Causality and the ontology of disease

3.1.On pattern, causal-chains and disease

Given that causation involves recurrence and regularity, it is reasonable to believe that the category of Pattern is applicable. Both causal chains and patterns require parts (broadly construed) that stand in certain relationships to one another. Patterns and their instances take various forms. Clinicians and researchers, for example, observe recurrent patterns among disease entities, including their effects, signs and symptoms. Some can be characterized as patterns of: bio-causal-chains, bio-causal-processes, or biological processes that are causally-linked.

Biological activity and the stability of entire living systems have been described as being dependent upon the interdependent and interconnected character of separate processes and events (Lillie, 1940, p. 331). That is, the repeatability of “complex chains and networks of causation … the regular features of causation are thus responsible for the stable side of organic life” (Lillie, 1940, p. 318, italics added). Patterns in nature exemplify this stability in life. And given that causation is typically held to relate occurrences in the world, processual entities – biological and abiotic – are major factors in the stability of life (a point we will see expressed in the RFM).

Various, if only partial, pattern-based descriptions of disease types are conceivable: disease as exhibiting, being constituted by/of, or at the class-level being a subtype of one or more patterns of pathological changes. A pattern of change, which is a pattern of occurrences or processual entities, is more generally a unity of changes that stand in certain spatio-temporal–causal relations to one another, the whole of which – the pattern – is exhibited time and again by distinct particulars. Disease, as a pattern of change(s), involves recurrent causally-related pathological processes (and their continuant participants). The basic idea is that bio-causal relations unify (in some sense) disease entities into a whole, e.g., a pattern, one or more interconnected biophysical causal chains, etc. Hucklenbroich (2014) describes a disease entity (type or pattern) as “a pattern of pathological behavior of the organism” and asserts: “disease entities are always patterns, i.e., sets of different courses”. We will briefly return to this idea in Section 6.

We should note, however, that one can question whether there is mind-independent unification between all the parts/elements of a pattern (and causal chain) that would justify the characterization as a whole. One can also question whether disease is a natural kind or universal. The final subsection of this excursion into causality presents a philosophical analysis of the general idea of causal chains.

3.2.Causal chains

The ordinary idea of a causal chain (C-C for short) and its application in discourse conveys a connected and ordered sequence of entities, and is often metaphorical. C-C’s are typically conceived of as some occurrence causing another, which then causes another, and so on over time. Visual representations of causal chains often take the form of unidirectional arrows connecting alphanumeric characters or shapes. The latter, also called ‘nodes’, represent causal relata, whereas arrows represent causal relations. A C-C is not a mere sequence or succession of occurrences just as the relationship between cause and effect is neither mere correlation nor regular constant conjunction. Rather, the connection is a stronger relationship between that which the links in the chain represent (the causal relata). In a sense, C-Cs are relational entities: without causal relations among relata a causal chain would cease to be what it I; their existence requires certain relational arrangements obtain.

The ontology of causal chains includes: (1) the C-C as a whole; (2) the links (relata) in the chain as the parts of the whole (in some sense of parthood); and (3) the causal relationship(s) between relata. The latter may be a generic causation relation or more specific causal relations.

Both C-Cs and sequences involve a following of one entity after another; they are ordered and have direction. The directionality of causality, and therefore of C-Cs, in the world is implicitly understood as the direction of time; that is, the asymmetry of time entails a direction. In epidemiology, direction is considered an essential causal property that indicates “consequential change” of and from cause to effect (Susser, 1991, p. 639).

By contrast, a literal chain taken end for end does not have a preferred direction. It exhibits directional symmetry such that opposing spatial paths can be traced along it. This is due to the compositional structure of the chain: being a material (and thus spatially-extended) object. A C-C is representative of a spatio-temporal path from the beginning of the causal sequence to the end. Many causal chains, then, are lawfully connected, ordered and directed sequences of entities. For example, disease causal chains (DCC) are directed toward pathology and can be interconnected. Although intuitions on causal chains would have them be almost exclusively sequential, representing simultaneous causation (Huemer & Kovitz, 2003) is not uncommon. Furthermore, biomedicine requires the understanding of causal feedback mechanisms and cyclical causation. We restrict the focus here to unidirectional and simultaneous causation.

Each link (node) in a C-C may be viewed as the cause/effect for the subsequent/preceding link.66 The adding of links signifies: that some temporally preceding, overlapping, or simultaneous link (relatum) has changed or has caused the newly added link. A change has occurred in the world in virtue of the causal interaction, or perhaps the causal powers of the relata (plus any other relevant conditions).

