Henrique Vicente

Bio: Henrique Vicente is an academic researcher from University of Évora. The author has contributed to research in topics: Knowledge representation and reasoning & Logic programming. The author has an hindex of 12, co-authored 118 publications receiving 504 citations. Previous affiliations of Henrique Vicente include University of Minho.

Artificial neural networks in diabetes control

Abstract: Diabetes Mellitus is now a prevalent disease in both developed and underdeveloped countries, being a major cause of morbidity and mortality. Overweight/obesity and hypertension are potentially modifiable risk factors for diabetes mellitus, and persist during the course of the disease. Despite the evidence from large controlled trials establishing the benefit of intensive diabetes management in reducing microvasculars and macrovasculars complications, high proportions of patients remain poorly controlled. Poor and inadequate glycemic control among patients with Type 2 diabetes constitutes a major public health problem and a risk factor for the development of diabetes complications. In clinical practice, optimal glycemic control is difficult to obtain on a long-term basis, once the reasons for feebly glycemic control are complex. Therefore, this work will focus on the development of a diagnosis support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centred on Artificial Neural Networks, to evaluate the Diabetes states and the Degree-of-Confidence that one has on such a happening.

Prediction of the quality of public water supply using artificial neural networks

Abstract: The Health Surveillance Program was established by the Regional Health Authority of Alentejo to control the quality of public water supply. This authority divides the water quality parameters into three distinct groups, namely P 1 (pH and conductivity), P 2 (nitrate and manganese) and P 3 (sodium and potassium), for which the sampling frequency is dissimilar. Thus, the development of formal models is essential to predict the chemical parameters included in group P 2 and included in group P 3 , for which the sampling frequency is lower, based on the chemical parameters included in group P 1 . In the present work, artificial neural networks (ANNs) were used to predict the concentration of nitrate, manganese, sodium and potassium from pH and conductivity. Different network structures have been elaborated and evaluated using the mean absolute deviation and the mean squared error. The ANN selected to predict the concentration of nitrate, sodium and potassium from pH and conductivity has a 2-18-14-3 topology while the network selected to predict the concentration of nitrate and manganese has a 2-19-10-2 topology. A good match between the observed and predicted values was observed with the R 2 values varying in the range 0.9960–0.9989 for the training set and 0.9993–0.9952 for the test set.

A data mining approach to study the impact of the methodology followed in chemistry lab classes on the weight attributed by the students to the lab work on learning and motivation

Abstract: This study reports the use of data mining tools in order to examine the influence of the methodology used in chemistry lab classes, on the weight attributed by the students to the lab work on learning and own motivation. The answer frequency analysis was unable to discriminate the opinions expressed by the respondents according to the type of the teaching methodology used in the lab classes. Conversely, the data mining approach using k-means clustering models, allowed a deeper analysis of the results, i.e., enabled one to identify the methodology to teach chemistry that, in students' opinion, is important for learning chemistry and increasing their motivation. The sample comprised 3447 students of Portuguese Secondary Schools (1736 in the 10th grade; 1711 in the 11th grade). The k-means Clustering Method was used, with k values ranging between 2 and 4. The main strengths of this study are the methodological approach for data analysis and the fact that the sample was formed by students with different school careers that enables the use of the individual as the unit of analysis.

A Soft Computing Approach to Kidney Diseases Evaluation

Abstract: Kidney renal failure means that one's kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient's history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9---94.2 %, respectively.

The Environmental Protection Agency

Abstract: The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.