Integration of geometric consistency contributory factors in three-leg junctions collision prediction models of Portuguese two-lane national highways

This paper aims at developing a collision prediction model for three-leg junctions located in national roads (NR) in Northern Portugal. The focus is to identify factors that contribute for collision type crashes in those locations, mainly factors related to road geometric consistency, since literature is scarce on those, and to research the impact of three modeling methods: generalized estimating equations, random-effects negative binomial models and random-parameters negative binomial models, on the factors of those models. The database used included data published between 2008 and 2010 of 177 three-leg junctions. It was split in three groups of contributing factors which were tested sequentially for each of the adopted models: at first only traffic, then, traffic and the geometric characteristics of the junctions within their area of influence; and, lastly, factors which show the difference between the geometric characteristics of the segments boarding the junctionsâ area of influence and the segment included in that area were added. The choice of the best modeling technique was supported by the result of a cross validation made to ascertain the best model for the three sets of researched contributing factors. The models fitted with random-parameters negative binomial models had the best performance in the process. In the best models obtained for every modeling technique, the characteristics of the road environment, including proxy measures for the geometric consistency, along with traffic volume, contribute significantly to the number of collisions. Both the variables concerning junctions and the various national highway segments in their area of influence, as well as variations from those characteristics concerning roadway segments which border the already mentioned area of influence have proven their relevance and, therefore, there is a rightful need to incorporate the effect of geometric consistency in the three-leg junctions safety studies.

Type 2 diabetes-related variants influence the risk of developing multiple myeloma: results from the IMMEnSE consortium

Type 2 diabetes (T2D) has been suggested to be a risk factor for multiple myeloma (MM), but the relationship between the two traits is still not well understood. The aims of this study were to evaluate whether 58 genome-wide-association-studies (GWAS)-identified common variants for T2D influence the risk of developing MM and to determine whether predictive models built with these variants might help to predict the disease risk. We conducted a case–control study including 1420 MM patients and 1858 controls ascertained through the International Multiple Myeloma (IMMEnSE) consortium. Subjects carrying the KCNQ1rs2237892T allele or the CDKN2A-2Brs2383208G/G, IGF1rs35767T/T and MADDrs7944584T/T genotypes had a significantly increased risk of MM (odds ratio (OR)=1.32–2.13) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a significantly decreased risk of developing the disease (OR=0.76–0.85). Interestingly, a prediction model including those T2D-related variants associated with the risk of MM showed a significantly improved discriminatory ability to predict the disease when compared to a model without genetic information (area under the curve (AUC)=0.645 vs AUC=0.629; P=4.05×10-06). A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348 and NOTCH2rs10923931 variants (Pinteraction=0.001 and 0.0004, respectively). Men carrying the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a significantly decreased risk of MM whereas an opposite but not significant effect was observed in women (ORM=0.71 and ORM=0.66 vs ORW=1.22 and ORW=1.15, respectively). These results suggest that TD2-related variants may influence the risk of developing MM and their genotyping might help to improve MM risk prediction models.

Avaliação do impacto na circulação e risco de acidente em estradas em obras

Dissertação de mestrado integrado em Engenharia Civil

Plataforma de apoio à decisão nos cuidados de ginecologia e obstetrícia

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)

Childhood history of abuse and child abuse potential: the role of parent’s gender and timing of childhood abuse

It has been suggested that being physically abused leads to someone becoming a perpetrator of abuse which could be associated to parents' gender, timing of the physical abuse and specific socio-demographic variables. This study aims to investigate the role the parents' gender, timing of childhood abuse and socio-demographic variables on the relationship between parents' history of childhood physical abuse and current risk for children. The sample consisted of 920 parents (414 fathers, 506 mothers) from the Portuguese National Representative Study of Psychosocial Context of Child Abuse and Neglect who completed the Childhood History Questionnaire and the Child Abuse Potential Inventory. The results showed that fathers had lower current potential risk of becoming physical abuse perpetrators with their children than mothers although they did not differed in their physical victimization history. Moreover, the risk was higher in parents (both genders) with continuous history of victimization than in parents without victimization. Prediction models showed that for fathers and mothers separately similar socio-demographic variables (family income, number of children at home, employment status and marital status) predicted the potential risk of becoming physical abuses perpetrators. Nevertheless, the timing of victimization was different for fathers (before 13 years old) and mothers (after 13 years old). Then our study targets specific variables (timing of physical abuse, parents' gender and specific socio-demographic variables), which may enable professionals to select groups of parents at greater need of participating in abuse prevention programs.

Avaliação do desempenho de indicadores com base na metodologia ROC (Receiver Operating Characteristic)

Dissertação de mestrado em Bioinformática

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.

Os fatores de risco da rendibilidade das ações: análise e evidência empírica em países da Zona Euro

Tese de Doutoramento em Ciências Empresariais.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Risk acceptance in the furniture sector: Analysis of acceptance level and relevant influence factors

Risk acceptance has been broadly discussed in relation to hazardous risk activities and/or technologies. A better understanding of risk acceptance in occupational settings is also important; however, studies on this topic are scarce. It seems important to understand the level of risk that stakeholders consider sufficiently low, how stakeholders form their opinion about risk, and why they adopt a certain attitude toward risk. Accordingly, the aim of this study is to examine risk acceptance in regard to occupational accidents in furniture industries. The safety climate analysis was conducted through the application of the Safety Climate in Wood Industries questionnaire. Judgments about risk acceptance, trust, risk perception, benefit perception, emotions, and moral values were measured. Several models were tested to explain occupational risk acceptance. The results showed that the level of risk acceptance decreased as the risk level increased. High-risk and death scenarios were assessed as unacceptable. Risk perception, emotions, and trust had an important influence on risk acceptance. Safety climate was correlated with risk acceptance and other variables that influence risk acceptance. These results are important for the risk assessment process in terms of defining risk acceptance criteria and strategies to reduce risks.

Predicting type of delivery by identification of obstetric risk factors through data mining

In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries.

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Development of a methodology to incorporate risk and uncertainty in electricity power planning

Dissertação de mestrado em Engenharia Industrial

Study to determine the cardiovascular risk ofthe population of Guimarães/Vizela, includingthe prevalence of arterial stiffness and early vascular aging syndrome

Tese de Doutoramento em Medicina.