Medical image processing using extended logic programming and artificial neural networks

Alves, Victor; Neves, José; Maia, Moreira; Nelas, Luís

doi:10.1109/viprom.2002.1026625

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…The accuracy of CBR system is 77% compared to 33% of an ANN for predicting the deterioration of highway bridges (Morcous 2002). Alves et al (2003) have compared CBR and ANN in the medical diagnosis and concluded that CBR outperforms ANN. Raman et al (2012) have compared the performance of ANN and CBR regarding the diagnosis of breast cancer, and they concluded that CBR is more accurate and applicable in diagnosis systems.…”

Section: Case-based Reasoning For Diabetes Managementmentioning

confidence: 99%

Medical Case Based Reasoning Frameworks

El–Sappagh

Elmogy

2016

International Journal of Decision Support System Technology

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Case-Based Reasoning (CBR) is one of the most suitable AI techniques for building clinical decision support systems. Medical domain complexity introduces many challenges for building these systems. Building the systems' knowledge base from the Electronic Health Record (EHR), the encoding of case-base knowledge with standard medical ontology, and the handling of vague data are examples of these challenges. Although several advantages of using CBR in medicine have been identified, there are no real systems acceptable to physicians. This systematic review examines the current state of CBR and its limitations in the medical domain, especially for diabetes mellitus. The critical evaluation of the status of diabetes CBR systems presents unique opportunities for improving these systems. The literature review covers most of the English language studies extracted from relevant databases by using search terms relating CBR, ontology, Fuzzy, and standard terminology concepts. The authors identify 38 articles published between 1999 and 15 January 2015, which represent original researches in CBR domain. The study includes 15 (39.5%) non-medical studies and 23 (60.5%) medical studies with ~22% for diabetes CBR. A list of 18 integrated evaluation metrics has been proposed and used in the analysis. The results show that the non-medical CBR systems achieved higher advances (50%) than medical systems (42.9%). In addition, the diabetes management CBR systems achieve the lowest advances (21.4%) compared to other systems. These shortages explain the question “why CBR paradigm are not fully utilized in the commercial medical systems?” As a result, there is a distinct need for more comprehensive enhancements in clinical CBR especially diabetes systems.

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Section: Case-based Reasoning For Diabetes Managementmentioning

confidence: 99%

Medical Case Based Reasoning Frameworks

El–Sappagh

Elmogy

2016

International Journal of Decision Support System Technology

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“…The priority in terms of implementation was to cover the medical imaging field going from data acquisition and storage to physicians, teachers and student's interfaces. The AIDA system, an Agency for Integration, Archive and Diffusion of Medical Information [5,19,20], that is at work at several main health care facilities in the north region of Portugal was our choice for heterogeneous data integration. These data is an immensely valuable resource for teaching and diagnostic aid.…”

Section: The Multi-agent System Implementationmentioning

confidence: 99%

Web-based Medical Teaching using a Multi-Agent System

Alves¹,

Neves²,

Nelas³

et al.

Applications and Innovations in Intelligent Systems XIII

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Web-based teaching via Intelligent Tutoring Systems (ITSs) is considered as one of the most successful enterprises in artificial intelligence. Indeed, there is a long list of ITSs that have been tested on humans and have proven to facilitate learning, among which we may find the well-tested and known tutors of algebra, geometry, and computer languages. These ITSs use a variety of computational paradigms, as production systems, Bayesian networks, schematemplates, theorem proving, and explanatory reasoning. The next generation of ITSs are expected to go one step further by adopting not only more intelligent interfaces but will focus on integration. This article will describe some particularities of a tutoring system that we are developing to simulate conversational dialogue in the area of Medicine, that enables the integration of highly heterogeneous sources of information into a coherent knowledge base, either from the tutor's point of view or the development of the discipline in itself, i.e. the system's content is created automatically by the physicians as their daily work goes on. This will encourage students to articulate lengthier answers that exhibit deep reasoning, rather than to deliver straight tips of shallow knowledge. The goal is to take advantage of the normal functioning of the health care units to build on the fly a knowledge base of cases and data for teaching and research purposes.

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Medical image processing using extended logic programming and artificial neural networks

Cited by 2 publications

References 2 publications

Medical Case Based Reasoning Frameworks

Medical Case Based Reasoning Frameworks

Web-based Medical Teaching using a Multi-Agent System

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