These links need not be interpreted as representing discrete (rather than continuous) causal relata. Just as the links in an actual chain overlap one another through the holes they form, so can the relata overlap. A causal scenario may involve continuous, simultaneously occurring, causally-connected or overlapping processes with vague (if any) spatio-temporal boundaries. This is often the case in biology where crisp spatio-temporal boundaries are difficult to discern, and where complex causal interactions takes place over time.

Toward formalizing a generic Causal Chain class (C-C FOL), a first-order formalization is as follows. For the moment, assume relata are processual entities, ignore temporal arguments, and keep the parent class of Causal Chain open. Lower case letters signify individuals, bold terms classes, and italicized terms relations. ‘instance_of’ signifies the instantiation relation between classes and their instances. (C-C FOL) says that instances of causal chains have occurrent parts that cause one another in some manner, sequence, etc.

To the extent that the relata are parts of a C-C, the mereological relation of Parthood is applicable, but not without complication. If we assume parts of a whole must be of the same ontological category as the whole, then the following holds. An Occurrent C-C is a group of causally-linked occurrent parts. Additional identity criteria for this class may include temporal contiguity of the processes, whereby c1 meets or overlaps c2 (Allen, 1983).77 This can be added as a fourth conjunct (iv) to C-C FOL. The important point is that the relata (c1 through cn) stand in causal relations, or otherwise involve causal processes. Note that the RFM considers the smallest causal chain to be a single entity (e.g. disorder, abnormal state, etc.). This limiting case can arguably be ontologically justified by considering the single entity to have some causal power or influence, but we will not explore this here.

Now, if we do not assume parts are the same type as the whole, then another relation is needed. If c1…cn and Causal Chain are mutually disjoint, or have different identity criteria, then a species of the formal ontological relation of Constitution (or a new relation) can relate them. It allows entities of distinct ontological categories to coincide in some manner.

For example, assuming occurrent relata: a continuant C-C is Constituted by causally-connected Occurrents. This may clash with the intuition that C-Cs are processual, but it is how the RFM models DCCs. Although this is an area for further development and clarification, at present, the RFM asserts a constitution relation to form Dependent Continuant diseases from C-Cs of Occurrents. Formal properties (A1), (A2) and (A3) from Table 2 are also are adopted by the RFM for its causal relation. In what follows, a general causation relation (causes and is_caused_by) is primitive, with the above content acting as an elucidation.

Finally, the concept of disease C-Cs appears similar to that of biological pathways. Biological pathways are series of molecular interactions causing specific biological processes. The representation of the possible reactions of an organism is “grouped into causal chains to form pathways” (Joshi-Tope et al., 2005). Likewise, Kozaki et al. (2015) describe how their browsing interface visually portrays potential disease courses (reactions, effects, etc.).

A representation of the full causal complexity inherent in biology and medicine may be a directed web or network of pathways and nodes, but for medical practitioners selectively focusing on specific biological interactions will likely be the task of the day. This focus is not uncommon in causal attribution and causal determination in general. After all, “a ‘complete’ causal account of an effect would be unmanageable or even impossible ever to achieve. The causal chain, or, to use a more holistic metaphor, the causal web, may extend indefinitely” (Raisanen et al., 2006, p. 294).

Like Causation, the general category of Causal Chain is broad, and applicable to most, if not all, domains and levels of granularity. A C-C class and one or more causation predicates (relation terms) can therefore take a place in the class and relation hierarchy of domain-neutral ontologies. Causal Chain, for instance, may be a subclass of Causal Structure (broadly construed). Specialized categories and relations, with domain-specific causal relata, can be included in domain ontologies as needed. Let us move on from this general presentation of causality to begin our formal discussion of the RFM by clarifying its central analogy.

5.1.The River Flow Model disease definition

The RFM is neutral with respect to the cornucopia of theories of causation. It focuses on knowledge and intuitions of causality at the mesoscopic scale of daily occurrences, as understood in biology and at the relevant biological scales. If we accept that “[…] in medical contexts causality talk has the same character as everyday causality talk” (Johansson & Lynoe, 2008, p. 127), and that this mirrors (to some degree) the causal structure of the world, then we can apply causal notions to finer biological scales.

