One goal of the Fast Healthcare Interoperability Resources (FHIR) standard is to prevent semantic ambiguities when patient data are electronically exchanged. To assess whether the FHIR specifications live up to this expectation, we examined FHIR's Condition resource thereby focusing on the resource elements 'clinical status' and 'verification status' when used in combination. We found that the definitions for these elements, as well as of several of their allowed values, suffer from the following semantic difficulties: the use of disjunctive descriptions, the presence of pseudo-synonyms lacking clear explanation of what distinctions FHIR has in mind, and insufficient discrimination between evidence and what such evidence would be about.

The Basic Formal Ontology's (BFO) current approach to 'history', in contradistinction to how the referent of 'history' has been described in scholarly work upon which the BFO is built, is quite reductionist: it allows only material entities to have a history, and what contributes to their histories is restricted to what takes place in the spatiotemporal region 'occupied by' the material parts. This has as one consequence that certain processes in which a material entity participates are not part of its history. In addition, the BFO is silent about whether instances of other types of continuants have a 'history'. We explore how this situation came to be and propose two alternative versions for 'history' inclusive to all sorts of continuants currently recognized by the BFO.

Substance use disorders (SUD) remain prevalent in the United States. The Office of Addiction Services and Support plays a critical role in tracking SUD trends in New York State and reports data to the federal system. However, ambiguities in substance classification pose challenges to data accuracy and consistency. To address these issues, we developed the foundations for the Addiction Substance Ontology (ASO) using Basic Formal Ontology principles. Definitions in the ASO are expressed in terms of genus and differentiae which form the backbone for a taxonomy of substances in function of their chemical composition and certain other characteristics essential for tracking their acquisition and use. While 143 classes have been developed thus far based on a specific program admission use case, pilot testing and stakeholder collaboration are necessary to refine the ASO and validate its application in real-world settings. These efforts aim to improve data reliability, enhance tracking of SUD patterns, and support effective public health interventions.

The Basic Formal Ontology (BFO) is an upper ontology that embraces both continuants and occurrents. Continuants can persist through time while undergoing changes through their participation in processes. Processes are held not to change as they are said to be changes. Yet, the BFO is silent about what sorts of changes might exist: history is the only type that is subsumed by process. Although representing and tracking instance data by means of the BFO's time-indexed relations allows one to infer that some change must have happened in the portion of reality described by the data, change is not explicitly represented. When a change exists, there must be a change of something. However, when the color of that flower (a quality inhering in, but distinct from, that flower) instantiates red at one time, and brown at a later time, then that change, alone, is not a process under the current definitions and axioms of the BFO. This is because qualities can participate in a process p, but never by itself: p must have a material entity as participant. Furthermore, processes can only have other processes and process boundaries as parts; if the BFO would accept the change of qualities, or specifically dependent continuants in general, as occurrents, though not processes, then such change cannot be occurrent-part-of a process. In this paper we explore the basis of a theory, and the beginnings of an axiomatization thereof, as an extension to the BFO that recognizes change as a subtype of occurrent so that instances thereof happen-in processes and happen-to continuants whereby these continuants participate in the processes these changes happen-in. We anticipate re-expressing the ideas presented here as axioms expressed in terms of processes and participation in a future revision of the BFO-FOL axioms that currently prevent a tighter integration.

The Basic Formal Ontology (BFO) class continuant fiat boundary and its subclasses, including fiat surface, are not heavily axiomatized; they have elucidations, not definitions; and the meanings of these elucidations are poorly captured by the relevant BFO axioms. This paper is an effort to make progress in these respects for fiat surface. We identify a range of desiderata for a BFO-conformant view of fiat surface, argue that the GitHub does not satisfy them, argue that the view of fiat surfaces in Arp et al. (2015) does a better job, and supplement that view in ways that do a still better job. Our discussion allows us to, inter alia, present a number of axioms relevant to our topic worthy of consideration for inclusion in BFO or dependent ontologies in specific domains and for specific purposes.

