Testing the validity, reliability and utility of the Self-Administration of Medication (SAM) tool in patients undergoing rehabilitation.

Determination of patients' ability to self-administer medications in the hospital has largely been determined using the subjective judgment of health professionals.

Validation and development of a clinical prediction rule in clinically suspected community-acquired pneumonia

To develop a mathematical model to predict the probability of having community-acquired pneumonia and to evaluate an already developed prediction rule that has not been validated in a clinical scenario.

Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators

Plasminogen (Pg), the precursor of the proteolytic and fibrinolytic enzyme of blood, is converted to the active enzyme plasmin (Pm) by different plasminogen activators (tissue plasminogen activators and urokinase), including the bacterial activators streptokinase and staphylokinase, which activate Pg to Pm and thus are used clinically for thrombolysis. The identification of Pg-activators is therefore an important step in understanding their functional mechanism and derives new therapies.

Characterization and prediction of issue-related risks in software projects

Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing “risky” software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macro-averaged Mean Cost-Error and 0.7-1.2 for Macro-averaged Mean Absolute Error.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

The addition of depression to the Framingham Risk Equation model for predicting coronary heart disease risk in women

BACKGROUND: Depression is widely considered to be an independent and robust predictor of Coronary Heart Disease (CHD), however is seldom considered in the context of formal risk assessment. We assessed whether the addition of depression to the Framingham Risk Equation (FRE) improved accuracy for predicting 10-year CHD in a sample of women.

DESIGN: A prospective, longitudinal design comprising an age-stratified, population-based sample of Australian women collected between 1993 and 2011 (n=862).

METHODS: Clinical depressive disorder was assessed using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID-I/NP), using retrospective age-of-onset data. A composite measure of CHD included non-fatal myocardial infarction, unstable angina coronary intervention or cardiac death. Cox proportional-hazards regression models were conducted and overall accuracy assessed using area under receiver operating characteristic (ROC) curve analysis.

RESULTS: ROC curve analyses revealed that the addition of baseline depression status to the FRE model improved its overall accuracy (AUC:0.77, Specificity:0.70, Sensitivity:0.75) when compared to the original FRE model (AUC:0.75, Specificity:0.73, Sensitivity:0.67). However, when calibrated against the original model, the predicted number of events generated by the augmented version marginally over-estimated the true number observed.

CONCLUSIONS: The addition of a depression variable to the FRE equation improves the overall accuracy of the model for predicting 10-year CHD events in women, however may over-estimate the number of events that actually occur. This model now requires validation in larger samples as it could form a new CHD risk equation for women.

BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

Validação da triagem de macroprolactinemia utilizando precipitação com polietilenoglicol e ensaio quimioluminescente

A prolactina(PRL) humana circula em múltiplas formas de diferentes tamanhos moleculares, das quais três são detectadas na cromatografia por filtração em gel(CFG): Little ou monomérica(mPRL), Big( bPRL) e Big-Big ou macroprolactina( bbPRL ou macroPRL). Em pessoas normais, a principal forma é a mPRL(85 a 90% do total) com uma pequena proporção de macroPRL. Em algumas pessoas, porém, ocorre uma maior quantidade de macroPRL, um fenômeno denominado Macroprolactinemia, que se sabe estar presente entre 10-25% dos soros hiperprolactinêmicos. É importante a sua identificação para evitar investigação e tratamento desnecessário. O método padrão para sua detecção é a cromatografia por filtração em gel( CFG); porém, a precipitação com polietilenoglicol( PEG) é um método de triagem simples, confiável e de baixo custo. Os testes com PEG originais foram feitos com o ensaio imunofluorimétrico( IFMA) Delfia para PRL. Objetivo: Validar um teste sensível e específico para a triagem de macroPRL baseado no ensaio de PRL quimioluminescente Immulite DPC. Resultados e métodos: Analisamos amostras séricas de 142 pessoas de ambos sexos. Baseado nessas amostras de rotina, dosamos a PRL seguida da precipitação com PEG e cálculo da recuperação de PRL( %R de PRL). Destes soros, 88 foram submetidos a cromatografia com filtração em Gel. Foi definido um ponto-de-corte para a presença de macroPRL, baseado numa curva ROC, ao comparar-se os resultados do teste de precipitação com PEG e os da CFG. O ponto-de-corte foi definido em 60%, com o achado de um teste com sensibilidade de 88,9% e especificidade de 98,6%. Correlacionou-se a dosagem de mPRL com a presença de macroPRL na cromatografia. Conclusão: Validamos um teste de triagem para macroPRL baseado no ensaio quimioluminescente DPC Immulite com sensibilidade de 88,9% e especificidade de 98,6 % para a porcentagem de recuperação PRL de 60%. O achado de uma valor de mPRL menor ou igual a 20 depois da precipitação com PEG vai suportar este diagnóstico. A prevalência( 20,4%) de macroPRL encontrada em nosso estudo, utilizando a metodologia proposta, é semelhante à encontrada na literatura.