A causation relation (causes) is a primitive in the theory, but adopts axioms (A1)–(A3). The is-caused-by relation (found in the RFM implementation) is the inverse. We define RFM causal chains in terms of these primitive causal relations, while including others. More specialized causal relations may be formulated as needed. Bio-causal relations, for instance, can be asserted based on the biological scale at which the C-C in question occurs. On this note, consider that homeostasis at one scale may, itself, be due to causal chains at another (finer or coarser) scale.

We unpack the original natural language disease definition (RFM-NL) by discussing the relevant ontological categories as they appear therein.

The supposed ontological classes mentioned in this definition are: Dependent Continuant, Clinical Disorder, Human Body, Causal Chain; Constitutes, and Initiates, respectively. At face value, a first-order formalization of RFM-NL is:

The actual formal implementation of the RFM, however, utilizes classes not mentioned in the NL definition (and vice versa). RFM-NL is at least acceptable as a shorthand, expressing the main idea of the theory, for the more complicated implementation.

As a subclass of Continuant, a Dependent Continuant (YAMATO: Dependent Entity)88 retains its identity over time, and is existentially dependent on some other entity(s). Examples include, but are not limited to properties, qualities, functions and roles. Without a particular organism, an instance of a disease (a dependent continuant) would not exist. The formal ontological relation of Inherence is used in foundational ontologies such as DOLCE (Masolo et al., 2003) and BFO, and relates objects (property-bearers) to their features. Generalizing to any organism, a disease is dependent on an Organism and/or the organism parts (the bearers of the disease).

To say a disease is Constituted of causal chains of clinical disorders is to say that a C-C class groups the causally-linked entities into a unified whole. “The course of a disease is a unity of such entities that is characterized by abnormal functioning” (Schulz & Johansson, 2007), and related pathological processes causing that dysfunction. Constitution relates potentially compresent or coincident entities of disjoint categories. It specifically relates disease (continuant) to causal chains (occurrents) (Fig. 1). Constitution is understood in the sense of DOLCE as an irreflexive relation, but with the caveat that the domain and range may need adjusting to include different ontological classes. The RFM uses a constitution (or similar) relation in part because the continuant and occurrent classes spatio-temporally coincide. Referents of the latter helping to sustain that of the former.

Fig. 1.

The constitution of the RFM causal structure class and causally-linked occurrents. The rounded rectangles signify that, depending on the disease, each causal sequence of occurrents can be represented as a particular causal chain. (Colors are visible in the online version of the article; http://dx.doi.org/10.3233/AO-150147.)

‘Causal chain’ has been defined as: “a chain of entities linked by causality” (Mizoguchi et al., 2011, p. 64). A more recent definition is: “a chain of entities linked by causal relation[s]” (Mizoguchi et al., 2012). As stated, this allows for a plurality of entities to compose C-Cs. At the instance level, however, RFM C-Cs include only Occurrent relata, focused primarily on YAMATO Abnormal (Pathological) States. We assert axiom (A4a–b), which expresses (ii) and (iii) from (C-C FOL), and with temporal arguments omitted. These axioms apply for the is-caused-by and has-effect-on relations as well. Changes in states (or changes in values of qualities) are YAMATO: Processes. Entire processes, not their parts or internal processes, stand in the causes relation to one another.99

Fig. 2.

RFM classes and relations in YAMATO. Solid and dashed is a arrows signify direct and indirect class subsumption, respectively. Red arrows indicate primitive causes and has effect on relations. Not all potential causal relations between occurrents are shown. Boxes are continuants, ovals occurrents. Orange marks continuant Causal Structure classes. Yellow marks the newly added/changed Causal Chain classes. (Colors are visible in the online version of the article; http://dx.doi.org/10.3233/AO-150147.)

We now present the additional categories and relations that are part of the RFM implementation. Although Causal Chain, Disorder and initiates from (RFM-NL) are not presently found as individual classes/relations in the implementation files (under development), we hereby change some pre-existing classes to better express the RFM concept of C-Cs. Within the YAMATO class hierarchy, the RFM has more than one causal class (Fig. 2).

Causal Structure (CS) is a type of Continuant and is Constituted of Occurrents, specifically types of Sequence of Occurrents, such as CLO. Due to the fact that the RFM models different causal scenarios (e.g. simultaneous, sequential, etc.), ‘sequence’ is not intended to exclusively connote linear or sequential causation. For CS and SO to exemplify the idea of a causal chain, the occurrents composing the SO must be causally-linked. As explained in Section 3, without causal relations – or an otherwise explicit statement of causality – between occurrents a SO cannot be asserted as causal. For this reason we added the Causally-linked Occurrent class to the YAMATO hierarchy. It expresses the most general occurrent class of RFM causal chains. Combined in this way, we have the RFM/YAMATO conception of causal chains.