In a proof of concept study, we assessed the feasibility of designing a first-order logic (FOL) framework capable of translating SNOMED CT's terminological view on patient data as referencing concepts, into the realism-based view of the Basic Formal Ontology and the Ontology for General Medical Science according to which patient data represent instances of types. Because within the subject domain of this study, SNOMED CT's terminological coverage was excellent, and its EL++ axioms can be automatically translated into FOL as well as the antecedent part of bridging axioms between SNOMED CT and realism-based ontologies, we conclude that this is an area of R&D that deserves further attention and that may lead to new ways of federating terminologies with ontologies.

Adequately representing kinship relations is crucial for a variety of medical and biomedical applications. Several kinship ontologies have been proposed but none of them have been designed thus far in line with the Basic Formal Ontology. In this paper, we propose a novel kinship ontology that exhibits the following characteristics: (1) it is fully axiomatized in First Order Logic following the rules governing predicate formation as proposed in BFO2020-FOL, (2) it is modularized in 6 separate files written in the Common Logic Interface Format (CLIF) each one of which can be imported based on specific needs, (3) it provides bridging axioms to and from SNOMED CT, and (4) it contains an extra module with axioms which would not be literally true when phrased naively but are crafted in such a way that they highlight the unusual kinship relations they represent and can be used to generate alerts on possible data entry mistakes. We describe design considerations and challenges encountered.

SNOMED CT is a large concept-based terminology designed according to epistemic, semantic and pragmatic principles relevant to clinicians. Its goal is structured clinical reporting in electronic healthcare records (EHRs). The Basic Formal Ontology (BFO) is an ontology designed on the basis of types claimed to exist in reality based on a domain-independent ontological theory. Its goal is faithful representation of reality within that theory. The Ontology for General Medical Science (OGMS) extends the BFO by providing definitions for types relevant within the clinical domain. Combining SNOMED CT with the ontological rigor of BFO and OGMS might improve clinical reporting by, f.i., preventing data entry mistakes and inconsistencies, and make EHRs more comparable. To that end, we are developing a logical framework capable of exploiting what SNOMED CT offers terminologically and realism-based ontologies such as the BFO and the OGMS ontologically by means of bridging axioms compatible with the BFO, and expressed in the same CLIF-dialect as used in its axiomatization in first order logic. In this paper, we report on our attempts to detect in the combinations of binary relations that are used in the definition of SNOMED CT’s definitions of disorder concepts patterns which might at least partially automate the construction of such axioms. Our findings suggest that this partial automation is indeed possible, but to a smaller extent than we had hoped for. We compare our approach with a recent proposal that seeks to bring SNOMED CT and BFO closer together by reinterpreting SNOMED CT disorders as clinical occurrents. The proposal has its merit in providing a realist underpinning for that part of SNOMED CT’s concept model in terms of the BFO, but is not discriminatory enough for an automatic translation into OGMS. Key problem is the lack of face validity of SNOMED CT disorder terms as compared to the formal definitions they are given and this in absence of textual definitions.

Terminological systems, including coding and classification systems, are used in electronic medical record systems to facilitate the interpretation of structured data by providing terms and codes with a relatively precise meaning. When a clinician selects a term or code from such system and enters it in the medical record of a patient, then, from an ontological perspective and as a consequence of how ter-minological systems are currently integrated in electronic medical record systems, an assertion has been made to the effect that the patient exhibits, or exhibited, some phenomenon of type T. It is however left unspecified which phenomenon in particular is of the designated type T. In other words: such records contain explic-it references, i.e. the terms or codes, but the referents of these references are not explicitly identified! Because referents can be referenced in many different ways, types used as references can be about many referents, and referents may change so they become of a different type, data analytics application which rely on types only are prone to drawing erroneous conclusions. Referent Tracking is a method-ology for data management which allows assertions only to be made with explicit reference to the referents they are about. This chapter offers an introduction to the principles upon which the methodology rests and how these principles can be applied to improve the quality of the problem list in medical records.

