Navegando por Palavras-chave "Decision support systems"
Agora exibindo 1 - 3 de 3
Resultados por página
Opções de Ordenação
- ItemAcesso aberto (Open Access)Aplicação de técnicas de inteligência artificial ao desenvolvimento de um sistema de apoio à decisão para doença celíaca(Universidade Federal de São Paulo (UNIFESP), 2011-02-22) Tenório, Josceli Maria [UNIFESP]; Marin, Heimar de Fatima [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)Introduction: the diagnosing of celiac disease involves some complexity due to its multiple symptoms, signs, risk groups, presentation and the wide possibility of differential diagnosis. In order to confirm the diagnosis of celiac disease, it is required to perform the biopsy or the small intestine, the gold standard. Objective: to develop a decision making support system, in web environment, including an automated classifier to recognize cases of celiac disease, to be previously selected among experimental models drawing upon techniques of artificial intelligence. Methods: a web system was implemented to support an electronic protocol designed to help with celiac disease investigation and collect clinical data. A preliminary assessment of this system usability was performed through the analysis of a questionnaire based on the System Usability Scale (SUS) completed by 10 direct users of the web system implemented. A retrospective database with 178 cases was build for training the automated classifier. A total of 270 automated classifiers available in the software Weka 3.6.1 were tested using 5 artificial intelligence techniques – decision tree, K-nearest-neighbor, Bayesian classifier, support vector machine and artificial neural networks. The parameters area under the receiver operating characteristic curve (AUC), sensitivity, specificity and correctness rate were used, in the order above, as criteria to select the classification algorithm to be implemented in the web system. The algorithm with the largest AUC was included in the web system whose software was named SADCEL. A database with 38 clinical cases was built to assess the diagnostic power this software. The diagnostic hypothesis obtained from SADCEL was compared with those reached by the specialists participating in the study using Kappa Statistic. Results: the preliminary usability score attained by the web system was 83.5 ± 10.0 (excellent). The Bayesian classifying algorithm AODE F1 had the best performance scoring 80.0% for correctness, 0.78 for sensitivity, 0.84 for specificity and 0.84 for AUC. Compared with the study gold standard, SADCEL achieved an accuracy of 84.2% with a level of agreement with the diagnostic gold standard rated as k = 0.68 (p-value < 0.0001), indicative of good level of agreement. The level of agreement between the specialist diagnostic hypothesis and the diagnostic gold standard was rated as k = 0.64 (p-value < 0.0001). The agreement between the specialist and SADCEL diagnostic hypotheses was rated as k = 0.46 (p-value) indicative of moderate level of agreement. Conclusion: the level of accuracy attained by the classifying algorithm incorporated in this study´s web system evidences the potential usefulness of SADCEL in helping with diagnosing celiac disease in clinical set. This study is, thus, expected to be a contribution towards the establishing of a computational means of diagnosing the celiac disease.
- ItemSomente MetadadadosModel for Differential Nursing Diagnosis of Alterations in Urinary Elimination Based on Fuzzy Logic(Lippincott Williams & Wilkins, 2009-09-01) Lopes, Maria Helena Baena de Moraes; Ortega, Neli Regina Siqueira; Massad, Eduardo [UNIFESP]|Marin, Heimar de Fatima [UNIFESP]; Universidade Estadual de Campinas (UNICAMP); Universidade de São Paulo (USP); Universidade Federal de São Paulo (UNIFESP)Nursing diagnoses associated with alterations of urinary elimination require different interventions, Nurses, who are not specialists, require support to diagnose and manage patients with disturbances of urine elimination. The aim of this study was to present a model based on fuzzy logic for differential diagnosis of alterations in urinary elimination, considering nursing diagnosis approved by the North American Nursing Diagnosis Association, 2001-2002. Fuzzy relations and the maximum-minimum composition approach were used to develop the system. The model performance was evaluated with 195 cases from the database of a previous study, resulting in 79.0% of total concordance and 19.5% of partial concordance, when compared with the panel of experts. Total discordance was observed in only three cases (1.5%). The agreement between model and experts was excellent (kappa = 0.98, P < .0001) or substantial (kappa = 0.69, P < .0001) when considering the overestimative accordance (accordance was considered when at least one diagnosis was equal) and the underestimative discordance (discordance was considered when at least one diagnosis was different), respectively. The model herein presented showed good performance and a simple theoretical structure, therefore demanding few computational resources.
- ItemAcesso aberto (Open Access)A semi-automated method for bone age assessment using cervical vertebral maturation(E H Angle Education Research Foundation, Inc, 2012-07-01) Baptista, Roberto Silva [UNIFESP]; Quaglio, Camila L. [UNIFESP]; Mourad, Leila M. E. H. [UNIFESP]; Hummel, Anderson Diniz [UNIFESP]; Caetano, Cesar Augusto C.; Ortolani, Cristina Lúcia Feijó; Pisa, Ivan Torres [UNIFESP]; Universidade Federal de São Paulo (UNIFESP); Fac Informat & Adm Paulista FIAP; Univ Paulista UNIPObjective: To propose a semi-automated method for pattern classification to predict individuals' stage of growth based on morphologic characteristics that are described in the modified cervical vertebral maturation (CVM) method of Baccetti et al.Materials and Methods: A total of 188 lateral cephalograms were collected, digitized, evaluated manually, and grouped into cervical stages by two expert examiners. Landmarks were located on each image and measured. Three pattern classifiers based on the Naive Bayes algorithm were built and assessed using a software program. the classifier with the greatest accuracy according to the weighted kappa test was considered best.Results: the classifier showed a weighted kappa coefficient of 0.861 +/- 0.020. If an adjacent estimated pre-stage or poststage value was taken to be acceptable, the classifier would show a weighted kappa coefficient of 0.992 +/- 0.019.Conclusion: Results from this study show that the proposed semi-automated pattern classification method can help orthodontists identify the stage of CVM. However, additional studies are needed before this semi-automated classification method for CVM assessment can be implemented in clinical practice. (Angle Orthod. 2012;82:658-662.)