The RFM uses different kinds of Causally-linked Occurrents to distinguish causal scenarios. For example, the ordinary conception of causal chains is one of sequential happenings. Our Sequential Causal Chain class captures this scenario. Some causal scenarios may be held to exhibit simultaneous causation in which cause and effect occur simultaneously. Strictly speaking, absolute simultaneity may be precluded by modern physics, but given that an approximation is sufficient for contemporary biology and daily life, the RFM allows for simultaneous causation. The class of Pseudo-Simultaneous Causal Chain has thereby been added (changed from YAMATOs ‘pseudo-instantaneous’) as a subclass of CLO to capture the different simultaneous causal occurrences the RFM seeks to represent. More specifically, if distinct changes occur simultaneously and without mediation, they are described as directly connected processes, or as Directly-connected Causal Chains. Consider an example.

A growing blood clot – a process with a clot as a participant – simultaneously causes the reduction of the cross section of the respective blood vessel, which simultaneously causes a reduction in oxygen supply to some organ. If left untreated, organ death will occur. At least three distinct processes occur simultaneously.1010 This is but one causal scenario to be considered in a causal account of disease. The RFM treats the (existence of the) blood clot as a state causing other states.

In the dependent entity portion of the YAMATO, Dependent Causal Structure (DCS) is a direct subclass of Specifically Dependent [Entity/Continuant],1111 a subclass of YAMATO: Dependent Entity. From a philosophical perspective, these sorts of entity are existentially dependent on specific individuals. A particular disease C-C is specifically existentially dependent on a particular organism, organism parts, and causal relata.

Disease is subclass of Abnormal Causal Structure (ACS), which is a subclass of DCS, a dependent entity/continuant. Disease, then, is a pathological causal structure. Although the implementation is complex, and in need of further definitions, there is nothing necessarily unintuitive about this particular idea.

The implementation – including OWL2 (OWL, 2014)1212 – indicates DCS is the most general class inheriting a CS. DCS can be understood as a derived class from, or defined by referring to, CS. As presented therein, the inherits relation holds between a DCS and CS, and allows the DCS to inherit the properties of CS without being the class subsumption relation. ‘Class X inherits Y’ means: Any property P obtaining for an instance/individual y of Y obtains for any instance x of X. Given that DCS (and therefore ACS) inherits the properties of CS, and CS is constituted of SO, the original idea that disease is a dependent continuant constituted of causal chains of disorders is captured. This is a complicated implementation, but there are, of course, simpler ways to formally implement this idea.

Clinical Disorders (or simply Disorders) are associated with dysfunctional or otherwise pathological functioning in organisms. There is no actual Disorder class in YAMATO. Rather, ‘clinical disorders’ in RFM-NL was intended to be broadly construed. The ‘disorder’ definition from (Scheuermann et al., 2009) is applicable if substituting ‘components’ with ‘entities’. This was intended to permit different conceptualizations of disorder to be captured by RFM C-Cs. Disorders from Scheuermann et al. (2009) and OGMS, for instance, are represented as occurrents, specifically Abnormal (or Pathological) States (AS), a type of YAMATO: State (Yamagata et al., 2013, 2014).

‘State’ has been defined as one or more time-indexed properties (Yamagata et al., 2013) that may or may not change over time. It is a snapshot of a process. A state category, like a C-C category, abstracts or groups together certain realities to be represented as a single whole. Thus, bio-causal systems can be described and symbolically represented as units.

Disease C-Cs are initiated by some disorder. This means that the first relatum of a disease C-C is a disorder (AS). Any temporal reading of “initiated by” in the definition should include a causal association because temporal connection and overlap between occurrences does not imply causal connection.

If external agents are included in a wider causal description of etiopathogenesis, they effectively cause (if only partially) the initiating disorder. For example, invading infectious foreign bodies, pathogens, harmful doses of radiation, or some combined set of factors, cause the formation of a disorder (or some other pathological entity). This external (set of) contributing causal factor(s) serves as the cause of the first link of a disease C-C, but is not included in the RFM C-C (which is strictly located within the organism).