Much progress has been made over the last 30 years in the creation, storage, and use of formal ontology models. Many have contributed to this great enterprise. There has been hundreds of millions of dollars spent on the activity, and this has led to the availability of larger and better curated datasets today in the brave new world. This has been complemented by better methods and by improved data in terms of both its comprehensiveness and its complexity. New methods in data governance, data provenance, and data cleaning have provided us with the ability to utilize data science methods for improved predictive analytics. For data-driven recruitment to clinical trials and for automation of phase IV trials, keeping our populations safe while encouraging advancement of science and technology.

The objective of this paper is to propose formal definitions for the terms 'protein aggregate' and 'protein-containing complex' such that the descriptions and usages of these terms in biomedical literature are unified and that those portions of reality are correctly represented. To this end, we surveyed the literature to assess the need for a distinction between these entities, then compared the features of usages and definitions found in the literature to the definitions for those terms found in Bioportal ontologies. Based on the results of this comparison, we propose updated definitions for the terms 'protein aggregate' and 'protein-containing complex'. Thus far, we propose the following distinguishing factors: first, that one important difference lies in whether an entity is disposed to change type in response to certain structural alterations, such as dissociation of a continuant part, and second that an important difference lies in the ability of the entity to realize its function after such an event occurs. These distinctions are reflected in the proposed definitions.

Objective:to identify on the basis of a use case major problem types novices in realism-based ontology design face when attempting to construct an ontology intended to explain differences and commonalities between competing scientific theories.Methodology:an ontology student was tasked (1) to extract manually from a paper about five distinct motivational learning theories the scientific terms used to explain the theories, (2) to map these terms where possible to type-terms from existing realism-based ontologies or create new ones otherwise, (3) to indicate for new type-terms their immediate subsumer, and (4) to document at every step issues that were encountered.Results:where term extraction and type-term assignment were handled satisfactorily, correct classification in function of the BFO was a major challenge. Root causes identified included ambiguous and underspecified term use in the theories, the ontological status of psychological constructs, lack of high quality ontologies for the behavioral sciences and insufficient 'deep' understanding of some BFO entities, in part because of insufficient documentation thereof suitable for learners. The issues the student encountered were often insufficiently described for the instructor to identify the problem without analyzing the source paper itself.Conclusion:whereas behavioral scientists need to do efforts to make their theories comparable, realism-based ontologies can help them therein only when ontology developers and educators put more effort in making them more accessible without violating the principles.

A diagnostic process is an investigative process that takes a clinical picture as input and outputs a diagnosis. We propose a method for distinguishing diagnoses that are warranted from those that are not, based on the cognitive processes of which they are the outputs. Processes designed and vetted to reliably produce correct diagnoses will output what we shall call ‘warranted diagnoses’. The latter are diagnoses that should be trusted even if they later turn out to have been wrong. Our work is based on the recently developed Cognitive Process Ontology and further develops the Ontology of General Medical Science. It also has applications in fields such as intelligence, forensics, and predictive maintenance, all of which rely on vetted processes designed to secure the reliability of their outputs.

Several ontologies represent entities pertinent to the domain of medicinal drugs. An analysis of these ontologies and the related literature shows that they primarily do so from the perspective of treatment and that the definitions for many of the core entities fall short when applied to drug discovery in general and drug repurposing in particular. We therefore redefined or created new elucidations and definitions for terms which are most important to understanding what is meant by ‘drug repurposing’ using guidelines of ontological realism, thereby making judicious use of the Basic Formal Ontology, the Ontology for Biomedical Investigations, the Ontology for General Medical Science, and the Drug Ontology. We tested the appropriateness of these modifications for the description of a use case on what is involved, and inferred when using the Computational Analysis of Novel Drug Opportunities (CANDO) drug repurposing platform. We found that the definitions proposed remove some of the shortcomings of other ontologies but that still more work is needed to address all issues.

