Reflections on a medical ontology
Introduction
Medical ontologies classify and encode relationships between nomenclature and/or concepts invoked in medical procedures and operations. These foster a mutual understanding between human experts with different backgrounds as well as between human and software agents and between software agents alone. They are, thus, well suited to provide knowledge-level support for describing and putting together information-intensive applications in medicine; hence, a wide range of efforts are directed at designing medical ontologies. Among this wide spectrum of approaches which differ in complexity and scale, we can broadly distinguish two groups of approaches to medical ontology development: the general purpose ones that are application-independent (Hahn and Schulz, 2004, Smith et al., 2005), and those set up to drive a particular application. Examples of the former include the efforts towards controlled vocabulary in medical domain such as UMLS Metathesaurus,1 SNOMED,2 MeSH,3 GALEN,4 etc. The universal reach of these efforts requires substantial resources and extensive collaborative efforts, and often leaves a trail of philosophical commentary regarding the degree to which they can be transparent representations of reality (Smith, 2004). In contrast, many of the applications built in short-term projects in the second category tend to develop medical ontologies which appear to be ad hoc to some degree (Abu-Hanna et al., 2005), e.g. the Brazilian National Health Card Ontology,5 the Drug Ontology Project for Elsevier,6 Domain Ontology of EORCA project,7 etc.
Medical imaging with advanced knowledge technologies (MIAKT) was a 2-year project with the aim of providing a prototype for advanced knowledge services to breast cancer screening procedure and breast cancer triple assessment (TA). Breast cancer is one of the leading causes of cancer death among women in the US (Patlak et al., 2001) and is the most common cancer for women in the UK.8 Diagnosis of breast cancer normally involves multi-disciplinary meetings with experts from different medical backgrounds, e.g. radiologists, surgeons, oncologists, histologists, and other clinical staff. As with other disciplines, we expect considerable variability among experts. This provides the motivation for standardising the vocabulary used in the breast cancer screening and diagnosis process. In the context of MIAKT, we have faced problems and difficulties that, we imagine, might be faced by most other ontology developers working on projects of similar size. In this paper, we try to clarify some of the conceptual underpinnings of our approach to ontology building, outlining our attempts to understand the context of application which determines the modelling choices we have made. We focus on examples from our Breast Cancer Imaging Ontology (BCIO) to illustrate essential questions that often need addressing when developing ontologies. Despite its roots in the specific application we have developed, the design of ontologies is meant to facilitate application independence. Hence, we set out the rationale by which we extricate the “ontological core” from the application context and indicate how we tailor the ontology to meet the application-specific requirements.
Section snippets
Setting the baseline for conceptual reflection
The problem we faced was to build a computer-aided patient data system to support routine breast cancer screening sessions, and subsequent patient management meetings. For this purpose, we needed to provide a framework and communication interface for the mutual understanding of clinicians from different backgrounds. We needed to make information accessible and understandable by computers in order to provide paperless storage and remote access and implement procedures for intelligent retrieval
Scrutinising the domain of discourse
Rooted in philosophy, Ontology is the study of existence (i.e. of what is). The recent blossoming of ontology-related research in computer science, however, takes a more practical perspective focussing on how to capture and codify the essence of beings and how to share the codified knowledge. A good example of such a trend can be witnessed in the artificial intelligence (AI) community wherein researchers tend to deliberately blur the differences among different uses of the word “ontology” and
Breast cancer imaging ontology
Breast cancer diagnosis relies heavily on visual and tactile information, e.g. various medical images, results of physical examination, which cannot be accurately and comprehensively represented using mathematical or logical models. Such information can be abstracted either using an approximate model or leaving a “semantic gap” between the source of information and the representations and requesting human interpreters to fill the gap with their experience and wisdom. We expect that a
Discussions
Nowadays, after the explosion of information available from the internet, we have witnessed an explosion of so-called ontologies made public by all sorts of ontology developers. Their “products” are quite diverse in nature ranging from comprehensive ones, e.g. Cyc14 and foundational ones, e.g. IEEE SUMO,15 DOLCE16 to application-specific ones such as BCIO. Despite the number of available ontologies, people working
Acknowledgements
Research for this paper was funded by the British Engineering and Physical Sciences Research Council (EPSRC) under the MIAKT Grant GR/R85150/01 and under the AKT IRC Grant GR/N15764.
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