“Appearing” expresses location.1313 Disorders and disease C-Cs are localized within organisms. They are Located In and/or Occur Within it. External causal factors are not considered part of the disease. Given that the concept of a disease C-C is not necessarily unique to human beings, generalize the definition by replacing “human body” with “organism”.

As pathological entities affect an organism, a disease spreads and the C-C changes. Depending on how the disease course unfolds – that is, what organism parts, properties and systems are affected and where – the causal chain may branch or fork … just like a river. Abnormal Causal Structure, the direct superclass of Disease, expresses this deviation from health. Figure 2 displays the central RFM classes in YAMATO as found in the January 22, 2015 version of the implementation file. The primitive causation relation is added in red.

Note that for dynamic scenarios, subclasses can be asserted with a temporally-indexed ternary is_a predicate: is_a(A,B,t)=def.∀x,t[instance_of(x,A,t)→instance_of(x,B,t)]. The has_effect_on predicate has Occurrent arguments. We differentiate it from causes in an intuitive fashion: x might have an effect on y, but it does not cause y. It captures the idea of contributing to some occurrence, or of bringing about some change. This relation, like that of ‘causes’ (or other causal relations) and ‘inherits’, requires further development.

Consistency in ontology development is important to, among other things, avoid confusion. In order to have a NL definition that coheres with the implementation (and vice versa), we present two revised RFM disease definitions. They are stated in genus–species–differentia form in order to foster a computable formalization. The first revised definition takes RFM-NL and includes locational relations, Abnormal/Pathological State and the parent class of Disease.

A FOL formalization of RFM-A is similar to RFM-FOL, but with the obvious substitutions. (i) + (ii) from (C-C FOL) express the causally-linked occurrents, and (iii) expresses the composition of causal chains.

The second definition closely matches the YAMATO hierarchy and implementation by including the classes and relations actually used in the implementation. As such this definition is more easily used as a computable definition. Using abbreviated class names, we have the following.

Let us note two points. First, logical definitions for each class removes the need for explicitly including all the relations in these definitional variations. For example, an axiom describing ACS1414 removes the need to mention inherits; and “and initiated by …” can be omitted since a formal definition of CS (should) includes a condition indicating a limiting case of one state. Second, a potential problem with RFM-B is this. If a ‘location’ predicate implies a dependency of the CS upon the organism, then this may contradict the RFM definition/classification of CS. Further development is in order.

An important class neither mentioned in the definition nor found in the current implementation (yet described at length in publications) is Core Causal Chain (Kozaki et al., 2012) (CCC). A disease type will often involve the same kinds of organs, bodily systems, processes and features. The course of a disease with its patterns of signs and symptoms vary from instance to instance while manifesting certain core patterns and changes.

To the degree that there are essential characteristics of disease, the CCCs of a disease are those causal chains essential to every instance of the disease type. They include the pathology common to a disease. If biomedical science discovers that all instances of a disease type involve some specific dysfunctional change, they should be represented using this class.

For example, cases (instance) of non-latent type 1 diabetes will involve elevated glucose and a deficiency of insulin, which give rise to a common set of effects on certain kinds of biological entity (blood vessels, kidneys, eyes). CCC serves to help identify a clinical case as an instance of a disease, distinguishing it from others. They are therefore necessary conditions, and minimum identity criteria for diseases. CCCs have Occurrents as parts, and are therefore parts of the Causally-linked Sequences of Occurrents. To express this idea we assert (A5).

Disease causal chains represent the causal structure of pathology through the full temporal range of a disease. They express the causal relationships of past to presently ongoing pathological changes. For example, RFM’s Historical Causal Chain (Mizoguchi et al., 2012, p. 3) represents causally-linked completed events.1515 The onset and early stages of a disease, the course of disease progression, and any subsequent decline in the disease (the healing stages) can be represented as parts of one or more causal chains. Table 3 summarizes the RFM concept of disease causal chain from object and process ontology perspectives.

The question ‘What is the causal structure of disease?’ can be partially answered with a description of the relevant causally-connected pathological entities (i.e. disease causal chains). An exhaustive causal representation of a disease that reflects the changes affecting a patient over time – the overall etiopathogenesis of the disease – will, needless to say, be highly intricate. Contributing causal factors, (quasi)simultaneous causation, reciprocal causation, and bio-causal feedback mechanisms will be involved. In short, considering only sequential/linear causal scenarios does not do justice to biological reality, which is more interrelated and continuous (Schulz & Johansson, 2007). Desiderata for any causal account of disease includes the ability to represent this complexity. Toward that end, systems biology may inform representations of this bio-causal complexity.

