Application of a model based on fuzzy logic for evaluating nursing diagnostic accuracy of students

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Purpose: To describe a model for assessing nursing diagnostic accuracy and its application to undergraduate students, comparing students' performance according to the course year. Methods: This model, based on the theory of fuzzy sets, guides a student through three steps: (a) the student must parameterize the model by establishing relationship values between defining characteristic/risk factors and nursing diagnoses; (b) presentation of a clinical case; (c) the student must define the presence of each defining characteristic/risk factors for the clinical case. Subsequently, the model computes the most plausible diagnoses by taking into account the values indicated by the student. This gives the student a performance score in comparison with parameters and diagnoses that were previously provided by nursing experts. These nursing experts collaborated with the construction of the model indicating the strength of the relationship between the concepts, meaning, they parameterized the model to compare the student's choice with the expert's choice (gold standard), thus generating performance scores for the student. The model was tested using three clinical cases presented to 38 students in their third and fourth years of the undergraduate nursing course. Results: Third year students showed superior performance in identifying the presence of defining characteristic/risk factors, while fourth year students showed superior performance in the diagnoses by the model. Conclusions: The Model for Evaluation of Diagnostic Accuracy Based on Fuzzy Logic applied in this study is feasible and can be used to evaluate students' performance. In this regard, it will open a broad variety of applications for learning and nursing research. Limitations: Despite the ease in filling the printed questionnaires out, the number of steps and fields to fill in may explain the considerable number of questionnaires with incorrect or missing data. This was solved in the digital version of the questionnaire. In addition, in more complex cases, it is possible that an expert opinion can lead to a wrong decision due to the subjectivity of the diagnostic process. (C) 2013 Elsevier Ireland Ltd. All rights reserved.829875881Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Fuzzy cognitive map in differential diagnosis of alterations in urinary elimination: A nursing approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Purpose: To develop a decision support system to discriminate the diagnoses of alterations in urinary elimination, according to the nursing terminology of NANDA International (NANDA-I). Methods: A fuzzy cognitive map (FCM) was structured considering six possible diagnoses: stress urinary incontinence, reflex urinary incontinence, urge urinary incontinence, functional urinary incontinence, total urinary incontinence and urinary retention; and 39 signals associated with them. The model was implemented in Microsoft Visual C++(R) Edition 2005 and applied in 195 real cases. Its performance was evaluated through the agreement test, comparing its results with the diagnoses determined by three experts (nurses). The sensitivity and specificity of the model were calculated considering the expert's opinion as a gold standard. In order to compute the Kappa's values we considered two situations, since more than one diagnosis was possible: the overestimation of the accordance in which the case was considered as concordant when at least one diagnoses was equal; and the underestimation of the accordance, in which the case was considered as discordant when at least one diagnosis was different. Results: The overestimation of the accordance showed an excellent agreement (kappa = 0.92, p < 0.0001); and the underestimation provided a moderate agreement (kappa = 0.42, p < 0.0001). In general the FCM model showed high sensitivity and specificity, of 0.95 and 0.92, respectively, but provided a low specificity value in determining the diagnosis of urge urinary incontinence (0.43) and a low sensitivity value to total urinary incontinence (0.42). Conclusions: The decision support system developed presented a good performance compared to other types of expert systems for differential diagnosis of alterations in urinary elimination. Since there are few similar studies in the literature, we are convinced of the importance of investing in this kind of modeling, both from the theoretical and from the health applied points of view. Limitations: In spite of the good results, the FCM should be improved to identify the diagnoses of urge urinary incontinence and total urinary incontinence. (C) 2012 Elsevier Ireland Ltd. All rights reserved.823201208Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)NIH [D43TW007015]BRIGHTConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)CNPq [CNPq 476854/2004-0]NIH [D43TW007015]CNPq [301735/2009

Nursing and fuzzy logic: an integrative review Enfermería y lógica fuzzy: una revisión de integradora Enfermagem e lógica fuzzy: uma revisão integrativa

This study conducted an integrative review investigating how fuzzy logic has been used in research with the participation of nurses. The article search was carried out in the CINAHL, EMBASE, SCOPUS, PubMed and Medline databases, with no limitation on time of publication. Articles written in Portuguese, English and Spanish with themes related to nursing and fuzzy logic with the authorship or participation of nurses were included. The final sample included 21 articles from eight countries. For the purpose of analysis, the articles were distributed into categories: theory, method and model. In nursing, fuzzy logic has significantly contributed to the understanding of subjects related to: imprecision or the need of an expert; as a research method; and in the development of models or decision support systems and hard technologies. The use of fuzzy logic in nursing has shown great potential and represents a vast field for research.<br>Este estudio tuvo como objetivo realizar una revisión integradora investigando como la lógica fuzzy ha sido utilizada en investigaciones con participación de enfermeros. La búsqueda de los artículos fue realizada en las bases de datos CINAHL, Embase, SCOPUS, Medline y PubMed, sin especificar un intervalo de años determinado. Fueron incluidos artículos en los idiomas: portugués, inglés y castellano; con una temática relacionada a la enfermería y a la lógica fuzzy; y con autoría o participación de enfermeros. La muestra final fue de 21 artículos, de ocho países. Para el análisis, los artículos fueron distribuidos en las categorías: teoría, método y modelo. En la enfermería, la lógica fuzzy ha contribuido significativamente para la comprensión de temas relativos a la imprecisión o a la necesidad del especialista, como método de investigación y en el desarrollo de modelos o sistemas de apoyo a la decisión y de tecnologías duras. El uso de la lógica fuzzy en la enfermería ha demostrado gran potencial y representa un vasto campo para investigaciones.<br>Este estudo teve como objetivo realizar revisão integrativa, investigando como a lógica fuzzy tem sido utilizada em pesquisas com participação de enfermeiros. A busca dos artigos foi realizada nas bases de dados CINAHL, Embase, Scopus, MEDLINE e PubMed, sem intervalo de anos especificado. Foram incluídos artigos na língua portuguesa, inglesa e espanhola; com temática relacionada à enfermagem e à lógica fuzzy, e autoria ou participação de enfermeiros. A amostra final foi de 21 artigos, de oito países. Para análise, os artigos foram distribuídos nas categorias: teoria, método e modelo. Na enfermagem, a lógica fuzzy tem contribuído significativamente para a compreensão de temas relativos à imprecisão ou à necessidade do especialista, como método de pesquisa e no desenvolvimento de modelos ou sistemas de apoio à decisão e de tecnologias duras. O uso da lógica fuzzy, na enfermagem, tem demonstrado grande potencial e representa vasto campo para pesquisas