A knowledge-based approach to assign breast cancer treatments in oncology units

Miranda-Mena, Teddy Gonzalo; Benítez, Luisa; Ochoa, José Luís; Martı́nez-Béjar, Rodrigo; Fernández-Breis, Jesualdo Tomás; Salinas, Juan

doi:10.1016/j.eswa.2005.09.076

Cited by 19 publications

(4 citation statements)

References 6 publications

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“…It would seem that a well‐designed knowledge‐based system would have much to offer within this domain. The project team developed a knowledge‐based system that employs the multiple classification ripple‐down rules (MCRDR) methodology (33–39) to drive the knowledge representation and acquisition. MCRDR provides a powerful and flexible representation of knowledge, and allows the user to build the knowledge base incrementally while the system is in use.…”

Section: Methodsmentioning

confidence: 99%

Development of an intelligent decision support system for medication review

et al. 2007

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The knowledge-based system has already demonstrated that the technique employed is well suited to a domain of this nature and has furthermore demonstrated that it is capable of improving the quality of service that the medication reviewer can provide. The system will be further enhanced and tested prior to use in the field. It should help pharmacists in the provision of medication reviews, improving their clinical and time management capabilities, and enhancing their ability to contribute to the quality use of medications.

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Section: Methodsmentioning

confidence: 99%

Development of an intelligent decision support system for medication review

et al. 2007

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“…In addition, an expert system can integrate opinions from different experts, and the knowledge in database can be accumulated and preserved for a long time. In the past decades, numerous successful cases of developing expert systems have demonstrated the benefits of applying this approach [15,17,28]. To develop an English article recommendation expert system, several preparatory works need to be done, including collecting relevant literatures, collecting English articles and interviewing with domain experts.…”

Section: Methodsmentioning

confidence: 99%

Development of a Reading Material Recommendation System Based on a Multi-expert Knowledge Acquisition Approach

Hsu

Chang

Hwang

2009

2009 Ninth IEEE International Conference on Advanced Learning Technologies

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In English courses, it is very important to assign proper articles to individual students for training their reading ability. This study proposes an innovative approach for developing reading material recommendation systems by eliciting domain knowledge from multiple experts. An experiment has been conducted to evaluate the performance of the approach; moreover, a comparison on the existing approaches is given to show the advantages of applying the innovative approach.

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“…The Semantic Web may well provide a solution to how to acquire and manage large volumes of knowledge to develop truly intelligent problem solvers and address the brittleness of traditional Knowledge base systems. Clinical knowledge -Breast Cancer in particular -is characterized by its: Large volume (Miranda-Mena et al, 2006); Incompleteness of data; and need for correct reasoning operations. If a situation necessitating that tacit (oncologist's mind) knowledge must be stored and organized in clinical protocols for use through knowledge elicitation and knowledge representation, then the Semantic Web Ontology languages already provide efficient ways of doing these.…”

Section: *Corresponding Authormentioning

confidence: 99%

A Knowledge Representation Model Based on Select and Test Algorithm for Diagnosing Breast Cancer

2020

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There exist several terminal diseases whose fatality rate escalates with time of which breast cancer is a frontline disease among such. Computer aided systems have also been well researched through the use intelligent algorithms capable of detecting, diagnosing, and proffering treatment for breast cancer. While good research breakthrough has been attained in terms of algorithmic solution towards diagnosis of breast cancer, however, not much has been done to sufficiently model knowledge frameworks for diagnostic algorithms that are knowledge-based. While Select and Test (ST) algorithm have proven relevant for implementing diagnostic systems, through support for reasoning, however the knowledge representation pattern that enables inference of missing or ambiguous data still limits the effectiveness of ST algorithm. This paper therefore proposes a knowledge representation model to systematically model knowledge to aid the performance of ST algorithm. Our proposal is specifically targeted at developing systematic knowledge representation for breast cancer. The approach uses the ontology web language (OWL) to implement the design of the knowledge model proposed. This study aims at carefully crafting a knowledge model whose implementation seamlessly work with ST algorithm. Furthermore, this study adapted the proposed model into an implementation of ST algorithm an obtained an improved performance compared to the simple knowledge model proposed by the author of ST algorithm. Our knowledge mode resulted in an accuracy gain of 23.5% and obtained and AUC of (0.49, 1.0). This proposed model has therefore shown that combining an inference-oriented knowledge model with an inference-oriented reasoning algorithm improves the performance of computer aided diagnostic (CADx) systems. In future, we intend to enhance the proposed model to support rules. Keywords— Semantic web, ontology, OWL, breast cancer, Select and Test (ST) algorithm, knowledge representation

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A knowledge-based approach to assign breast cancer treatments in oncology units

Cited by 19 publications

References 6 publications

Development of an intelligent decision support system for medication review

Development of an intelligent decision support system for medication review

Development of a Reading Material Recommendation System Based on a Multi-expert Knowledge Acquisition Approach

A Knowledge Representation Model Based on Select and Test Algorithm for Diagnosing Breast Cancer

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