000			05764nam a22002657a 4500
003			NULRC
005			20250520133428.0
008			250520b ph ||||| |||| 00| 0 eng d
022			_a1938-7857
040			_cNULRC
245			_aJournal of Information Technology Research
260			_aUSA: _bIGI Global, _cc2011.
300			_a59 pages; _c26 cm.
490			_vJournal of Information Technology Research, Volume 4, Issue 1, January-March 2011
504			_aIncludes bibliographical references.
505			_aThe Consistency of the Medical Expert System CADIAG-2: A Probabilistic Approach -- Development of a Knowledge Based System for an Intensive Care Environment Using Ontologies -- Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks -- DISMON: Using Social Web and Semantic Technologies to Monitor Diseases in Limited Environments.
520			_a[Article Title: The Consistency of the Medical Expert System CADIAG-2: A Probabilistic Approach/ Pavel Picado Klinov, Bijan Parsia and David Muiño, p. 1-20] Abstract: CADIAG-2 is a well known rule-based medical expert system aimed at providing support in medical diagnose in the field of internal medicine. Its knowledge base consists of a large collection of IF-THEN rules that represent uncertain relationships between distinct medical entities. Given this uncertainty and the size of the system, it has been challenging to validate its consistency. Recent attempts to partially formalize CADIAG-2's knowledge base into decidable Gödel logics have shown that, on formalization, the system is inconsistent. In this paper, the authors use an alternative, more expressive formalization of CADIAG-2's knowledge base as a set of probabilistic conditional statements and apply their probabilistic logic solver Pronto to confirm its inconsistency and compute its conflicting sets of rules under a slightly relaxed interpretation. Once this is achieved, the authors define a measure to evaluate inconsistency and discuss suitable repair strategies for CADIAG-2 and similar systems. _uhttps://doi.org/10.4018/jitr.2011010101
520			_a[Article Title: Development of a Knowledge Based System for an Intensive Care Environment Using Ontologies/ Javier Pereira Loureiro, Ana Torres Morgade, Marcos Martínez-Romero, José M. Vázquez-Naya, Miguel Pereira Loureiro and Ángel González Albo, p. 21-33] Abstract: In intensive care units ICUs, clinicians must monitor patients' vital signs and make decisions regarding the drugs they administer. The patients' lives depend on the quality of these decisions but experts can make mistakes. Recent technological strategies and tools can decrease these errors. In this paper, the authors describe the development of a knowledge based system KBS to provide support to clinicians with respect to the drugs they administer to patients with cardiopathies in ICUs to stabilize them. To develop the system, knowledge from medical experts at the Meixoeiro Hospital in Vigo Spain has been extracted and formally represented as an ontology. As a result, a validated KBS has been obtained, which can be helpful to experts in ICUs and whose underlying knowledge can be easily shared and reused. _uhttps://doi.org/10.4018/jitr.2011010102
520			_a[Article Title: Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks/ Rahul Kala, Anupam Shukla and Ritu Tiwari, p. 34-47] Abstract: The complexity of problems has led to a shift toward the use of modular neural networks in place of traditional neural networks. The number of inputs to neural networks must be kept within manageable limits to escape from the curse of dimensionality. Attribute division is a novel concept to reduce the problem dimensionality without losing information. In this paper, the authors use Genetic Algorithms to determine the optimal distribution of the parameters to the various modules of the modular neural network. The attribute set is divided into the various modules. Each module computes the output using its own list of attributes. The individual results are then integrated by an integrator. This framework is used for the diagnosis of breast cancer. Experimental results show that optimal distribution strategy exceeds the well-known methods for the diagnosis of the disease. _uhttps://doi.org/10.4018/jitr.2011010103
520			_a[Article Title: DISMON: Using Social Web and Semantic Technologies to Monitor Diseases in Limited Environments/ Ricardo Colomo-Palacios, Ángel García-Crespo, Juan Miguel Gómez-Berbís, Ángel M. Lagares-Lemos and Miguel Lagares-Lemos, p. 48-59] Abstract: Information technology and, more precisely, the internet represent challenges and opportunities for medicine. Technology-driven medicine has changed how practitioners perform their roles in and medical information systems have recently gained momentum as a proof-of-concept of the efficiency of new support-oriented technologies. Emerging applications combine sharing information with a social dimension. This paper presents DISMON Disease Monitor, a system based on Semantic Technologies and Social Web SW to improve patient care for medical diagnosis in limited environments, namely, organizations. DISMON combines Web 2.0 capacities and SW to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee diseases, giving useful information to the company and its employees to increase efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for workers. _uhttps://doi.org/10.4018/jitr.2011010104
690			_aPROBABILISTIC SATISFIABILITY
690			_aONTOLOGIES
690			_aBREAST CANCER DIAGNOSIS
942			_2lcc _cSER _n0
999			_c22523 _d22523