Comparison of reproductive management programs for submission of Holstein heifers for first insemination with conventional or sexed semen based on expression of estrus, pregnancy outcomes, and cost per pregnancy.

Our objective was to evaluate reproductive management programs for submission of Holstein heifers for first insemination with conventional or sexed semen. In experiment 1, nulliparous Holstein heifers (n = 462) were submitted to a 5-d progesterone-releasing intravaginal device (PRID)-Synch protocol [d 0, GnRH + PRID; d 5, PGF2α - PRID; d 6, PGF2α; d 8, GnRH + TAI] and were randomly assigned for PRID removal on d 5 or 6 of the protocol followed by timed artificial insemination (TAI) with conventional semen. Delaying PRID removal decreased early expression of estrus before scheduled TAI (0.9 vs. 12.2%), and pregnancies per AI (P/AI) did not differ between treatments. In experiment 2, nulliparous Holstein heifers (n = 736) from 3 commercial farms were randomized within farm to 1 of 3 treatments for first AI with sexed semen: (1) CIDR5 [d -6, GnRH + controlled internal drug release (CIDR); d -1, PGF2α - CIDR; d 0, PGF2α; d 2, GnRH + TAI]; (2) CIDR6 (d -6, GnRH + CIDR; d -1, PGF2α; d 0, PGF2α - CIDR; d 2, GnRH + TAI); and (3) EDAI (PGF2α on d 0 followed by once-daily estrous detection and AI). Delaying CIDR removal decreased early expression of estrus before scheduled TAI (0.004 vs. 27.8%); however, CIDR5 heifers tended to have more P/AI at 35 (53 vs. 45 vs. 46%) and 64 (52 vs. 45 vs. 45%) days after AI than CIDR6 and EDAI heifers, respectively. Overall, CIDR5 and CIDR6 heifers had fewer days to first AI and pregnancy than EDAI heifers which resulted in less feed costs than EDAI heifers due to fewer days on feed until pregnancy. Despite greater hormonal treatment costs for CIDR5 heifers, costs per pregnancy were $16.66 less for CIDR5 than for EDAI heifers. In conclusion, delaying PRID removal by 24 h within a 5-d PRID-Synch protocol in experiment 1 suppressed early expression of estrus before TAI, and P/AI for heifers inseminated with conventional semen did not differ between treatments. By contrast, although delaying CIDR removal by 24 h within a 5-CIDR-Synch protocol in experiment 2 suppressed early expression of estrus before TAI, delaying CIDR removal by 24 h tended to decrease P/AI for heifers inseminated with sexed semen. Further, submission of heifers to a 5-d CIDR-Synch protocol for first AI tended to increase P/AI and decrease the cost per pregnancy compared with EDAI heifers.

Assessment of metal and metalloid contamination in soils trough compositional data: the old Mortórios uranium mine area, central Portugal.

Soils from the old Mortórios uranium mine area were studied to look for contamination, as they are close to two villages, up to 3 km away, and used for agriculture. They are mainly contaminated in U and As and constitute an ecological threat. This study attempts to outline the degree to which soils have been affected by the old mining activities through the computation of significant hot clusters, Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the analyzed elements represent the soil's entirety. However, in geochemical studies these elements are just a fraction of the total soil composition. Thus, considering compositional data is pivotal. The spatial characterization, considering raw and compositional data together, allowed a broad discussion about not only the concentrations' spatial distribution, but also a better understanding on the possibility of trends of "relative enrichment" and, furthermore an insight in U and As fate. The highest proportions (compositional data) on U (up to 33%), As (up to 35%) and Th (up to 13%) are reached in the south-southeast segment. However, the highest concentrations (raw data) occur in north and northwest of the studied area, pointing out to a "relative enrichment" toward the south-southeast zone. The Mondego Sul area is mainly contaminated in U and As, but also in Co, Cu, Pb and Sb. The Mortórios area is less contaminated than the Mondego Sul area.

Software code complexity assessment using EEG features.

