Biomedical data collections are typically compiled on the basis of assessment instruments and associated terminologies and their data structure explained by means of data dictionaries. The Information Artifact Ontology (IAO) is an attempt to give a realism-based account of the essence of information entities and how components of such entities relate to each other and to that what they are information about. Changes in the taxonomy and the definitions of the IAO, most importantly the addition of the terms 'representational artifact' (...) and 'representational unit', are proposed to make the IAO a useful tool to clarify formally the distinctions and commonalities between data collections and associated artifacts that are compiled independently from each other, yet cover the same domain. (shrink)

To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.

We present an ontology of pain and of other pain-related phenomena, building on the definition of pain provided by the International Association for the Study of Pain (IASP). Our strategy is to identify an evolutionarily basic canonical pain phenomenon, involving unpleasant sensory and emotional experience based causally in localized tissue damage that is concordant with that experience. We then show how different variant cases of this canonical pain phenomenon can be distinguished, including pain that is elevated relative to peripheral trauma, (...) pain that is caused neuropathically (thus with no necessary peripheral stimulus), and pain reports arising through deception either of self or of others. We describe how our approach can answer some of the objections raised against the IASP definition, and sketch how it can be used to support more sophisticated discrimination of different types of pain resulting in improved data analysis that can help in advancing pain research. (shrink)

PURPOSE—A substantial fraction of the observations made by clinicians and entered into patient records are expressed by means of negation or by using terms which contain negative qualifiers (as in “absence of pulse” or “surgical procedure not performed”). This seems at first sight to present problems for ontologies, terminologies and data repositories that adhere to a realist view and thus reject any reference to putative non-existing entities. Basic Formal Ontology (BFO) and Referent Tracking (RT) are examples of such paradigms. The (...) purpose of the research here described was to test a proposal to capture negative findings in electronic health record systems based on BFO and RT. METHODS—We analysed a series of negative findings encountered in 748 sentences taken from 41 patient charts. We classified the phenomena described in terms of the various top-level categories and relations defined in BFO, taking into account the role of negation in the corresponding descriptions. We also studied terms from SNOMED-CT containing one or other form of negation. We then explored ways to represent the described phenomena by means of the types of representational units available to realist ontologies such as BFO. RESULTS—We introduced a new family of ‘lacks’ relations into the OBO Relation Ontology. The relation lacks_part, for example, defined in terms of the positive relation part_of, holds between a particular p and a universal U when p has no instance of U as part. Since p and U both exist, assertions involving ‘lacks_part’ and its cognates meet the requirements of positivity. CONCLUSION—By expanding the OBO Relation Ontology, we were able to accommodate nearly all occurrences of negative findings in the sample studied. (shrink)

The goal of referent tracking is to create an ever-growing pool of data relating to the entities existing in concrete spatiotemporal reality. In the context of Electronic Healthcare Records (EHRs) the relevant concrete entities are not only particular patients but also their parts, diseases, therapies, lesions, and so forth, insofar as these are salient to diagnosis and treatment. Within a referent tracking system, all such entities are referred to directly and explicitly, something which cannot be achieved when familiar concept-based systems (...) are used in what is called “clinical coding”. In this paper we describe the components of a referent tracking system in an informal way and we outline the procedures that would have to be followed by healthcare personnel in using such a system. We argue that the referent tracking paradigm can be introduced with only minor – though nevertheless ontologically important – technical changes to existing EHR infrastructures, but that it will require a radically different mindset on the part of those involved in clinical coding and terminology development from that which has prevailed hitherto. (shrink)