While ontologies have been embraced by many sciences, developing, establishing, and sustaining them has been a significant challenge in the behavioral sciences. Describing and measuring behavioral phenomena is inherently complex, and even in many subfields of study there is a lack of well-established, widely shared definitions for key concepts. Researchers in the behavioral sciences seek to describe observed phenomena, to understand the causes for those phenomena, to use this knowledge to improve psychological and physical health outcomes, and to predict who might be affected in the future. Yet the distinctions between describing and explaining observed phenomena are not always clear. Even defining the boundaries of the behavioral sciences is challenging. Growing evidence from the study of development, for example, has shown how biological and social influences interact even prenatally to shape gene expression as well as physical and cognitive development. This reality points to the critical importance of research that integrates work from across and beyond the behavioral sciences.
To gain a more detailed understanding of how ontological systems function in the behavioral sciences the committee explored several examples. We look first at an example of a formal and explicit ontology that lies on the far right of the continuum of semantic formality shown in Figure 2-1. We then examine three different systems for classifying mental health problems.
There are very few ontological systems in the behavioral sciences that have been developed using standard representation languages (such as the Semantic Web language developed for ontologies, Web Ontology Language, or OWL). One that does, the Behavioral Change Intervention Ontology (BCIO) is example of what a behavioral science ontology looks like when constructed by practitioners in the field using a standard representation approach.
The BCIO is being developed as part of the Human Behaviour-Change Project at University College London. The developers were responding to a problem with behavior change interventions: that they are heterogeneous in context, content, and methods of evaluation, and this heterogeneity makes it difficult to synthesize evidence, develop real-world policy, and design practical applications. Especially concerning was the lack of common terms for such interventions, which is evident in the proliferation of theories and concepts in the relevant research. The BCIO developers used a multistep process to build their ontology. Although the approach taken by the BCIO team was tailored to the particular challenges of creating this ontology, it is representative of ontology engineering practices commonly used in the sciences, and it provides insight into the tremendous human effort needed to develop a practical ontology of even modest scope.
The BCIO team identified 12 entities as central and common to all behavioral interventions (intervention, content, delivery, mechanism of action, exposure, reach, engagement, context, population, setting, behavior and outcome) and a subset of related concepts (source, mode, schedule, dose, fidelity, and adherence). Experts were asked to rate the extent to which entity names were clear, definitions were non-overlapping and without redundancy, relationships were suitable, and the overall structure was clear. This feedback was discussed and incorporated into further refinements of definitions until consensus was reached, and the results were shared with a wider team that included systems architects and computer scientists. The resulting behavioral interventions ontology has two types of behavioral change interventions, each with its own set of associated entities. The entities are related by 19 ontological relationships, such as has part, subclass of, has attribute, has disposition, has process part, evaluates, has output, is about, difference between. Each of the entities in the final version of the ontology has a parent class that relates to concepts from external ontologies developed by other groups. The final step in the construction of the BCIO ontology development was the establishment of a change-management and version-tracking strategy.
The BCIO illustrates the value of a formal and explicit ontology in the behavioral sciences because it provides a way to move beyond a prevalent
challenge in the discipline: teams of researchers who work in silos, using data that are incompatible with data used by others in similar domains. It allows for integration of evidence when BCIO terms are shared by diverse groups of investigators to describe their data, and it makes the evidence searchable when the same ontological terms appear in both the underlying data descriptions and in the queries with which users perform their searches. Computers potentially can reason about behavioral intervention data to discover new relationships, to develop novel hypotheses, and to expose gaps in the evidence.
Three influential classification systems are frequently used in the behavioral sciences: (1) the Diagnostic and Statistical Manual of Mental Disorders (DSM), (2) the Research Domain Criteria (RDoC), and (3) the hierarchical taxonomy of psychopathology (HiTOP). While each of these is an ontological system, none has the level of formal specification that would make it compatible with representation in a logic that supports computer-based reasoning. A review of these systems highlights several of the challenges inherent in moving toward the use of more standard and more computational ontologies in the behavioral sciences.
The Diagnostic and Statistical Manual of Mental Disorders (DSM) has been the primary tool for classifying mental disorders since the early 1950s.1 Its purpose is to support diagnosis and treatment of, and research about, mental disorders. As a classification system, the DSM can be used to identify whether or not individuals meet specified criteria for a mental disorder. That is, it treats disorders as categorically (qualitatively) distinct from one another.
The most recent version of the DSM, the DSM-5, contains 22 classes or categories of disorders and other conditions that may be a focus of clinical attention. Under each of these major classes of disorders is a set of hierarchically organized disorders, each with its own code. The DSM provides a disease code for each diagnosis in order to group sets of related disorders or manifestations of a disorder. For example, all the diagnoses in the anxiety disorder class differ from diagnoses in the other classes on the basis of distinguishing characteristics of each class.
1 See https://www.psychiatry.org/psychiatrists/practice/dsm/history-of-the-dsm for an account of the history of the classification of mental disorders in the United States.
The DSM is an ontological system because it defines the entities it enumerates, although it is not as rigorously formalized as the BCIO. For a classification system to fall on the right side of the continuum described in Chapter 1, it would have clear, named relationships among the entities in the ontology and the definitions for the entities would be expressed in a computable logic.2 The DSM is placed to the left of the BCIO on the continuum because it represents classification of mental disorders using a common language and standard criteria.
