Design of guidelines evidence-based nursing care in patients with

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Aims: Evidence-based clinical guidelines effectively guide medical teams and nurses to increase the quality of clinical work. Designing evidence-based guidelines in critical care units, especially in cardiac care unit is much more needed. Therefore, this study was conducted to design evidence-based nursing care guidelines for patients with angina pectoris. Methods: This descriptive-comparative study was conducted in two cardiac care units in Baqiyatallah Hospital in Tehran in 2011. First, the quality of 30 available care guidelines was investigated via a checklist designed by the researcher in three levels: good, average and poor. Then nursing care guidelines were designed based on Stetler model with an evidence-based approach and their quality was re-investigated. Finally, the collected data was analyzed by the help of descriptive statistics and using SPSS 17 software. Results: Quality of 26.7 % of the available guidelines was found out to be poor and 73.3 % was proven to be average. After designing the guidelines, this number increased to 100 %. Finally, an eight evidence-based nursing care guideline was designed for patients with angina pectoris. Conclusion: Since the available guidelines are of low quality, designing evidence-based care guidelines can improve nursing care.

Medical reasoning describes a form of qualitative inquiry that examines the cognitive (thought) processes involved in making medical decision. In this field the goal for di-agnostic reasoning is assessing causes of observed conditions in order to make informed choices about treatment. In order to design a diagnostic reasoning method we merge ideas from a hypothetic-deductive method and the Domino model. In this setting, we introduce the so called Hypothetic-Deductive-Domino (HD-D) algorithm. In addition, a multi-agent approach is presented, which takes advantage of the HD-D algorithm for illuminating different standpoints in a diagnostic reasoning and assessment process, and for reaching a well-founded conclusion. This multi-agent approach is based on the so called Observer and Validating agents. The Observer agents are supported by a deduc-tive inference process and the Validating agents are supported by an abductive inference process. The knowledge bases of these agents are captured by a class of possibilistic logic programs. Hence, these agents are able to deal with qualitative information. The approach is illustrated by a real scenario from diagnosing dementia diseases.

Abstract — Knowledge and belief are generally incomplete, contradictory, or even error sensitive, being desirable to use formal tools to deal with the problems that arise from the use of partial, contradictory, ambiguous, imperfect, nebulous, or missing information. Historically, uncertain reasoning has been associated with probability theory. However, qualitative models and qualitative reasoning have been around in database theory and Artificial Intelligence research for some time, in particular due to the growing need to offer user support in decision making processes. In this paper, and under the umbrella of the Multi-valued Extended Logic Programming formalism to knowledge representation and reasoning we present an evaluative perspective of such an approach, in order to select the best theories (or logic programs) that model the universe of discourse to solve a problem, in terms of a process of quantification of the quality-of-information that stems out from those theories. Additionally, we present a novel approach to integrate incomplete information into the relational data model, making possible the use of the relational algebra operators and the potential inherent to the Structured Query Languages to present solutions to a particular problem and to measure their

Copyright © 2015 L. Cardoso et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information. 1.