This paper provides a study using Electroencephalography (EEG) to investigate the brain activity during code comprehension tasks. Three different code complexity levels according to five complexity metrics were considered. The use of EEG for this purpose is relevant, since the existing studies were mostly focused on neuroimaging techniques. Using Leave-One-Subject-Out cross-validation procedure for 30 subjects, it was found that the features related with the Gamma activity were the most common in all the folds. Regarding the brain regions, right parietal was the most frequent region contributing with more features. A Linear Discriminant Analysis Classifier for task classification, obtained a F-Measure of 92.71% for Code complexity easy, 52.25% for Code complexity intermediate and 53.13% for Code complexity advanced, revealing an evidence of mental effort saturation with the code complexity degree. This suggests that current code complexity metrics do not capture cognitive load and might not be the best approach to assess bug risk.

A Clinical Interpretable Approach Applied to Cardiovascular Risk Assessment.

The effectiveness of predictive models in supporting the Clinical Decision is closely related with their clinical interpretability, i.e.the model should provide clear information on how to reach a specific classification/decision. In fact, the development of interpretable and accurate predictive models assumes a key importance as these tools can be very useful in Clinical Decision Support Systems (CDSS). The development of those models may comprise two main perspectives; existent clinical knowledge (clinical expert knowledge, clinical guidelines, current models, etc.) as well as data driven approaches able to extract (new) knowledge from recent clinical datasets. This work focuses in knowledge extraction from recent datasets (data driven) based on computational intelligence techniques. The main hypothesis that supports this work is that individuals with similar characteristics present a similar risk prof ile. Thus, this work addresses the development of stratification models able to learn distinct groups (or classes) of subjects assessing the similarity between characterizing variables. In particular, in the current study a data-driven supervised cluster approach is proposed aiming the derivation of meaningful rules directly from the dataset. The validation was performed based on the largest Portuguese coronary artery disease patient's dataset, provided by the Portuguese Society of Cardiology and comprising 13902 acute coronary syndrome patients. The goal was to assess the risk of death 30 days after admission. The models' performance was assessed through the sensitivity, specificity and geometric mean values. The obtained results show the potential of this approach, as they represent an acceptable performance (GM= 72%) while the clinical interpretability of the model is assured through the derived rules. Despite the achieved results, there are several research directions to be followed in order to enhance this work.

Pattern discovery and similarity assessment for robust heart sound segmentation.

Heart Sound Segmentation plays a fundamental role in pathology detection in Phonocardiogram (PCG) signals. This matter of study has been widely studied in the past decades, however the majority of algorithms' results correspond only to small databases, composed by only quality signals or signals specific to one acquisition system. In this work we proposed a robust segmentation algorithm integrated with clinical information, based on a pattern recognition approach for segmentation of the fundamental heart sounds, which is validated in several databases from different countries and with different acquisition instrumentations. The database comprises a total of 3153 recordings from 764 patients with a variety of pathological conditions. The general results were 95% and 96% of sensitivity and positive predictivity, respectively. Based on the results the algorithm is able to perform with accuracy maintaining generalization capabilities.

An interpretable data-driven approach for rules construction: Application to cardiovascular risk assessment.

The development of models able to produce an understandable decision by the clinicians is of great importance to support their decision. Therefore, the research of methodologies able to extract useful knowledge from existing datasets, as well as to integrate this knowledge into the current clinical evidence, is a key aspect in the enhancement of the clinical decision. This work focuses on the development of interpretable models to assess the patient's condition based on supervised clustering theories, enabling the discovery of a set of features that best represents that condition. At the same time, the technique is supported on a structure that enables the formulation of simple and interpretable rules. Despite its general applicability, the proposed methodology is applied to coronary artery disease (CAD), particularly, in the risk of death assessment (30 days after the admission) of patients that have been admitted to the emergency unit. The validation is performed using a real dataset with Acute Coronary Syndromes, provided by the Portuguese Society of Cardiology. While the methodology produces simple and interpretable rules, the performance achieves an improvement of 7% in relation to geometric mean, when compared with GRACE model (commonly used in Portugal).

Pattern discovery and similarity assessment for robust Heart Sound Segmentation.

Heart Sound Segmentation plays a fundamental role in pathology detection in Phonocardiogram (PCG) signals. This matter of study has been widely studied in the past decades, however the majority of algorithms' results correspond only to small databases, composed by only quality signals or signals specific to one acquisition system. In this work we proposed a robust segmentation algorithm integrated with clinical information, based on a pattern recognition approach for segmentation of the fundamental heart sounds, which is validated in several databases from different countries and with different acquisition instrumentations. The database comprises a total of 3153 recordings from 764 patients with a variety of pathological conditions. The general results were 95% and 96% of sensitivity and positive predictivity, respectively. Based on the results the algorithm is able to perform with accuracy maintaining generalization capabilities.