A method is described to use SNOMED CT's history mechanism as a means to compute how the formal and linguistic intensions of its concepts change over versions. As a result of this, it is demonstrated that the intended principle of concept permanence is not always adhered to. It is shown that the evolution of formal intensions can be monitored fully automatically and that the proposed procedure includes a method to suggest missing subsumers in a concept's transitive closure set by identifying mistakes that have been made in the past. Changes in linguistic intensions were found to be much more labor-intensive to identify. It is suggested that this could be improved if the history mechanism would come with more detailed motivations for change than the current and insufficiently used annotation to the effect that a fully specified name 'fails to comply with the current editorial guidance'.

The Informatics for Integrating Biology and the Bedside (i2b2) software platform has proven successful in leveraging clinical enterprise data for the identification of cohorts of patients satisfying certain demographic, phenotypic and genetic criteria in support of further studies. An unanswered question thus far is whether i2b2 search criteria could include characteristics of assertions themselves, e.g. diagnoses, rather than what the assertions (observations) are about, e.g. diseases. This would allow, for instance, to find cohorts of patients for which different providers have been in disagreement about what condition the patient is suffering from. Previous research has shown that this requires more explicit detail about, and unique identification of, two sorts of entities: those that directly or indirectly contribute to the coming into existence of such observations and those that are either explicitly mentioned or merely implied in the assertions. Our research here demonstrates that i2b2's modifier system can be used to represent the relationships between observations and their explicit or implied referents on the one hand, and between relevant referents themselves on the other hand, both in combination with the storage of explicit unique instance identifiers for these observations and referents in i2b2's fact table. While this approach adheres to i2b2's base functionality and implementation specifications, it makes explicit ambiguities and confusions that would otherwise remain undetected.

The fully specified name of a concept in SNOMED CT is formed by a term to which in the typical case is added a semantic tag. The latter is meant to disambiguate homonymous terms and to indicate in which major subhierarchy of SNOMED CT that concept fits. We have developed a method to determine whether a concept's tag correctly identifies its place in the hierarchy, and applied this method to an analysis of all active concepts in every SNOMED CT release from January 2003 to January 2017. Our results show (1) that there are concepts in almost every release whose semantic tag does not match their placement in the hierarchy, (2) that it is primarily disorder concepts that are involved, and (3) that the number of such mismatches increase since the July 2012 version. Our analysis determined that it is primarily the absence of a mechanism in the SNOMED CT authoring environment to suggest stated relationships for very similar concepts that is responsible for the mismatches. We argue that the SNOMED CT authoring environment should treat the semantic tags as part of the formal structure so that methods can be implemented to keep the sub-hierarchies in sync with the semantic tags.

In a series of recent publications, orofacial researchers have debated the question of how 'bruxism' should be defined for the purposes of accurate diagnosis and reliable clinical research. Following the principles of realism-based ontology, we performed an analysis of the arguments involved. This revealed that the disagreements rested primarily on inconsistent use of terms, so that issues of ontology were thus obfuscated by shortfalls in terminology. In this paper, we demonstrate how bruxism terminology can be improved by paying attention to the relationships between (1) particulars and types, and (2) continuants and occurrents.

Diagnoses recorded on the problem list are increasingly being used for decision support applications. To obtain insight in the adequacy of the clinical user interface to capture what the clinician has in mind, and to reconstruct the clinical reality of the patient, we analyzed in the database of an EHR system the transactions that resulted from managing the problem list. Our findings indicate (1) that caution is required when using the evolution of the problem list for determining comorbidity or ongoing disease, and (2) that similarities or differences in problem list annotation sequences do not always correspond with similarities resp. differences in disease courses. It is to be investigated whether automatically identifiable subsets of problem list evolution patterns exist from which ground truth reliably can be inferred or whether clinicians need more education in how problem list user interfaces should be used to avoid erroneous interpretations by clinical decision support applications.