5.2.OGMS and unstated causality

Other attempts at a general account of disease include the Ontology of General Medical Science (OGMS, 2014). It draws ideas from Scheuermann et al. (2009), and uses the BFO as its top-level ontology. It distinguishes disease from disease courses, a distinction reflecting the two ontological perspectives adopted by its top-level. OGMS: Disease is a subclass of BFO: Disposition, which is an entity that is realized through some Process. Diseases as dispositions are potentially realizable via Pathological Processes, and have some OGMS: Disorder as their physical basis. At the time of this writing, the OGMS disease definition was (Disease-OGMS): A disposition (i) to undergo pathological processes that (ii) exists in an organism because of one or more disorders in that organism.

The realizable nature of dispositions is intended to account for the fact that a disease may not manifest itself, i.e., without the organism exhibiting observable signs and symptoms, at all times it exists in the organism. A disease, then, is conceived as something that is potentially manifested via pathological processes. Once manifested, it causes other disorders. Causality in OGMS is unstated, implicit or stated indirectly. OGMS: Etiological Process and Pathological Bodily Process, for instance, have been described as “producing” abnormal bodily features and disorders.

As discussed in Section 3, production is causative. Etiological and pathological processes stand in causal relations1616 to various processes, but perhaps also to abnormal bodily features and disorders (depending on the relata constraints). A causes relation may be asserted as holding between pathological bodily processes, and a production relation between, say, an OGMS process and Disorder. There are various possibilities. Figure 3 displays part of OGMS and these candidate placements of causal relations.

Fig. 3.

OGMS classes with suggested areas for causal relations (red). Rectangles and rounded rectangles represent continuant and occurrents, respectively. (The colors are visible in the online version of the article; http://dx.doi.org/10.3233/AO-150147.)

One or more causation predicates are also potential additions to, say, the Relation Ontology (RO, 2014), which is used by OGMS and BFO. Mihăilă et al. (2013, p. 3) note that although general physical causation is of interest in biocuration efforts, terms such as ‘cause’ rarely appear in domain ontologies. Terms such as ‘regulation’ and ‘stimulation’ are used instead. Both the comment annotation for RO: preceded_by, and the description of the Regulates relation for the Gene Ontology (GO, 2013), implicitly support the idea that domain-specific relations, such as Regulation, are subtypes of a broader causal relation. These causative terms communicate causal influence, and are therefore candidate domain-specific causal relations. Moreover, the RO appears to be considering some general causal predicates such as ‘causally related to’ and ‘causally upstream of’,1717 which are best treated as domain-neutral relations.

Before we discuss strategies for interoperability between the RFM to OGMS, there is one concern to mention. Although dispositions capture the idea of disease manifestation/realization, categorizing all disease as disposition appears false. Medical practitioners work with concrete biological realities, and are naturally concerned with domain-specific phenomena. Far from being the sort of entity that biologists study, dispositions (at least in a pre-theoretic sense) are intuitively of a more intangible and perhaps inaccessible nature. How are dispositions detected when they are not manifested, for instance. It may appear too detached (or abstract) from the concrete biomedical reality practitioners encounter on a daily basis. Moreover, medical discourse using ‘disposition’ or ‘predisposition’ does not necessarily indicate reference to the posited corresponding ontological categories, and does not necessarily reflect widespread (or even individual) belief in diseases as (pre)dispositions. That is, dispositional talk in biomedicine does not imply or express a general scientific (or personal) view of disease as being a type of disposition. Some disease may be accurately characterized as dispositions, but not necessarily all. As such, clinicians may find a dispositional account of disease to be counterintuitive or contrary to their scientific understanding of the phenomena called ‘disease’. A revision to OGMS may be in order.

That being said the RFM generally aims for interoperability with other ontological accounts of disease, while putting forth an alternative and autonomous account (a point we will return to in Section 5.5). If individual disease accounts succeed in providing representations of some, but not all, aspects of disease, then an application of one to the other may yield a more thorough representation of disease.

5.3.Conceptual overlap with OGMS and BFO

There are a few points of overlap between the RFM (and thus YAMATO) and OGMS (and BFO). As noted in Section 2, the River Flow Model of Diseases shares upper-level categories and distinctions with BFO and DOLCE. It specifically shares the classification of disease as a (Specifically) Dependent Continuant with OGMS (Mizoguchi et al., 2011, p. 64). A partial one-to-one mapping is therefore feasible. Another similarity is with respect to causal notions.

