An Ontological Model for Analyzing Liver Cancer Medical Reports

Messaoudi, Rim; Labidi, Taher; Vacavant, Antoine; Gargouri, Faïez; Grand-Brochier, Manuel; Amouri, A.; Fourati, H.; Mtibaa, Achraf; Jaziri, Faouzi

doi:10.1007/978-3-030-11395-7_29

Cited by 3 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ontologies provide the basis to establish an explicit formal concept specification in a specific domain, allowing the development of relationships between these concepts and the reuse and integration of domain knowledge [10]. In the hospital domain, ontologies provide information associated with a wealth of knowledge about clinical decisions [3], medication [2], diagnoses [19], medical records [11], and so on.…”

Section: Introductionmentioning

confidence: 99%

Towards an Ontology to Support Decision-making in Hospital Bed Allocation (S)

Engelmann¹,

Couto²,

Gabriel³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Using advanced technologies is imperative to support quick decision-making in a hospital, where people work with complex and critical processes. An example of a complex and very important task is to make the best decision about in which room and bed a patient should be admitted to hospital, considering the patient needs, characteristics, and available resources. In this case, bad decisions can even compromise patient health. With this study, we aim to facilitate patient-related decisions related to bed allocation, based on an ontology. To do so, we developed an ontology that takes patients' information into account to help health professionals decide where to allocate them. Our main contribution is an ontology, with classes, relationships, individuals, and rules to be used in specific scenarios. Additionally, we exemplify some scenarios in which the rules could be applied.Index Terms-Ontology, health care, hospital bed allocation.

show abstract

Section: Introductionmentioning

confidence: 99%

Towards an Ontology to Support Decision-making in Hospital Bed Allocation (S)

Engelmann¹,

Couto²,

Gabriel³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

Ontologies for Liver Diseases Representation: A Systematic Literature Review

et al. 2019

Self Cite

View full text Add to dashboard Cite

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 welldefined 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.

show abstract

Ontology-Driven Approach for Liver MRI Classification and HCC Detection

Messaoudi

Jaziri

Mtibaa

et al. 2021

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

Reading and interpreting the medical image still remains the most challenging task in radiology. Through the important achievement of deep Convolutional Neural Networks (CNN) in the context of medical image classification, various clinical applications have been provided to detect lesions from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. In the diagnosis process for the liver cancer from Dynamic Contrast-Enhanced MRI (DCE-MRI), radiologists consider three phases during contrast injection: before injection, arterial phase, and portal phase for instance. Even if the contrast agent helps in enhancing the tumoral tissues, the diagnosis may be very difficult due to the possible low contrast and pathological tissues surrounding the tumors (cirrhosis). Alongside, in the medical field, ontologies have proven their effectiveness to solve several clinical problems such as offering shareable terminologies, vocabularies, and databases. In this article, we propose a multi-label CNN classification approach based on a parallel preprocessing algorithm. This algorithm is an extension of our previous work cited in the International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) 2020. The aim of our approach is to ameliorate the detection of HCC lesions and to extract more information about the detected tumor such as the stage, the localization, the size, and the type thanks to the use of ontologies. Moreover, the integration of such information has improved the detection process. In fact, experiments conducted by testing with real patient cases have shown that the proposed approach reached an accuracy of 93% using MRI patches of [Formula: see text] pixels, which is an improvement compared with our previous works.

show abstract

An Ontological Model for Analyzing Liver Cancer Medical Reports

Cited by 3 publications

References 16 publications

Towards an Ontology to Support Decision-making in Hospital Bed Allocation (S)

Towards an Ontology to Support Decision-making in Hospital Bed Allocation (S)

Ontologies for Liver Diseases Representation: A Systematic Literature Review

Ontology-Driven Approach for Liver MRI Classification and HCC Detection

Contact Info

Product

Resources

About