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

Yamagata, Yuki; Kozaki, Kouji; Imai, T.; Mizoguchi, Riichiro

doi:10.1186/2041-1480-5-23

Cited by 16 publications

(15 citation statements)

References 7 publications

(10 reference statements)

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“…To verify the effectiveness of EAPB, four real-world ontologies related to infection diseases are adopted for comparison. Infection constitutes the invasion of an organism’s body tissues by disease-causing agents, the multiplication of these agents, the reaction of host tissues to these organisms, and the toxins produced by these organisms [ 17 , 18 ]. Disease Ontology (DO) …”

Section: Methodsmentioning

confidence: 99%

EAPB: entropy-aware path-based metric for ontology quality

et al. 2018

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BackgroundEntropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.ResultsWe propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field.ConclusionsWe leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate).

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Section: Methodsmentioning

confidence: 99%

EAPB: entropy-aware path-based metric for ontology quality

et al. 2018

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“…Ontologies constitute one of the methods applied for efficient knowledge sharing and reuse [13]. An ontology allows combining conceptual knowledge with quantitative and qualitative data, supporting interoperability and flexibility of its underlying model [14].…”

Section: Introductionmentioning

confidence: 99%

“OPTImAL”: an ontology for patient adherence modeling in physical activity domain

Livitckaia

Koutkias

Kouidi

et al. 2019

BMC Med Inform Decis Mak

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Background Maintaining physical fitness is a crucial component of the therapeutic process for patients with cardiovascular disease (CVD). Despite the known importance of being physically active, patient adherence to exercise, both in daily life and during cardiac rehabilitation (CR), is low. Patient adherence is frequently composed of numerous determinants associated with different patient aspects (e.g., psychological, clinical, etc.). Understanding the influence of such determinants is a central component of developing personalized interventions to improve or maintain patient adherence. Medical research produced evidence regarding factors affecting patients’ adherence to physical activity regimen. However, the heterogeneity of the available data is a significant challenge for knowledge reusability. Ontologies constitute one of the methods applied for efficient knowledge sharing and reuse. In this paper, we are proposing an ontology called OPTImAL, focusing on CVD patient adherence to physical activity and exercise training. Methods OPTImAL was developed following the Ontology Development 101 methodology and refined based on the NeOn framework. First, we defined the ontology specification (i.e., purpose, scope, target users, etc.). Then, we elicited domain knowledge based on the published studies. Further, the model was conceptualized, formalized and implemented, while the developed ontology was validated for its consistency. An independent cardiologist and three CR trainers evaluated the ontology for its appropriateness and usefulness. Results We developed a formal model that includes 142 classes, ten object properties, and 371 individuals, that describes the relations of different factors of CVD patient profile to adherence and adherence quality, as well as the associated types and dimensions of physical activity and exercise. 2637 logical axioms were constructed to comprise the overall concepts that the ontology defines. The ontology was successfully validated for its consistency and preliminary evaluated for its appropriateness and usefulness in medical practice. Conclusions OPTImAL describes relations of 320 factors originated from 60 multidimensional aspects (e.g., social, clinical, psychological, etc.) affecting CVD patient adherence to physical activity and exercise. The formal model is evidence-based and can serve as a knowledge tool in the practice of cardiac rehabilitation experts, supporting the process of activity regimen recommendation for better patient adherence. Electronic supplementary material The online version of this article (10.1186/s12911-019-0809-9) contains supplementary material, which is available to authorized users.

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“…There were some previous studies concerning diseases in perspectives of chain [1, 10–14]. They defined a causal chain that focused on pathological viewpoints in which diseases are caused by the series of risk factors such as abnormal states, symptoms, or lifestyles.…”

Section: Introductionmentioning

confidence: 99%

Inference on chains of disease progression based on disease networks

2019

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Motivation Disease progression originates from the concept that an individual disease may go through different changes as it evolves, and such changes can cause new diseases. It is important to find a progression between diseases since knowing the prior-posterior relationship beforehand can prevent further complications or evolutions to other diseases. Furthermore, the series of progressions can be represented in the form of a chain, which enables us to readily infer successive influences from one disease to another after many passages through other diseases. Methods In this paper, we propose a systematic approach for finding a disease progression chain from a source disease to a target one via exploring a disease network. The network is constructed based on various sets of biomedical data. To find the most influential progression chains, the k -shortest path search algorithm is employed. The most representative algorithms such as A*, Dijkstra, and Yen’s are incorporated into the proposed method. Results A disease network consisting of 3,302 diseases was constructed based on four sources of biomedical data: disease-protein relations, biological pathways, clinical history, and biomedical literature information. The last three sets of data contain prior-posterior information, and they endow directionality on the edges of the network. The results were interesting and informative: for example, when colitis and respiratory insufficiency were set as a source disease and a target one, respectively, five progression chains were found within several seconds (when k = 5). Each chain was provided with a progression score, which indicates the strength of plausibility relative to others. Similarly, the proposed method can be expanded to any pair of source-target diseases in the network. This can be utilized as a preliminary tool for inferring complications or progressions between diseases.

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

Cited by 16 publications

References 7 publications

EAPB: entropy-aware path-based metric for ontology quality

EAPB: entropy-aware path-based metric for ontology quality

“OPTImAL”: an ontology for patient adherence modeling in physical activity domain

Inference on chains of disease progression based on disease networks

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