Assessment of accessory canals of the canalis sinuosus: a study of 1000 cone beam computed tomography examinations.

The aim of this study was to verify the presence, spatial location, and calibre of the accessory canals (AC) of the canalis sinuosus by cone beam computed tomography, and their relationship to the anterior maxilla. This retrospective analysis included the scans of 1000 subjects. Parameters registered were sex, age, number of AC, presence or absence of AC with a diameter <1.0mm, AC diameter (only for AC with a diameter >1.0mm), and AC location in relation to the adjacent teeth. Males showed a statistically higher frequency of AC than females. The difference in age distribution was not statistically significant. Twenty percent of all AC presented a diameter of a least 1.0mm. The end of the AC trajectory was most frequently located palatal to the anterior maxillary teeth. All relationships analyzed here were very weak (age vs. number of AC, age vs. AC diameter, number of AC vs. sex). Overall, the results of this study showed that AC of the canalis sinuosus are a common anatomical structure in the anterior maxilla, regardless of age and sex.

Potential risk assessment in stream sediments, soils and waters after remediation in an abandoned W>Sn mine (NE Portugal).

The mining complex of Murçós belongs to the Terras de Cavaleiros Geopark, located in Trás-os-Montes region, northeast Portugal. A stockwork of NW-SE-trending W>Sn quartz veins intruded Silurian metamorphic rocks and a Variscan biotite granite. The mineralized veins contain mainly quartz, cassiterite, wolframite, scheelite, arsenopyrite, pyrite, sphalerite, chalcopyrite, galena, rare pyrrhotite, stannite, native bismuth and also later bismuthinite, matildite, joseite, roosveltite, anglesite, scorodite, zavaritskite and covellite. The exploitation produced 335t of a concentrate with 70% of W and 150t of another concentrate with 70% of Sn between 1948 and 1976. The exploitation took place mainly in four open pit mines as well as underground. Three lakes were left in the area. Remediation processes of confination and control of tailings and rejected materials and phytoremediation with macrophytes from three lakes were carried out between 2005 and 2007. Stream sediments, soils and water samples were collected in 2008 and 2009, after the remediation process. Most stream sediments showed deficiency or minimum enrichment for metals. The sequential enrichment factor in stream sediments W>Bi>As>U>Cd>Sn=Ag>Cu>Sb>Pb>Be>Zn is mainly associated with the W>Sn mineralizations. Stream sediments receiving drainage of a mine dump were found to be significantly to extremely enriched with W, while stream sediments and soils were found to be contaminated with As. Two soil samples collected around mine dumps and an open pit lake were also found to be contaminated with U. The waters from the Murçós W>Sn mine area were acidic to neutral. After the remediation, the surface waters were contaminated with F(-), Al, As, Mn and Ni and must not be used for human consumption, while open pit lake waters must also not be used for agriculture because of contamination with F(-), Al, Mn and Ni. In most waters, the As occurred as As (III), which is toxic and is easily mobilized in the drainage system. The remediation promoted a decrease in metals and As concentrations of soils and waters, however the applied processes were not enough to rehabilitate the area.

Detection of crackle events using a multi-feature approach.

The automatic detection of adventitious lung sounds is a valuable tool to monitor respiratory diseases like chronic obstructive pulmonary disease. Crackles are adventitious and explosive respiratory sounds that are usually associated with the inflammation or infection of the small bronchi, bronchioles and alveoli. In this study a multi-feature approach is proposed for the detection of events, in the frame space, that contain one or more crackles. The performance of thirty-five features was tested. These features include thirty-one features usually used in the context of Music Information Retrieval, a wavelet based feature as well as the Teager energy and the entropy. The classification was done using a logistic regression classifier. Data from seventeen patients with manifestations of adventitious sounds and three healthy volunteers were used to evaluate the performance of the proposed method. The dataset includes crackles, wheezes and normal lung sounds. The optimal detection parameters, such as the number of features, were chosen based on a grid search. The performance of the detection was studied taking into account the sensitivity and the positive predictive value. For the conditions tested, the best results were obtained for the frame size equal to 128 ms and twenty-seven features.

