An evaluation of parchments' degradation a hybrid approach

Parchment stands for a multifaceted material made from animal skin, which has been used for centuries as a writing support or as bookbinding. Due to the historic value of objects made of parchment, understanding their degradation and their condition is of utmost importance to archives, libraries and museums, i.e., the assessment of parchment degradation is mandatory, although it is hard to do with traditional methodologies and 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, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate Parchment Degradation and the respective Degree-of-Confidence that one has on such a happening.(undefined

School dropout screening through artificial neural networks based systems

School dropout is one of the major concerns of our society. Indeed, it is a complex phenomenon, resulting in economic and social losses, either to the individual, family or the community to which the person belongs. Academic difficulty and failure, poor attendance, retention, disengagement from school together with family and socio-economic reasons can lead to such occurrence. In this work Logic Programming was used for knowledge representation and reasoning, letting the modeling of the universe of discourse in terms of defective data, information and knowledge. Artificial Neural Networks were used in order to evaluate potential situations of school dropout and the degree of confidence that one has on such a happening

Electronic health record in dermatology service

In this paper we describe the implementation of an Electronic Health Record in the Dermatology service of a Portuguese hospital. This system must follow the principle of simplicity, enabling recording quality and analytical processing. Standards and norms were also followed and it is shown that interoperability has a key role in the whole process. This project is a good example of cooperation between academic and healthcare institutions and shows the impact of new technology on healthcare organizations.Fundação para a Ciência e a Tecnologia (FCT

Evaluation of concrete deterioration through artificial neural networks based systems

The deterioration of concrete structures is one of the major concerns of our society. Indeed, concrete is a relatively sensitive material, which degrades throughout time. Factors like age, use, periodic maintenance, type of environmental exposure and aggression by biological, chemical, mechanical and physical agents are important to determine the level of degradation of the concrete structures. Logic Programming was used for knowledge representation and reasoning, letting the modeling of the universe of discourse in terms of defective data, information and knowledge. Artificial Neural Networks were used in order to evaluate the deterioration of concrete structures and the degree of confidence that one has on such a happening

Decision making and quality-of-information

Springer - Series Advances in Intelligent and Soft Computing, vol. 73In Group Decision Making based on argumentation, decisions are made considering the diverse points of view of the different partakers in order to decide which course of action a group should follow. However, knowledge and belief are normally incomplete, contradictory, or error sensitive, being desirable to use formal tools to deal with the problems that arise from the use of uncertain and even not precise information. On the other hand, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes, a problem that in this work will be addressed in terms of an extension to the logic programming language and based on an evaluation of the Quality-of-Information (QoI) that stems out from those extended logic programs or theories. We present a computational model to address the problem of decision making, in terms of a multitude of scenarios, also defined as logic programs or theories, where the more appropriate ones stand for the higher QoIs values

Improving nursing practice through interoperability and intelligence

Hospital inpatient care compromises one of the most demanding services in health institutions when providing a careful and continuous healthcare assistance. Such demands require constant update of the patients' electronic health record allied with support systems responsible for monitoring their clinical information. In this context, this paper presents a new web platform for daily monitoring of patients, designed to be used by health professionals, especially nurses. The application is based on React, an open-source JavaScript library for building user interfaces. The developed tool incorporates two main features: the real-time visualization of the data, and the storage of the patient's historic during an inpatient care episode. The storage capability allows keeping the data updated among hospital shifts. Moreover, this work also highlights the required adaptability of this platform for each health units inside a hospital center according with its needs.This work has been supported by Compete POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope UID/CEC/00319/2013

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.This work is funded by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PEst-OE/EEI/UI0752/2014 and PEst-OE/QUI/UI0619/2012

Knowledge acquisition process for intelligent decision support in critical health care

An efficient triage system is a good way to avoid some future problems and, how much quicker it is, more the patient can benefit. However, a limitation still exists, the triage system are general and not specific to each case. Manchester Triage System is a reliable known system and is focused in the emergency department of a hospital. When applied to specific patients’ conditions, such the pregnancy has several limitations. To overcome those limitations, an alternative triage system, integrated into an intelligent decision support system, was developed. The system classifies patients according to the severity of their clinical condition, establishing clinical priorities and not diagnosis. According to the woman urgency of attendance or problem type, it suggests one of the three possible categories of the triage. This paper presents the overall knowledge acquisition cycle associated to the workflow of patient arrival and the inherent decision making process. Results showed that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place, reducing the waiting triage time and the number of women in general urgency.Fundação para a Ciência e a Tecnologia (FCT

Handling default data under a case-based reasoning approach

The knowledge acquired through past experiences is of the most importance when humans or machines try to find solutions for new problems based on past ones, which makes the core of any Case-based Reasoning approach to problem solving. On the other hand, existent CBR systems are neither complete nor adaptable to specific domains. Indeed, the effort to adapt either the reasoning process or the knowledge representation mechanism to a new problem is too high, i.e., it is extremely difficult to adapt the input to the computational framework in order to get a solution to a particular problem. This is the drawback that is addressed in this work.This work is funded by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PEst-OE/EEI/UI0752/2014 and PEst-OE/QUI/UI0619/2012