A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

Predicting plateau pressure in intensive medicine for ventilated patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

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.

Predicting harsh discipline in at-risk mothers: the moderating effect of socioeconomic deprivation severity

Socioeconomic disadvantage is an important predictor of maternal harsh discipline, but few studies have examined risk mechanisms for harsh parenting within disadvantaged samples. In the present study, parenting stress, family conflict, and child difficult temperament are examined as predictors of maternal harsh discipline among a group of 58 mothers from socioeconomically disadvantaged backgrounds and their young children between the ages of 1- to 4-years-old. Maternal harsh discipline was measured using standardized observations, and mothers reported on parenting stress, family conflict, and child temperament. Severity of socioeconomic deprivation was included as a moderator in these associations. Results showed that parenting stress and family conflict predicted maternal harsh discipline, but only in the most severely deprived families. These findings extend prior research on the processes through which socioeconomic deprivation severity and family functioning impact maternal harsh discipline within a high-risk sample of low-income families. They suggest that the spillover of negative parental functioning into parent–child interactions is particularly likely under conditions of substantial socioeconomic deprivation. Severity of socioeconomic stress seems to undermine maternal adaptive forms of coping, resulting in harsh disciplining practices. Intervention efforts aimed at improving parenting and family relations, as well as an adaptive coping style assume especial relevance.

Sistema inteligente para análise do posicionamento de árbitros de futebol

Dissertação de mestrado em Sistemas de Informação

Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil

Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables were: elevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.

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.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.

Value of the Qrs-T Angle in Predicting the Induction of Ventricular Tachyarrhythmias in Patients with Chagas Disease

Background:The QRS-T angle correlates with prognosis in patients with heart failure and coronary artery disease, reflected by an increase in mortality proportional to an increase in the difference between the axes of the QRS complex and T wave in the frontal plane. The value of this correlation in patients with Chagas heart disease is currently unknown.Objective:Determine the correlation of the QRS-T angle and the risk of induction of ventricular tachycardia / ventricular fibrillation (VT / VF) during electrophysiological study (EPS) in patients with Chagas disease.Methods:Case-control study at a tertiary center. Patients without induction of VT / VF on EPS were used as controls. The QRS-T angle was categorized as normal (0-105º), borderline (105-135º) or abnormal (135-180º). Differences between groups for continuous variables were analyzed with the t test or Mann-Whitney test, and for categorical variables with Fisher's exact test. P values < 0.05 were considered significant.Results:Of 116 patients undergoing EPS, 37.9% were excluded due to incomplete information / inactive records or due to the impossibility to correctly calculate the QRS-T angle (presence of left bundle branch block and atrial fibrillation). Of 72 patients included in the study, 31 induced VT / VF on EPS. Of these, the QRS-T angle was normal in 41.9%, borderline in 12.9% and abnormal in 45.2%. Among patients without induction of VT / VF on EPS, the QRS-T angle was normal in 63.4%, borderline in 14.6% and abnormal in 17.1% (p = 0.04). When compared with patients with normal QRS-T angle, those with abnormal angle had a fourfold higher risk of inducing ventricular tachycardia / ventricular fibrillation on EPS [odds ratio (OR) 4; confidence interval (CI) 1.298-12.325; p = 0.028]. After adjustment for other variables such as age, ejection fraction (EF) and QRS size, there was a trend for the abnormal QRS-T angle to identify patients with increased risk of inducing VT / VF during EPS (OR 3.95; CI 0.99-15.82; p = 0.052). The EF also emerged as a predictor of induction of VT / VF: for each point increase in EF, there was a 4% reduction in the rate of sustained ventricular arrhythmia on EPS.Conclusions:Changes in the QRS-T angle and decreases in EF were associated with an increased risk of induction of VT / VF on EPS.

Comparison of ACUITY and CRUSADE Scores in Predicting Major Bleeding during Acute Coronary Syndrome

Background:The ACUITY and CRUSADE scores are validated models for prediction of major bleeding events in acute coronary syndrome (ACS). However, the comparative performances of these scores are not known.Objective:To compare the accuracy of ACUITY and CRUSADE in predicting major bleeding events during ACS.Methods:This study included 519 patients consecutively admitted for unstable angina, non-ST-elevation or ST-elevation myocardial infarction. The scores were calculated based on admission data. We considered major bleeding events during hospitalization and not related to cardiac surgery, according to the Bleeding Academic Research Consortium (BARC) criteria (type 3 or 5: hemodynamic instability, need for transfusion, drop in hemoglobin ≥ 3 g, and intracranial, intraocular or fatal bleeding).Results:Major bleeding was observed in 31 patients (23 caused by femoral puncture, 5 digestive, 3 in other sites), an incidence of 6%. While both scores were associated with bleeding, ACUITY demonstrated better C-statistics (0.73, 95% CI = 0.63 - 0.82) as compared with CRUSADE (0.62, 95% CI = 0.53 - 0.71; p = 0.04). The best performance of ACUITY was also reflected by a net reclassification improvement of + 0.19 (p = 0.02) over CRUSADE’s definition of low or high risk. Exploratory analysis suggested that the presence of the variables ‘age’ and ‘type of ACS’ in ACUITY was the main reason for its superiority.Conclusion:The ACUITY Score is a better predictor of major bleeding when compared with the CRUSADE Score in patients hospitalized for ACS.

Modeling, evaluation and predicting of IT human resources performance

Magdeburg, Univ., Fak. für Informatik, Diss., 2012

Measuring and predicting non-functional properties of customizable programs

Magdeburg, Univ., Fak. für Informatik, Diss., 2012