Avaliação da qualidade de amostras comerciais de leite de janaguba (Himatanthus drasticus (Mart. ) Plumel) em Fortaleza – Ceará / Quality assessment of commercial samples of janaguba’s milk (Himatanthus drasticus (Mart. ) Plumel) in Fortaleza – Ceará.

RESUMO Os fitoterápicos à base de leite de janaguba (Himatanthus drasticus (Mart.) Plumel), usados para o tratamento de câncer, úlcera gástrica e outras doenças, são muito vendidos em mercados públicos de Fortaleza (CE). No entanto, registros mencionam que é comum a troca deste leite por látex de mangabeira (Hancornia speciosa Gomes). O trabalho objetivou avaliar a qualidade físico-química, química e microbiológica de amostras comerciais do leite de janaguba. Dez amostras comerciais foram adquiridas de um mercado de Fortaleza; quatro amostras autênticas de látex de janaguba foram obtidas da chapada do Araripe e uma amostra de látex de mangabeira foi obtida em Paracuru (CE). Foram determinados o aspecto geral, densidade, pH, resíduo seco, volume de sedimentação, perfil cromatográfico e qualidade microbiológica das amostras. Os resultados mostraram elevada contaminação microbiológica nas preparações comerciais e adulteração em seis destas amostras, o que aponta a urgente implantação de uma efetiva farmacovigilância dos fitoterápicos a fim de tornar seu consumo mais seguro e racional.

ABSTRACT The herbal medicines made from janaguba milk (Himatanthus drasticus (Mart.) Plumel), used to treat gastric ulcer, cancer and other diseases, and arewidely sold in the public markets of Fortaleza (CE). However, records mention that it is common to make fake copies of the milk by using mango tree latex (Hanconia speciosa Gomes). This study aimed to evaluate the chemical, physical-chemical and microbiological quality of commercial samples of janaguba milk. Ten commercial samples were purchased from a Fortaleza market; four authentic samples of janaguba latex were obtained from the Araripe plateau, and one mango tree latex sample was obtained in Paracuru (CE). We determined the general appearance, density, pH, dry residue, sedimentation volume, chromatographic profile and microbiological qualities. Results showed high microbiological contamination in the commercial preparations and evidence of tampering in six of these samples, which indicates the need for an urgent implementation of an effective pharmacovigilance strategy for herbal medicines in order to make its consumption more secure and rational.

Assessment of cardiovascular function from multi-Gaussian fitting of a finger photoplethysmogram.

Monitoring of cardiovascular function on a beat-to-beat basis is fundamental for protecting patients in different settings including emergency medicine and interventional cardiology, but still faces technical challenges and several limitations. In the present study, we propose a new method for the extraction of cardiovascular performance surrogates from analysis of the photoplethysmographic (PPG) signal alone.We propose using a multi-Gaussian (MG) model consisting of five Gaussian functions to decompose the PPG pulses into its main physiological components. From the analysis of these components, we aim to extract estimators of the left ventricular ejection time, blood pressure and vascular tone changes. Using a multi-derivative analysis of the components related with the systolic ejection, we investigate which are the characteristic points that best define the left ventricular ejection time (LVET). Six LVET estimates were compared with the echocardiographic LVET in a database comprising 68 healthy and cardiovascular diseased volunteers. The best LVET estimate achieved a low absolute error (15.41 ± 13.66 ms), and a high correlation (ρ = 0.78) with the echocardiographic reference.To assess the potential use of the temporal and morphological characteristics of the proposed MG model components as surrogates for blood pressure and vascular tone, six parameters have been investigated: the stiffness index (SI), the T1_d and T1_2 (defined as the time span between the MG model forward and reflected waves), the reflection index (RI), the R1_d and the R1_2 (defined as their amplitude ratio). Their association to reference values of blood pressure and total peripheral resistance was investigated in 43 volunteers exhibiting hemodynamic instability. A good correlation was found between the majority of the extracted and reference parameters, with an exception to R1_2 (amplitude ratio between the main forward wave and the first reflection wave), which correlated low with all the reference parameters. The highest correlation ([Formula: see text] = 0.45) was found between T1_2 and the total peripheral resistance index (TPRI); while in the patients that experienced syncope, the highest agreement ([Formula: see text] = 0.57) was found between SI and systolic blood pressure (SBP) and mean blood pressure (MBP).In conclusion, the presented method for the extraction of surrogates of cardiovascular performance might improve patient monitoring and warrants further investigation.