The RFM explicitly applies causal concepts to disease ontology development by using classes that can be formalized with one or more causation predicates. A formal account of cause–effect pairs and causation in DOLCE is found in Lehmann et al. (2004). The BFO discusses types of causal unity in BFO 2.0 (2013), but not causation or causal relations. Each ontological theory seems to share an understanding of the general idea of causal unity.

For example, according to the RFM and YAMATO, some internal processes of a solid material object help sustain (or maintain) its existence. Internal interactions and molecular bonds maintain its structural integrity. This is on conceptual par with the second kind of causal unity described in BFO 2.0 (2013), namely “causal unity via internal physical forces”, as well as Topological Unity as described by DOLCE (Guarino & Welty, 2000).

Generally speaking, causal unity helps make something a physical, but at least a material, object. As Bird (2014, pp. 24–25) states, a physical object is more than just the mereological sum or physical proximity of its parts; “some kind of causal unity among those parts is also required”. Material objects are those composed of some portion of matter. Physical objects are, more broadly, spatio-temporal wholes.

The unity of disease causal chains is partly in virtue of the causal connectedness of the interacting, dysfunctional parts, processes, and the surrounding environments. According to the RFM, a disease, like a river, is an example of this unity.

Looking more closely at OGMS classes we see another similarity. OGMS: Etiological Process, is defined as “a process in an organism that leads to a subsequent disorder” (Scheuermann et al., 2009). The definition implicitly expresses the causality at work through “leads to”. “Subsequent” can be interpreted as connoting the temporal order of a causal or causal-like relation and the occurrent relata. Finally, “etiological” has a causal meaning by definition.

5.4.Strategies toward harmonization between disease accounts

Although OGMS defines Disorder as a Material Entity (a BFO: Continuant), the RFM would represent them as Abnormal States in causal chains (Causally-linked Occurrents). These states can be interpreted as the Qualities of OGMS: Disorders, perhaps a Quality Pattern. A problem, not addressed here, is that Disorders are Continuants, but YAMATO: States are Occurrents.

Consider another idea. If OGMS persists with a dispositional classification, its Disease class can be described as an Abnormal Causal Disposition, which would be similar (perhaps equivalent) to some RFM classes.

In any event, at least two OGMS classes are candidate causal relata in RFM causal chains. The class Etiological Process represents either (a) a process causing the first link in the chain, or (b) a proper part (i.e., the first, or some other, link) of the chain. OGMS: Pathological Process serves as relata in any part of a disease C-C. Indeed, the processes mentioned in “undergo pathological processes” from the OGMS definition are causally efficacious and therefore easily accepted as C-C processes. It is not contradictory for OGMS to assert its pathological process class as causally-connected. Additionally, if the BFO (or OGMS) adds a (disease) causal chain/structure class, then it will be feasible to include them in the OGMS Disease definition.

Some Disorders may be asserted as participating in the processes that are parts of a RFM Causally-linked Occurrent or subclass C-C. A disease definition along the lines of D1 expresses the idea (Fig. 4).

Fig. 4.

The causal structure of a generic disease causal chain. The dotted yellow lines from the Causal Chain node represent an expansion/zooming. Squares are causally-linked biological continuants. Rectangles are causally-linked processes. Red arrows signify one or more causal relations. Two opposing arrows indicate mutual causal interaction (not necessarily simultaneous, but perhaps causal feedback mechanisms). These represent different ways to model causal chains. Statements about continuants causing others may be defined in terms of their participation in causal processes, for instance. (The colors are visible in the online version of the article; http://dx.doi.org/10.3233/AO-150147.)

The top part of the yellow rounded-rectangle displays a disease causal chain in terms of pathological processes, with the lower part in terms of causally-linked biological continuants. If one seeks to describe a DCC using only processes, then the top part is appropriate. Together, top and lower portions demonstrate the combining of process-like and object-like ontological perspectives (Table 3). If processes presumably play the preferred role of causal relata.

At least part of an OGMS: Disease Course is a causally-connected sequence of processes, i.e. a C-C of pathological processes. An RFM C-C class, then, groups causally-linked occurrent parts of the disease course. Since RFM Processes are state-changes, OGMS: Pathological Processes can ideally (c) be described in terms of RFM Abnormal States, and therefore play the role of causal relata in C-Cs. In this way, we have conceivable options (a)–(c) toward applying RFM concepts to at least one other disease account. A challenge that requires further consideration is in theory-specific upper-level ontological distinctions, specifically the treatments of Processes and States.

