An Ontology-Based System for Cancer Diseases Knowledge Management

Alfonse, Marco; Aref, Mostafa; Salem, Abdel-Badeeh M.

doi:10.5815/ijieeb.2014.06.07

Cited by 21 publications

(14 citation statements)

References 5 publications

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“…Up to now, many ontology-based clinical decision support systems for different types of diseases have been developed and achieved convincing results in the clinical practice [13][14][15][16] . This valuable work provides us with many inspirations.…”

Section: Architecture Of the Ontopv Systemmentioning

confidence: 99%

Towards an Ontology-Based Decision Support System for Syndrome-Differentiation and Treatment of Psoriasis Vulgaris in Traditional Chinese Medicine

Long

Wang

Cui

et al. 2020

Preprint

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Abstract Background: Psoriasis is a chronic, non-communicable, painful, disfiguring and disabling disease, which is not curable and strongly declines the patients’ quality of life (QoL). However, diagnose and treatment in traditional Chinese medicine (TCM) based on syndrome differentiation has been used in practice for a long time and has also achieved some effect. Though, up to now, only few studies are available reporting on the use of semantic technologies and pertain to knowledge systems that use TCM-syndrome differentiation for information retrieval and automated reasoning. Nowadays, the diagnosis in TCM relies mainly on the personal expertise and clinical experience of the doctors. For various reasons, misdiagnoses or missed diagnoses cannot be completely excluded, leading to unexpected results.Methods: Firstly, we developed a domain ontology for syndrome differentiation of psoriasis vulgaris. For this purpose, we used the ontology editor Protégé and applied a top-down approach which adopts the framework of general formal ontology (GFO) and its middle-level core ontology GFO-TCM. Furthermore, we implement a prototype which is based on this ontology. Additionally, we also used a case-database for CBR (Case Based Reasoning) combined with fuzzy pattern recognition.Results: A prototype for diagnosis and treatment of psoriasis vulgaris, named ONTOPV-system, is proposed which is based on the principle of syndrome differentiation and treatment in TCM; this system realizes an expert-assisted decision support method which relies on a domain ontology, uses fuzzy logic reasoning, and case retrieval and is intended to support clinical diagnostic decisions for TCM practitioners.Conclusions: We designed and implemented a prototype for psoriasis diagnosis in terms of syndrome differentiation. The system can not only realize the basic functionalities of data collection, querying, browsing and navigation, but also support rule-based knowledge reasoning and customize approximate reasoning based on CBR through fuzzy logic, which can provide users with clinical decision support for TCM syndrome differentiation in diagnosis of psoriasis. In addition, it comprises a domain knowledge base of psoriasis, which is developed based on the GFO framework with good extensibility.

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Section: Architecture Of the Ontopv Systemmentioning

confidence: 99%

Towards an Ontology-Based Decision Support System for Syndrome-Differentiation and Treatment of Psoriasis Vulgaris in Traditional Chinese Medicine

Long

Wang

Cui

et al. 2020

Preprint

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“…Ontology specifies the concepts, relationships, and other distinctions that are related to modelling a domain to be shared between users [4,11]. In consequence, fuzzy ontology allows each object to be related to other objects in the ontology with a matching degree based on the fuzzy set theory invented by Zadeh [1].…”

Section: B Crisp Versus Fuzzy Ontologiesmentioning

confidence: 99%

Fuzzy Ontology based Approach for Flexible Association Rules Mining

Moawad¹,

Gadallah²,

Kholief³

2017

ijacsa

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Abstract-Data mining is used for extracting related data. The association rules approach is one of the used methods for analyzing, discovering and extracting knowledge and mining the relationships among raw data. Commonly, it is important to understand and discover such knowledge directly from huge records of items stored in a relational database. This paper proposes an approach for generating human-like fuzzy association rules based on fuzzy ontology. It focuses on enhancing the process of extracting association rules from a huge database respecting a predefined domain fuzzy ontology. Commonly, association rules mining based on crisp ontology is found to be more flexible than classical ones as it considers the relationships between concepts or items. Yet, crisp ontology suffers from the problem of information losing resulted from the rigid boundaries of crisp relationships, which are approximated to be 0 or 1, between concepts. In contrast, the smooth boundaries of fuzzy sets make it able to represent partial relationships that range from 0 to 1 between concepts in an ontology in a more flexible human-like manner. Consequently, generating fuzzy association rules based on fuzzy ontology makes it more human-like and reliable compared with other previous ones. An illustrative case study, on two different data sets, shows the added value of the proposed approach compared with some other recent approaches.

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“…MRI, as a diagnostic technology, also allows the diagnosis of the type of the tumor and also the identification of the denomination of the cancer pathology, in other words, the classification of the tumor. Human brain tumors likewise other cancer pathologies are characterized by unregulated cell growth [5]. The unregulated cell growth in cancer is also characterized by the pace at which it takes place.…”

Section: Introduction Magnetic Resonance Imaging (Mri) Computerizedmentioning

confidence: 99%

“…Therefore, the severity of the disease and the need for a surgery, and/or radiation, chemotherapy, hormone therapy, biological therapy, and targeted therapy needs to be assessed immediately [5]. MRI is one of the most rapid and used technology that can make the aforementioned assessments.…”

Section: Introduction Magnetic Resonance Imaging (Mri) Computerizedmentioning

confidence: 99%

Calculation of the Classic-Curvature and the Intensity-Curvature Term Before Interpolation of a Bivariate Polynomial

Agyapong

2015

IJIEEB

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Abstract-This paper presents the calculation of the classic-curvature and the intensity-curvature term before interpolation of a bivariate polynomial model function. The classic-curvature is termed as y c (x, y) and the intensity-curvature term before interpolation is termed as E 0 . The classic-curvature is defined as the sum of the four second order partial derivatives of the bivariate polynomial. The intensity-curvature term before interpolation is defined as the integral of the product between the pixel intensity value termed as f(0, 0) and the classic-curvature calculated at the origin of the coordinate system of the pixel. This paper presents an application of the calculation of classic-curvature and the intensitycurvature term before interpolation using twodimensional Magnetic Resonance Imaging (MRI) data and reports for the first time in the literature on the behavior of the intensity-curvature term before interpolation.Index Terms-Magnetic Resonance Imaging (MRI), classic-curvature, intensity-curvature term before interpolation, intensity-curvature measurement approaches, first order partial derivative, second order partial derivative, bivariate polynomial, model function, image.

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An Ontology-Based System for Cancer Diseases Knowledge Management

Cited by 21 publications

References 5 publications

Towards an Ontology-Based Decision Support System for Syndrome-Differentiation and Treatment of Psoriasis Vulgaris in Traditional Chinese Medicine

Towards an Ontology-Based Decision Support System for Syndrome-Differentiation and Treatment of Psoriasis Vulgaris in Traditional Chinese Medicine

Fuzzy Ontology based Approach for Flexible Association Rules Mining

Calculation of the Classic-Curvature and the Intensity-Curvature Term Before Interpolation of a Bivariate Polynomial

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