Integration of Different Risk Assessment Tools to Improve Stratification of Patients with Coronary Artery Disease.

Cardiovascular disease (CVD) causes unaffordable social and health costs that tend to increase as the European population ages. In this context, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Some useful tools have been developed to predict the risk of occurrence of a cardiovascular disease event (e.g. hospitalization or death). However, these tools present some drawbacks. These problems are addressed through two methodologies: (i) combination of risk assessment tools: fusion of naïve Bayes classifiers complemented with a genetic optimization algorithm and (ii) personalization of risk assessment: subtractive clustering applied to a reduced-dimensional space to create groups of patients. Validation was performed based on two ACS-NSTEMI patient data sets. This work improved the performance in relation to current risk assessment tools, achieving maximum values of sensitivity, specificity, and geometric mean of, respectively, 79.8, 83.8, and 80.9 %. Additionally, it assured clinical interpretability, ability to incorporate of new risk factors, higher capability to deal with missing risk factors and avoiding the selection of a standard CVD risk assessment tool to be applied in the clinical practice.

Expert knowledge integration in the data mining process with application to cardiovascular risk assessment.

The data mining process, when applied to clinical databases, suffers from critical data problems, from noisy acquisitions to missing or incomplete data points. Expert knowledge, in the form of practitioners' experience and clinical guidelines, is already used to manually correct some of these problems, while enhancing expert's confidence in such systems. In this work, we propose the Knowledge-Biased Tree (KB3), a knowledge biased decision tree inducer that is able to exploit IF THEN rules to guide the tree inducing process. The KB3 approach was tested against its unbiased counterpart, the C5.0 algorithm in the cardiovascular risk assessment task. Using a clinical dataset provided by the hospital of Sta Cruz (Lisbon, Portugal) the performance of the proposed algorithm is compared against the unbiased C5.0 and the state of the art risk score used in clinical practice (GRACE risk score).

Assessment of cardiovascular risk based on a data-driven knowledge discovery approach.

The cardioRisk project addresses the development of personalized risk assessment tools for patients who have been admitted to the hospital with acute myocardial infarction. Although there are models available that assess the short-term risk of death/new events for such patients, these models were established in circumstances that do not take into account the present clinical interventions and, in some cases, the risk factors used by such models are not easily available in clinical practice. The integration of the existing risk tools (applied in the clinician's daily practice) with data-driven knowledge discovery mechanisms based on data routinely collected during hospitalizations, will be a breakthrough in overcoming some of these difficulties. In this context, the development of simple and interpretable models (based on recent datasets), unquestionably will facilitate and will introduce confidence in this integration process. In this work, a simple and interpretable model based on a real dataset is proposed. It consists of a decision tree model structure that uses a reduced set of six binary risk factors. The validation is performed using a recent dataset provided by the Portuguese Society of Cardiology (11113 patients), which originally comprised 77 risk factors. A sensitivity, specificity and accuracy of, respectively, 80.42%, 77.25% and 78.80% were achieved showing the effectiveness of the approach.

Personalization algorithms applied to cardiovascular disease risk assessment.

Cardiovascular disease (CVD) is the major cause of death in the world. Clinical guidelines recommend the use of risk assessment tools (scores) to identify the CVD risk of each patient as the correct stratification of patients may significantly contribute to the optimization of the health care strategies. This work further explores the personalization of CVD risk assessment, supported on the evidence that a specific CVD risk assessment tool may have good performance within a given group of patients and might perform poorly within other groups. Two main personalization methods based on the proper creation of groups of patients are presented: i) clustering patients approach; ii) similarity measures approach. These two methodologies were validated in a Portuguese population (460 Acute Coronary Syndrome with non-ST segment elevation (ACS-NSTEMI) patients). The similarity measures approach had the best performance, achieving maximum values of sensitivity, specificity and geometric mean of, respectively, 77.7%, 63.2%, 69.7%. These values represent an enhancement in relation to the best performance obtained with current CVD risk assessment tools applied in clinical practice (78.5%, 53.2%, 64.4%).

Cardiovascular disease risk assessment innovative approaches developed in HeartCycle project.

