Neli Regina Siqueira Ortega

Fuzzy gradual rules in epidemiology

This paper presents an application of the fuzzy gradual rules in an epidemic study of canine rabies in Sao Paulo city, Brazil. A linguistic epidemiological model was elaborated through fuzzy rules built by the Extension Principle. We used both the inference method of Mamdani and of Dubois et al. The results were compared with real data from Sao Paulo and with another MISO Model, which is entirely based on expert knowledge presented in a previous work. Questions about application of fuzzy techniques in epidemiology, different inference methods and the Dubois et al. methodology are discussed.

Fuzzy epidemics

The purpose of this paper is to provide a review of the current state of fuzzy logic theory in epidemiology, which is a recent area of research. We present four applications of fuzzy logic theory in epidemic problems, using linguistic fuzzy models, possibility measure, probability of fuzzy events and fuzzy decision making techniques. The results demonstrate that the applications of fuzzy sets in epidemiology is a very promising area of research. The final discussion sets the future stage of fuzzy sets application in epidemiology.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Fuzzy Dynamical Systems in Epidemic Modeling

Proposes an application of fuzzy set theory to model epidemiological problems. Fuzzy logic has been revealed as a powerful predictive tool in the epidemiology of infectious diseases and some ideas are presented on how this could be done. This work presents an attempt to model the dynamics of rabies among a population of partially vaccinated dogs. This study demonstrates how a dynamical system can be modelled by fuzzy linguistic rules compared to the classical differential equations approach. The results are very encouraging and the approach through a more complex dynamical system is discussed in the final section.

Abnormalities of plantar pressure distribution in early, intermediate, and late stages of diabetic neuropathy

Inconsistent findings with regard to plantar pressure while walking in the diabetic population may be due to the heterogeneity of the studied groups resulting from the classification/grouping criteria adopted. The clinical diagnosis and classification of diabetes have inherent uncertainties that compromise the definition of its onset and the differentiation of its severity stages. A fuzzy system could improve the precision of the diagnosis and classification of diabetic neuropathy because it takes those uncertainties into account and combines different assessment methods. Here, we investigated how plantar pressure abnormalities evolve throughout different severity stages of diabetic polyneuropathy (absent, n=38; mild, n=20; moderate, n=47; severe, n=24). Pressure distribution was analysed over five areas while patients walked barefoot. Patients with mild neuropathy displayed an increase in pressure-time integral at the forefoot and a lower peak pressure at the heel. The peak and pressure-time integral under the forefoot and heel were aggravated in later stages of the disease (moderate and severe) compared with early stages of the disease (absent and mild). In the severe group, lower pressures at the lateral forefoot and hallux were observed, which could be related to symptoms that develop with the aggravation of neuropathy: atrophy of the intrinsic foot muscles, reduction of distal muscle activity, and joint stiffness. Although there were clear alterations over the forefoot and in a number of plantar areas with higher pressures within each severity stage, they did not follow the aggravation evolution of neuropathy classified by the fuzzy model. Based on these results, therapeutic interventions should begin in the early stages of this disease to prevent further consequences of the disease.

Effect of diabetic neuropathy severity classified by a fuzzy model in muscle dynamics during gait.

BackgroundElectromyography (EMG) alterations during gait, supposedly caused by diabetic sensorimotor polyneuropathy, are subtle and still inconsistent, due to difficulties in defining homogeneous experimental groups with a clear definition of disease stages. Since evaluating these patients involve many uncertainties, the use of a fuzzy model could enable a better discrimination among different stages of diabetic polyneuropathy and lead to a clarification of when changes in muscle activation start occurring. The aim of this study was to investigate EMG patterns during gait in diabetic individuals with different stages of DSP severity, classified by a fuzzy system.Methods147 subjects were divided into a control group (n = 30) and four diabetic groups: absent (n = 43), mild (n = 30), moderate (n = 16), and severe (n = 28) neuropathy, classified by a fuzzy model. The EMG activity of the vastus lateralis, tibialis anterior, and gastrocnemius medialis were measured during gait. Temporal and relative magnitude variables were compared among groups using ANOVA tests.ResultsMuscle activity changes are present even before an established neural involvement, with delay in vastus lateralis peak and lower tibialis anterior relative magnitude. These alterations suggest an impaired ankle shock absorption mechanism, with compensation at the knee. This condition seems to be more pronounced in higher degrees of neuropathy, as there is an increased vastus lateralis activity in the mild and severe neuropathy groups. Tibialis anterior onset at terminal stance was anticipated in all diabetic groups; at higher degrees of neuropathy, the gastrocnemius medialis exhibited activity reduction and peak delay.ConclusionEMG alterations in the vastus lateralis and tibialis anterior occur even in the absence of diabetic neuropathy and in mild neuropathic subjects, seemingly causing changes in the shock absorption mechanisms at the heel strike. These changes increase with the onset of neural impairments, and the gastrocnemius medialis starts presenting altered activity in the later stages of the disease (moderate and severe neuropathy). The degree of severity of diabetic neuropathy must be taken into account when analyzing diabetic patients’ biomechanical patterns of locomotion; we recommend the use of a fuzzy model for classification of disease stages.

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment.

Fuzzy modeling of electrical impedance tomography images of the lungs

OBJECTIVES Aiming to improve the anatomical resolution of electrical impedance tomography images, we developed a fuzzy model based on electrical impedance tomography’s high temporal resolution and on the functional pulmonary signals of perfusion and ventilation. INTRODUCTION Electrical impedance tomography images carry information about both ventilation and perfusion. However, these images are difficult to interpret because of insufficient anatomical resolution, such that it becomes almost impossible to distinguish the heart from the lungs. METHODS Electrical impedance tomography data from an experimental animal model were collected during normal ventilation and apnea while an injection of hypertonic saline was administered. The fuzzy model was elaborated in three parts: a modeling of the heart, the pulmonary ventilation map and the pulmonary perfusion map. Image segmentation was performed using a threshold method, and a ventilation/perfusion map was generated. RESULTS Electrical impedance tomography images treated by the fuzzy model were compared with the hypertonic saline injection method and computed tomography scan images, presenting good results. The average accuracy index was 0.80 when comparing the fuzzy modeled lung maps and the computed tomography scan lung mask. The average ROC curve area comparing a saline injection image and a fuzzy modeled pulmonary perfusion image was 0.77. DISCUSSION The innovative aspects of our work are the use of temporal information for the delineation of the heart structure and the use of two pulmonary functions for lung structure delineation. However, robustness of the method should be tested for the imaging of abnormal lung conditions. CONCLUSIONS These results showed the adequacy of the fuzzy approach in treating the anatomical resolution uncertainties in electrical impedance tomography images.

Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

Perception of disability in a public health perspective: a model based on fuzzy logic

Measures of functional levels, commonly used to assess the safety and quality of life of individuals and populations, have not yet been derived from a fuzzy framework. The aim of this study is to estimate the degree of disability associated with varying functional levels, through a model based on fuzzy sets theory. A fuzzy linguistic model was developed to measure varying levels of functional disability, in accordance with the definitions of an individuals social and physical activities and mobility. One year of an adults life whose mobility, social and physical activities were somewhat limited, was judged to be equivalent to 0.575 years free of functional disability. Results obtained from the fuzzy model approach those obtained with the quality of well-being scale (QWB), used as a conceptual framework. Such findings are encouraging, since the QWB is considered a consistent and valid approach for disability assessment and quality-of-life evaluation.