To partially address any incompatibility between YAMATO: Process and BFO: Process, consider this. If either (or both) BFO or YAMATO generalize their Process (or Occurrent classes), then they may be in a better place toward harmonization. Alternatively, if either (or both) disease account assert a Pathological Occurrent superclass (to represent types of disease occurrents) in their disease models, then an equivalence between the respective classes appears viable. This class can, for instance, subsume RFM: Abnormal State as well as OGMS: Pathological (Bodily) Process. Classes from other biomedical ontologies, such as the Gene Ontology, the Infectious Disease Ontology (IDO, 2013), and the Disease Ontology (DO, 2013) should be applicable in a similar fashion. Further research is in order.

To the extent that the RFM concentrates on the instance-level,1818 another idea relating the two theories is as follows. When a disease disposition is realized at time t, the processes beginning the disease course1919 are the first links in a disease causal chain at t. That is, the Realization Processes of OGMS disease dispositions are processes (causal relata) in RFM disease causal chains. In other words, C-Cs are the manifestation(s) of supposed disease dispositions. C-C classes can therefore be understood as involving an abstraction from instances of causally-connected occurrences in the world.

For example, if Arterial Hypertension is a disposition, then its realization process would stand in certain causal relations to other parts of the disease course directed to heart disease. If treated, the hypertension is not manifested via some elevated blood pressure process, i.e., the respective C-C or causal process does not occur. If untreated, there is high blood pressure: the C-C occurs.

The problem with this example is that if Arterial Hypertension simply is high blood pressure (not a measurement of it) or that which causes it, then instances of hypertension are continuously ongoing, abnormal processes of blood flow. There may be no observable symptoms, but these processes still occur at some scale in the organism. They remain causally efficacious upon the organism. Thus, there is always an ongoing process, not necessarily a disposition.

5.5.RFM as a non-dispositional account of disease

The River Flow Model does not require disposition classes to define disease, but allows for them. Moreover, talk of dispositions is unnecessary for some diseases. The characterization of disease as being a type of disposition may merely be a summary notion for (unknown or unobserved) causal chains, causal networks and causal processes at some scale or level of granularity. Whenever a disorder or disease is present in an organism, there is some causally influential disruption, interruption, dysfunctionality, pathology or abnormality present. There is some disordered organ, biological system, and/or some ongoing pathological process (as with hypertensive blood flows) occurring at some scale … regardless of whether there are resultant observable changes. The current state of medical science may not afford detection or measurement of certain subtle, complex or fine-grain disruptions, processes or interactions.

One or more disease C-Cs at one or more scales is constantly influencing the organism. When they cause specific pathological effects at the same (or more coarse-grained) levels, signs and symptoms become observable to the physician and patient. In short, some pathology may always be present.

Consider the blood clot example. The clot decreases the cross section of the blood vessel. The result is an abnormally narrow channel for blood to flow through. Rather than a dispositional account according to which the disorder affords the existence of a disposition, we simply state the causal realities. The clot – the existence of which already establishes some pathology and the partial causal basis for subsequent pathological entities – causes certain abnormal processes, e.g., abnormal blood flow processes. It disrupts what otherwise is normal blood flow. The dysfunctional blood flow processes continuously unfold for the duration of the clot’s existence regardless of the degree of deviation from healthy blood flow. They merely do not cause certain signs or symptoms at some level.

OGMS would describe this scenario as one of an unrealized disposition, but this may be unnecessary for some diseases because talk of unrealized dispositions could simply be a shorthand reference to the existent pathology at some finer biological scale, or to some as yet unknown phenomenon or causal interaction. What is actually happening is a constantly present/occurring pathology at different levels of description, not all of which are necessarily known or understood. The clot and altered blood flow may eventually cause some pathological condition2020 at another scale that is observable (signs) or that does satisfy some clinical threshold. Having a disease without exhibiting signs, or without having observable pathological processes, does not necessarily mean the organism bears an unrealized (disease) disposition. If the preceding line of reasoning is correct, then not all diseases are types of disposition.

Regardless of the classification or ontological description of disease, ultimately what is important is the degree to which the ontological characterization can help support the prevention and curing of illness/disease.