Ontologies for Liver Diseases Representation: A Systematic Literature Review.

Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive, and with well-defined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.

Ontology-Based Approach for Liver Cancer Diagnosis and Treatment.

Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC. Hence, the main purpose of this paper is to offer a new ontology-based approach modeling HCC tumors by focusing on two major aspects: the first focuses on tumor detection in medical imaging, and the second focuses on its staging by applying different classification systems. We implemented our approach in Java using Jena API. Also, we developed a prototype OntHCC by the use of semantic aspects and reasoning rules to validate our work. To show the efficiency of our work, we tested the proposed approach on real datasets. The obtained results have showed a reliable system with high accuracies of recall (76%), precision (85%), and F-measure (80%).