Acurácia do teste de labirinto no rastreamento de déficits cognitivos em idosos

This study intended to evaluate the maze test accuracy in cognitive deficit screening in elderly with or without neuropsychological pathology. The sample included 40 healthy young (18-25 years old; mean- 21 ± 1.6), 40 healthy old (60-77 years old; mean- 67 ± 5.1) and 18 patients with probable diagnosis of Alzheimer s disease initial stage (52-90 years old; mean- 78 ± 9.2). Data analysis was made using Anova with Tukey s post hoc, multiple linear regression analysis and ROC curve analysis. According to Tukey s test Alzheimer patients spent more time (46843 ± 37926 ms) to execute the test than healthy young (5482 ± 2873 ms; p= 0.0001) and elderly (17978 ± 13700; p= 0.0001); healthy young executed test n lower time (p= 0.035). According to the regression analysis of age, education level and cognitive performance of the three groups, the cognitive performance was the predictor of the execution time. When analyzing young and elderly only age was the predictor and the cognitive performance was the only factor to influence the test of old aged healthy and patients. The ROC curve analysis indicated 72% accuracy for young and elderly and 36% for healthy and elderly patients. The maze execution time represented a better balance between sensibility (75%) and the specificity (61%) was near 13575 ms, indicating that those subjects that execute the maze in a time higher to this value may show cognitive deficit related to the executive function. According to the results it is suggested that the maze test used in this study shows a good accuracy in the cognitive deficit tracking and may discriminate age changes

Sistema inteligente para detecção de manchas de óleo na superfície marinha através de imagens de SAR

Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents

Drug resistance in Mycobacterium tuberculosis clinical isolates from Brazil: Phenotypic and genotypic methods

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Velocidade da marcha como preditor de fragilidade em uma amostra populacional de idosos brasileiros

Gait speed has been described as a predictive indicator of important adverse outcomes in older populations. Among the criteria to evaluate frailty, gait speed has been identified as the most reliable predictor of fragility, practical and low cost. Objective: This study assesses the discriminating capability of gait speed in determining the presence of fragility in the elderly community in northeast of Brazil. Method: We performed an observational analytic study with a transversal character with a sample of 391 community-living elders, aged 65 years or older, of both sexes, in the city of Santa Cruz-RN. Participants were interviewed using a multidimensional questionnaire to obtain sociodemographic information, physical-related and mental health-related information. The unintentional weight loss, muscle weakness, self-reported exhaustion, slow gait and low-physical activity were considered to evaluate the frailty syndrome. Gait velocity was measured as the time taken to walk the middle 4,6 meters of 8,6 meters (excluding 2 meters to warm-up phase and 2 meters to deceleration phase).We calculate the sensitivity and specificity of gait speed test in different cutoff points for the test run time, from which ROC curve was constructed as a measure of test predictive value to identify frail elders. The prevalence of frailty in Santa Cruz-RN was 17.1%. The gait speed test accuracy was 71%when speed is below 0,91m/s. Among women, the gait speed test accuracy was 80%(gait speed below 0.77m/s) and among men, the test accuracy was 86% (gait spend below 0,82%) (p<0,0001).Conclusion: our findings have clinical relevance when we consider that the detection of frailty presence by the gait speed test can be observed in elderly men and women by a simple, cheap and efficient exam

Early echocardiographic predictors of increased left ventricular end-diastolic pressure three months after myocardial infarction in rats

Background: The objective of this study was to determine the early echocardiographic predictors of elevated left ventricular end-diastolic pressure (LVEDP) after a long follow-up period in the infarcted rat model.Material/Methods: Five days and three months after surgery, sham and infarcted animals were subjected to transthoracic echocardiography. Regression analysis and receiver-operating characteristic (ROC) curve were performed for predicting increased LVEDP 3 months after MI.Results: Among all of the variables, assessed 5 days after myocardial infarction, infarct size (OR: 0.760; CI 95% 0.563-0.900; p=0.005), end-systolic area (ESA) (OR: 0.761; Cl 95% 0.564-0.900; p=0.008), fractional area change (FAC) (OR: 0.771; CI 95% 0.574-0.907; p=0.003), and posterior wall-shortening velocity (PWSV) (OR: 0.703; CI 95% 0.502-0.860; p=0.048) were predictors of increased LVEDP. The LVEDP was 3.6 +/- 1.8 mmHg in the control group and 9.4 +/- 7.8 mmHg among the infarcted animals (p=0.007). Considering the critical value of predictor variables in inducing cardiac dysfunction, the cut-off value was 35% for infarct size, 0.33 cm(2) for ESA, 40% for FAC, and 26 mm/s for PWSV.Conclusions: Infarct size, FAC, ESA, and PWSV, assessed five days after myocardial infarction, can be used to estimate an increased LVEDP three months following the coronary occlusion.

Markers of uremia and pericardial effusion in peritoneal dialysis

Inadequate dialysis causes accumulation of toxic residues that may lead to the development of dialysis-associated pericardial effusion, but several other factors could be associated with this abnormality. The purpose of this study was to evaluate clinical risk factors to asymptomatic pericardial effusion in peritoneal dialysis.This cross-sectional study included 34 patients aged a parts per thousand yen18 years on peritoneal dialysis for at least 3 months, who showed no symptomatic pericardial effusion, hepatic cirrhosis, neoplasias, lupus or amputations, none in minoxidil use. Asymptomatic pericardial effusion was diagnosed by echocardiography. Risk factors were evaluated by logistic regression and Roc curve. Significance level was set at P < 0.05.Patient age was 51 +/- A 15.9 years. of the 34 patients enrolled, 16 were men and 11 diabetic. Five of them presented pericardial effusion. Logistic regression identifies low hemoglobin level (RR 0.454 CI 95%: 0.225-0.913; P = 0.027), low phase angle (RR 0.236 CI 95%: 0.057-0.984; P = 0.048) and low Kt/V (RR 0.001 CI 95%: 0.0-0.492; P = 0.03) as risk factors to pericardial effusion. Roc curve showed that hemoglobin levels below 12.2 g/dL, Kt/V lower than 1.9 and phase angle lower than 4.5A degrees were the best cutoffs to predict pericardial effusion. Four patients showed these three parameters in the unfavorable range, and all these four patients presented pericardial effusion. The other patient with pericardial effusion had two of these parameters reduced.These findings corroborate the hypothesis that uremia plays a significant role in the pathogenesis of dialysis-associated pericardial effusion.