Despite the DSM’s usefulness for clinical and other purposes, many of its categories do not have a firm scientific base. For example, the diagnostic categories in DSM do not align with findings from clinical neuroscience or genetics. Furthermore, DSM classification is based on behavioral phenotypic manifestations of underlying disorder (i.e., symptoms), not on pathophysiology. People who meet the criteria for many DSM disorders may in fact share very few common symptoms. To illustrate, the DSM criteria for diagnosis of major depressive disorder specifies that a patient must have five of nine specified symptoms. Thus, two people both diagnosed with the disorder may have only one symptom in common.
Another issue is that many patients meet criteria for more than one mental disorder, leading to concerns that disorders are inappropriately studied in isolation from each other. Researchers have developed their thinking about disorders, no longer regarding them as distinct categories but rather as sharing underlying dimensions that explain their covariation. The categorical emphasis still present in the DSM has led to problems, such as researchers or clinicians choosing not to enroll patients with more complex diagnoses in research trials because they wish to focus on a single diagnosis rather than the relationships among diagnoses. For these reasons, the DSM’s dominance in the field has likely hindered scientific progress. However, the DSM system is still considered the authoritative system by which mental disorders are classified in the United States and many other countries.
Tying observed or self-reported signs and symptoms at the behavioral phenotypic level more closely to biological processes was the objective when the National Institute of Mental Health developed a new system for categorizing mental disorders. The goal of the resulting system, known as the Research Domain Criteria (RDoC), was not to create a new diagnostic
2 In contrast with the DMS, the Ontology of General Medical Science organizes medical diseases using such terms as bodily component, bodily feature, bodily process, bodily quantity, and the like. For classifications of mental disorders to follow this approach, it would be necessary to link the observed signs and symptoms to bodily (brain) processes.
system that would compete with the DSM, but rather to devise alternative criteria to be used for purposes such as peer review for grant applications. This new research-focused approach would support investigators and reviewers in thinking outside the boundaries of the DSM. For example, an investigator could research underlying dimensions (e.g., the symptom anhedonia, which is a feature of more than one category) that cut across multiple DSM categories, rather than being constrained by the need to design research that aligned within DSM categories.
RDoC includes six domains (e.g., negative valence, positive valence) and seven levels of analysis (e.g., genes, physiology, self-reports; see Figure 4-1) and provides a framework to understand the nature of mental health and illness in terms of varying degrees of dysfunction in general psychological/biological systems, but it does not represent the same degree of formal specification as other examples with strong semantics.
RDoC is intended to facilitate research that investigates fundamental dimensions, grounded in biology, that span multiple disorders (e.g., response to threat, attention, social processes); examines the full range of variation, from normal to abnormal; integrates genetic, neurobiological, behavioral, environmental, and self-report measures; and develops reliable and valid measures of components for basic and clinical studies. We note, however, that the assumption that fundamental dimensions are those grounded in biology implies that there is a single causal mechanism for the
phenomena being accounted for. However, behavioral phenomena may be caused by a number of mechanisms.
A third approach to the classification of mental disorders uses factor analysis.3 Rather than relying on the consensus of expert committees (as with the DSM), researchers using this quantitative approach seek to identify consensus from existing studies using factor analysis. This approach is most recently exemplified in the work of the hierarchical taxonomy of psychopathology (HiTOP) consortium.
The HiTOP model is premised on the idea that psychiatric signs and symptoms are captured by underlying dimensions of internalizing disorder, externalizing disorder, and thought disorder, rather than categorical diagnoses as described in the DSM (and other similar models). HiTOP aims to facilitate the search for causes and mechanisms of psychopathology by identifying coherent constructs that can be measured reliably (because the strong factorial measurement model has reliably established them) and are thus more usable and informative than traditional diagnoses. Thus, in the same way that the RDoC supports use of a psychophysiological dimension (e.g., negative valence system to understand depression rather than a heterogeneous depression diagnosis), HiTOP also supports the use of a dimension, such as the dimension of negative symptoms rather than highly heterogeneous schizophrenia diagnosis.
Hierarchical relationships in HiTOP allow for improved classification of psychopathology dimensions to facilitate research and clinical practice. However, HiTOP is mainly an empirical organization of psychopathology to support classification of psychiatric disorders. HiTOP, like the DSM and the RDoC, adheres to a hierarchical structure, which is an indicator that a classification system has specified relationships among the entities it defines. However, it should be noted that factor analysis, per se, does not result in a formal specification. While it describes the relationships between variables (through its covariance matrix), the empirical approach bypasses any kind of shared conceptualization.
Both the DSM and the RDoc are based in shared conceptualizations of phenomena, although they both lack formal specification of their content. In contrast, HiTOP is not based on any conceptualization. Rather, it offers
a clear specification, not one with any formal semantics, but one derived from standard statistical inference. These three examples are each influential and demonstrate some points that arise in the development of ontological structures for the behavioral sciences. First, in choosing a representation system—and the degree of formal specification needed—developers are choosing a language with the expressivity needed for the purpose the ontology is designed to serve. Second, ontologies that incorporate relatively strong semantics support use of the ontology to create a knowledge base that goes beyond the simple enumeration of classes and taxonomic relationships. Finally, ontologies are important tools for identifying gaps in the way researchers talk about a discipline, for developing formal definitions when they are lacking, and for testing those definitions to see if they are useful for their intended purpose.
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