An ontological modeling approach for abnormal states and its application in the medical domain

Abstract

Background: Recently, exchanging data and information has become a significant challenge in medicine. Such data include abnormal states. Establishing a unified representation framework of abnormal states can be a difficult task because of the diverse and heterogeneous nature of these states. Furthermore, in the definition of diseases found in several textbooks or dictionaries, abnormal states are not directly associated with the corresponding quantitative values of clinical test data, making the processing of such data by computers difficult.

Results: We focused on abnormal states in the definition of diseases and proposed a unified form to describe an abnormal state as a "property," which can be decomposed into an "attribute" and a "value" in a qualitative representation. We have developed a three-layer ontological model of abnormal states from the generic to disease-specific level. By developing an is-a hierarchy and combining causal chains of diseases, 21,000 abnormal states from 6000 diseases have been captured as generic causal relations and commonalities have been found among diseases across 13 medical departments.

Conclusions: Our results showed that our representation framework promotes interoperability and flexibility of the quantitative raw data, qualitative information, and generic/conceptual knowledge of abnormal states. In addition, the results showed that our ontological model have found commonalities in abnormal states among diseases across 13 medical departments.

Keywords: Abnormal state; Attribute; Disease; Interoperability; Ontology; Property.

Figures

Figure 1

Three-level ontological model of abnormal states. This figure shows an example of the three levels of structural abnormality of our abnormality ontology. “Level 1” defines generic concepts, which are object-independent states, e.g., “small in area.” “Level 2” defines object-dependent abnormal states. States at the upper levels of Level 2 are dependent on generic structures, such as the “narrowing tube” and “narrowing valve,” which are common and are used in several domains. Note that concepts at the lower level of the tree are specialized into medicine-specific concepts such as vascular stenosis, arterial stenosis, and coronary artery stenosis. “Level 3” defines disease-dependent concepts. For example, “coronary artery stenosis in angina pectoris” is defined as a constituent of the disease “angina pectoris” at Layer 3.

Figure 2

Top-level categories related to abnormal states. The top level categories of abnormal states are classified into three subclasses: “structural abnormality,” “functional abnormality,” and “other abnormality such as parametric/nonparametric change and so on”.

Figure 3

Types of ischemic heart disease constituted of causal chains. This figure shows a couple of causal chain-constituted ischemic heart disease. Each node shows the abnormal states, and each link indicates the causal relation between the abnormal states. A core causal chain of each disease is colored differently: ischemic heart disease is orange, and the subclasses of the ischemic heart disease, myocardial infarction are yellow. Prinzmetal angina is also a subclass of the ischemic heart disease consists of a pink core causal chain, and by an upstream extension smoking is added in the derived causal chain. Organic angina pectoris is green and the accumulation of cholesterol is added to the derived causal chain, which is a possible cause of arterial sclerosis.

Figure 4

Examples of hypertrophy constituted of causal chains. This figure shows two different uses of cardiac hypertrophy. Each cardiac hypertrophy is red. One usage is a constituent of a causal chain of the hypertensive heart disease in the cardiovascular department (upper figure), and the other is a constituent of a glycogenesis type II disease (Pompe disease) in the metabolic disease department shown below (A core causal chain of each disease is yellow).

Figure 5

Computational representation of abnormal states from generic to specific level. This figure shows the specialization of abnormal states from “small in area” to “ischemic heart disease specific coronary artery stenosis” using HOZO.

Figure 6

A visual editing tool for causal chains to define disease concepts. This is a screenshot of our visual editing tool for editing the definition of disease concepts. It visualizes the causal chains defined in a selected disease as a directed graph like that in Figure 3.