Logic Programming and Artificial Neural Networks in Pharmacological Screening of Schinus Essential Oils

Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a 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 and the respective Degree-of-Confidence that one has on such an occurrence.

Screening a Case Base for Stroke Disease Detection

Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality fig-ure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge rep-resentation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases re-trieval one. On the other hand, and aiming at an improvement of the CBR theo-retical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were re-written, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of one`s confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

Prediction of Length of Hospital Stay in Preterm Infants - A Case-Based Reasoning View

The length of stay of preterm infants in a neonatology service has become an issue of a growing concern, namely considering, on the one hand, the mothers and infants health conditions and, on the other hand, the scarce healthcare facilities own resources. Thus, a pro-active strategy for problem solving has to be put in place, either to improve the quality-of-service provided or to reduce the inherent financial costs. Therefore, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a case-based problem solving methodology to computing, that caters for the handling of incomplete, unknown, or even contradictory in-formation. The proposed model has been quite accurate in predicting the length of stay (overall accuracy of 84.9%) and by reducing the computational time with values around 21.3%.

A Case-Based Approach to Nosocomial Infection Detection

The nosocomial infections are a growing concern because they affect a large number of people and they increase the admission time in healthcare facilities. Additionally, its diagnosis is very tricky, requiring multiple medical exams. So, this work is focused on the development of a clinical decision support system to prevent these events from happening. The proposed solution is unique once it caters for the explicit treatment of incomplete, unknown, or even contradictory information under a logic programming basis, that to our knowledge is something that happens for the first time.

Waiting Time Screening in Diagnostic Medical Imaging - A Case-Based View

Due to the high standards expected from diagnostic medical imaging, the analysis of information regarding waiting lists via different information systems is of utmost importance. Such analysis, on the one hand, may improve the diagnostic quality and, on the other hand, may lead to the reduction of waiting times, with the concomitant increase of the quality of services and the reduction of the inherent financial costs. Hence, the purpose of this study is to assess the waiting time in the delivery of diagnostic medical imaging services, like computed tomography and magnetic resonance imaging. Thereby, this work is focused on the development of a decision support system to assess waiting times in diagnostic medical imaging with recourse to operational data of selected attributes extracted from distinct information systems. The computational framework is built on top of a Logic Programming Case-base Reasoning approach to Knowledge Representation and Reasoning that caters for the handling of in-complete, unknown, or even self-contradictory information.

A Case Based approach to assess Waiting Time Prediction at an Intensive Care Unity

Waiting time at an intensive care unity stands for a key feature in the assessment of healthcare quality. Nevertheless, its estimation is a difficult task, not only due to the different factors with intricate relations among them, but also with respect to the available data, which may be incomplete, self-contradictory or even unknown. However, its prediction not only improves the patients’ satisfaction but also enhance the quality of the healthcare being provided. To fulfill this goal, this work aims at the development of a decision support system that allows one to predict how long a patient should remain at an emergency unit, having into consideration all the remarks that were just stated above. It is built on top of a Logic Programming approach to knowledge representation and reasoning, complemented with a Case Base approach to computing.

Length of Stay in Intensive Care Units - A Case Base Evaluation

As a matter of fact, an Intensive Care Unit (ICU) stands for a hospital facility where patients require close observation and monitoring. Indeed, predicting Length-of-Stay (LoS) at ICUs is essential not only to provide them with improved Quality-of-Care, but also to help the hospital management to cope with hospital resources. Therefore, in this work one`s aim is to present an Artificial Intelligence based Decision Support System to assist on the prediction of LoS at ICUs, which will be centered on a formal framework based on a Logic Programming acquaintance for knowledge representation and reasoning, complemented with a Case Based approach to computing, and able to handle unknown, incomplete, or even contradictory data, information or knowledge.

Contributo para a caracterização morfométrica da raça de abelhas melíferas (Apis mellifera iberiensis) da região norte de Portugal

A necessidade de aprofundar o conhecimento sobre a morfologia da abelha melífera portuguesa (Apis mellifera iberiensis) originou o presente estudo. A amostragem definiu-se com base no banco de amostras existentes no Laboratório de Patologia Apícola da ESA-IPB, relativas ao ano de 2015. Cada amostra foi constituída por cinco obreiras adultas conservadas pelo frio (-18 °C). Foram analisadas 124 amostras distribuídas por 16 concelhos (aproximadamente oito por concelho) pertencentes aos distritos de Bragança e Vila Real. As medições efetuadas em cada uma das obreiras foram: peso (PA), comprimento (CA) e largura (LA) do corpo; comprimento (CAA) e largura (LAA) da asa anterior; comprimento (CAP) e largura da asa posterior (LAP); comprimento do fêmur (CF), da tíbia (CT), do basitarso (CBT), do tarso (CTA) e da probóscide (CP) e a largura do basitarso (LBT). Para avaliação destas medidas utilizou-se uma balança analítica de precisão (0,01g) e um paquímetro eletrónico digital (0-100mm±0,02mm). As diferentes variáveis estudadas compararam-se por análise de variância (ANOVA), sendo o teste de Tukey-Kramer HSD utilizado para a comparação múltipla de médias. O peso médio das obreiras do concelho de Vila Pouca de Aguiar (0,123±0,018 g) foi mais elevado (p<0,05) do que o das obreiras dos concelhos de Torre de Moncorvo e Ribeira de Pena (0,106±0,023 g em ambos os casos). Também, as obreiras do concelho de Vila Pouca de Aguiar apresentaram um comprimento médio do corpo (13,065±0,890 mm) superior (p<0,05) às obreiras do concelho de Boticas (12,228±0,958 mm) e uma largura média (4,565 ± 0,392 mm) superior às obreiras de Vila Flor (4,089 ±0,288 mm). Porém, o CAP (6,333±0,303 mm) e o CT (3,179±0,183mm) das obreiras de Vila Pouca de Aguiar foi mais baixo (p<0,05) ao observado nos concelhos de Torre de Moncorvo (6,616±0,361 mm) e Vila Flor (3,358±0,146 mm). As variáveis LAA, LAP, CF, CBT e CP não apresentaram diferenças significativas (p>0,05) entre os concelhos estudados.