Two innovative CVD event risk assessment strategies were developed in the scope of HeartCycle project: i) combination of individual risk assessment tools; ii) personalization of risk assessment based on grouping of patients. These approaches aimed to defeat some of the major limitations of the tools currently applied in the daily clinical practice, namely to: i) improve the risk prediction performance when comparing it to the one achieved by the individual current risk assessment tools; ii) consider the available knowledge provided by other risk assessment tools; iii) cope with missing risk factors; iv) incorporate additional clinical knowledge. Two different real patients' datasets were applied to validate the developed strategies: i) Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI1 patients; ii) Leiria Pombal Hospital Centre, Portugal, N=99 ACS-NSTEMI. Based on the gathered results, we propose a new strategy in order to improve patients' stratification.

Multi-Gaussian fitting for the assessment of left ventricular ejection time from the photoplethysmogram.

The Left ventricular ejection time (LVET) is one of the primary surrogates of the left ventricular contractility and stroke volume. Its continuous monitoring is considered to be a valuable hypovolumia prognostic parameter and an important risk predictor in cardiovascular diseases such as cardiac and light chain amyloidosis. In this paper, we present a novel methodology for the assessment of LVET based the Photoplethysmographic (PPG) waveform. We propose the use of Gaussian functions to model both systolic and diastolic phases of the PPG beat and consequently determine the onset and offset of the systolic ejection from the analysis of the systolic phase 3(rd) derivative. The results achieved by the proposed methodology were compared with the algorithm proposed by Chan et al. [1], revealing better estimation of LVET (15.84 ± 13.56 ms vs 23.01 ± 14.60 ms), and similar correlation with the echocardiographic reference (0.73 vs 0.75).

An educational international partnership responding to local needs: process evaluation of the Brazil FAIMER Regional Institute.

INTRODUCTION: The Brazilian public health system requires competent professionals sensitive to the needs of the population. The Foundation for Advancement of International Medical Education and Research (FAIMER) provides a two-year faculty development programme for health professions educators, aiming to build leadership in education to improve health. A partnership with governmental initiatives and FAIMER was established for meeting these needs. This paper describes the initial process evaluation results of the Brazilian FAIMER Institute Fellowship (FAIMER BR). METHODS: Data were analysed for the classes 2007-2010 regarding: application processes; innovation project themes; retrospective post-pre self-ratings of knowledge acquisition; and professional development portfolios. RESULTS: Seventeen of 26 Brazilian states were represented among 98 Fellows, predominantly from public medical schools (75.5%) and schools awarded Ministry of Health grants to align education with public health services (89.8%). One-third (n = 32) of Fellows' innovation projects were related to these grants. Significant increases occurred in all topic subscales on self-report of knowledge acquisition (effect sizes, 1.21-2.77). In the follow up questionnaire, 63% of Fellows reported that their projects were incorporated into the curriculum or institutional policies. The majority reported that the programme deepened their knowledge (98%), provided new ideas about medical education (90%) and provided skills for conflict management (63%). One-half of the Fellows reported sustained benefits from the programme listserv and other communications, including breadth of expertise, establishment of research collaboration and receiving emotional support. CONCLUSION: Contributors to initial programme success included alignment of curriculum with governmental initiatives, curriculum design merging educational technology, leadership and management skills and central role of an innovation educational project responding to local needs.

Improvement of CVD risk assessment tools' performance through innovative patients' grouping strategies.

There are available in the clinical community several practical risk tools to assess the risk of occurrence of a cardiovascular event. Although valuable, these tools typically present some lack of performance (low sensitivity/low specificity) when applied to a general (average) patient. This flaw is addressed in this work through an innovative personalization strategy that is supported on the evidence that current risk assessment tools perform differently among different populations/groups of patients. The proposed methodology is based on two main hypotheses: i) patients are grouped through a proper dimension reduction technique complemented with an unsupervised learning algorithm, ii) for each group the most suitable risk assessment tool can be selected improving the risk prediction performance. As a result, risk personalization is simply achieved by the identification of the group that patients belong to. The validation of the strategy is carried out through the combination of three current risk assessment tools (GRACE, TIMI, PURSUIT) developed to predict the risk of an event in coronary artery disease patients. The combination of these tools is validated with a real patient testing dataset: Santa Cruz Hospital, Portugal, N=460 ACS-NSTEMI patients. Considering the obtained results with the available dataset it is possible to state that the main objective of this work was achieved.