AN ONTOLOGY OF
THERAPIES
Claudio Eccher - eHealth Research Unit, FBK, Trento
Antonella Ferro - Medical Oncology Unit, S. C...
BACKGROUND
 Medicine is a very complex domain from the point of
view of modeling and representing intended meaning:
 dif...
THE ONCOCURE PROJECT
 The Oncocure project, started in 2007, aims to:
 design, and develop a prescriptive DSS based on b...
AIM
 Starting from the necessities of unambiguously define
therapy-related concepts in the Oncocure project, we
designed ...
WHICH ONTOLOGIES ARE WE TALKING
ABOUT?
 Rome
Domain Ontologies
biomedical instruments,
drugs, oncology therapies
Referenc...
THE NCI THESAURUS
 One of the most comprehensive vocabularies in the
cancer domain, defined by its authors as:
 “a biome...
EXAMPLE OF PROBLEMS WITH NCIT
 Adjuvant_Therapy and Neo_Adjuvant_Therapy
(treatments in different care phases) siblings o...
ONTOLOGY DESIGN PRINCIPLES
 Created in OWL (DL),
 Based upon the top-level ontology DOLCE.
 Endurants: entities wholly ...
THE ONTOLOGY DEVELOPED
 Medical therapies are represented by the
therapy_description class (information entity).
The enac...
USE OF THE ONTOLOGY
 Our axiomatic ontology constitutes a model for
labeling (temporally annotated) EPR data with
higher ...
AN EXPERIMENT
 From the axioms defining the therapy_role_by_phase
subclasses in the ontology we defined the rules to
labe...
RESULTS
 961 therapies (474 patients) with valid data (82 MX);
 868 (90.3 %) correctly classified;
 18 not classified (...
CONCLUSIONS
 The relevant role of ontologies in the design and
implementation of health care information systems
is widel...
Thank you for your attention
THE ONTOLOGY DEVELOPED
 Medical therapies are represented by the
therapy_description class (information entity). The
enac...
ONTOLOGY DESIGN PRINCIPLES
 Based upon the top-level ontology DOLCE.
 It inherits the basic distinction between
Endurant...
An Ontology of Therapies
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An Ontology of Therapies

  1. 1. 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
  2. 2. 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.
  3. 3. 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).
  4. 4. 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.
  5. 5. WHICH ONTOLOGIES ARE WE TALKING ABOUT?  Rome Domain Ontologies biomedical instruments, drugs, oncology therapies Reference Ontologies medicine, agriculture, law Domain-independent Ontologies objects, events, processes, parts Formal Ontologies classes, istances, roles  DOLCE  Ontology of therapies
  6. 6. 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.
  7. 7. EXAMPLE OF PROBLEMS WITH 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).
  8. 8. ONTOLOGY DESIGN PRINCIPLES  Created in OWL (DL),  Based upon the top-level ontology DOLCE.  Endurants: entities wholly present at any time.  Perdurants: entities that extend in time by accumulating temporal parts.  Endurants are distinguished in physical (resources) and non physical endurants (information entities).  Qualities: entities (weight, color, etc.) that characterize the features of the different items.
  9. 9. THE ONTOLOGY DEVELOPED  Medical therapies are represented by the therapy_description class (information entity). The enactment of a therapy is a perdurant.
  10. 10. USE OF 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.
  11. 11. 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).
  12. 12. 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)
  13. 13. 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.
  14. 14. Thank you for your attention
  15. 15. THE ONTOLOGY DEVELOPED  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.
  16. 16. ONTOLOGY DESIGN PRINCIPLES  Based upon the top-level ontology DOLCE.  It inherits the basic distinction between Endurants (entities wholly present at any time ) and Perdurants (entities that extend in time by accumulating temporal parts).  Endurants are distinguished between physical (resources) and non physical entities (information entities)  Definition of qualities: entities (pharmacological, radiant, etc.) that characterize the features of the different items.
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