs, oncology therapies

WhichOntologiesare wetalkingabout?
  • Rome
  • DOLCE
  • Ontologyoftherapies
the nci thesaurus
The NCI Thesaurus
  • One of the most comprehensive vocabularies in the cancer domain, defined by its authors as:
    • “a biomedical vocabulary that provides consistent, unambiguous codes and definitions for concepts used in cancer research”
    • “exhibits ontology like properties in its construction and use”
  • NCIT is available in OWL (over 1.200.000 triples);
  • Although a valuable resource as reference terminology, as regards therapy concepts NCIT is merely a taxonomy (only IS_A relations) and suffers of some classification problems.
example of problems with ncit
ExampleofProblemswith NCIT
  • Adjuvant_Therapy and Neo_Adjuvant_Therapy (treatments in different care phases) siblings of Hormone_Therapy and Chemotherapy (different drug kinds) and of Breast_Cancer_Treatment (treatment for a specific cancer);
  • Dose, Dose_Rate and Dose_Modification direct subclasses of Treatment_Regimen (is_a relation);
  • Protocol_Treatment_Arm (protocol in a clinical trial) sibling of Second_Line_Treament (treatment given after the first line failure);
  • Palliative_Surgery and Curative_Surgery (goal) siblings of Ambulatory_Surgical_Procedure (place).
ontology design principles
Ontology Design Principles
  • Created in OWL (DL),
  • Based upon the top-level ontology DOLCE.
  • Endurants are distinguished in physical (resources) andnon physicalendurants (information entities).
  • Qualities: entities (weight, color, etc.) that characterize the features of the different items.
  • Endurants: entities wholly present at any time.
  • Perdurants: entities that extend in time by accumulating temporal parts.
the ontology developed
The OntologyDeveloped
  • Medical therapies are represented by the therapy_description class (information entity). The enactment of a therapy is a perdurant.
use of the ontology
Useof the Ontology
  • Our axiomatic ontology constitutes a model for labeling (temporally annotated) EPR data with higher level abstraction information for:
    • Modeling guidelines and interfacing automatic DSS with an EPR: guidelines often requires abstractions related to therapy history (e.g., Taxanes in adjuvant).
    • Enabling automatic data analysis: the identification and classification of therapies related to specific care delivery ‘phases’ facilitates visual representation for an immediate comprehension of the care process.
    • Controlling the medical errors: a physician can be automatically alerted if possible incongruities in data are found.
an experiment
An Experiment
  • From the axioms defining the therapy_role_by_phase subclasses in the ontology we defined the rules to label oncological therapies in the EPR as neoadjuvant, adjuvant and metastatic.
    • Ex: Therapy takes place after surgery, ends before a progression, is administered to a patient with M0 tumor at diagnosis  Adjuvant Therapy
  • Using the rules, we retrieved and labelled the set of breast cancer hormone and chemo-therapies administered in three years (2006-2008).
  • We compared our results with the therapy roles registered by the oncologist who planned the treatment (available since 2006).
results
results
  • 961 therapies (474 patients) with valid data (82 MX);
  • 868 (90.3 %) correctly classified;
  • 18 not classified (all MX).
  • 93 “incongruities”:
    • 4 adjuvant classified as metastatic (3 M1, 2 MX), 6 as neoadjuvant (2MX)
    • 6 neoadjuvant classified as adjuvant (5 M0, 1 MX), 2 as metastatic (1 M1, 1 M0)
    • 21 ‘palliative’ (metastatic) classified as adjuvant (1MX), 21 as neoadjuvant (11 M0, 10 MX).
    • 33 curative classified as metastatic (9 M0, 20 M1, 4 MX)
conclusions
Conclusions
  • The relevant role of ontologies in the design and implementation of health care information systems is widely acknowledged.
  • Our axiomatic ontology, developed according to good design principles and based on a foundational ontology (DOLCE), allows to assign precise meanings to concepts and remove ambiguities.
  • Without an ontological grounding like this the same information may shift its sense according to the context and the human agent tacit knowledge.
the ontology developed1
The OntologyDeveloped
  • Medical therapies are represented by the therapy_description class (information entity). The enactment of a therapy is a perdurant.
  • With the help of an oncologist, therapies were classified according to their qualities: method (e.g., radiant, surgery), involved pathology, role (e.g., curative/palliative, primary/non primary), etc.
  • To describe a therapy, rather than establishing IS_A links, we add existential restrictions.
  • We can attach several qualities to the same therapy avoiding the entanglement of multiple hierarchies.
ontology design principles1
Ontology Design Principles
  • Basedupon the top-levelontology DOLCE.
  • Itinherits the basicdistinctionbetweenEndurants(entitieswhollypresent at anytime ) and Perdurants(entitiesthatextend in timebyaccumulatingtemporalparts).
  • Endurants are distinguishedbetweenphysical (resources) and non physicalentities (information entities)
  • Definitionofqualities: entities (pharmacological, radiant, etc.) thatcharacterize the featuresof the differentitems.