Dataset Information

Applying data mining techniques to improve diagnosis in neonatal jaundice.

ABSTRACT:

Background

Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques.

Methods

This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology.This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa--EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer.Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia.

Results

The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic.

Conclusions

The findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.

SUBMITTER: Ferreira D

PROVIDER: S-EPMC3557145 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Applying data mining techniques to improve diagnosis in neonatal jaundice.

Ferreira Duarte D Oliveira Abílio A Freitas Alberto A

BMC medical informatics and decision making 20121207

<h4>Background</h4>Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal ...[more]

PMID: 23216895

Similar Datasets

Project description:Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification.

Project description:BackgroundPhototherapy is a widely accepted, effective first-line therapy for neonatal jaundice. It is traditionally used continuously but intermittent phototherapy has been proposed as an equally effective alternative with practical advantages of improved maternal feeding and bonding. The effectiveness of intermittent phototherapy compared with continuous phototherapy is unknown.ObjectivesTo assess the safety and effectiveness of intermittent phototherapy compared with continuous phototherapy.Search methodsSearches were conducted on 31 January 2022 in the following databases: CENTRAL via CRS Web, MEDLINE and Embase via Ovid. We also searched clinical trials databases and the reference lists of retrieved articles for randomised controlled trials (RCTs) and quasi-randomised trials.Selection criteriaWe included RCTs, cluster-RCTs and quasi-RCTs comparing intermittent phototherapy with continuous phototherapy in jaundiced infants (both term and preterm) up to the age of 30 days. We compared intermittent phototherapy with continuous phototherapy by any method and at any dose and duration as defined by the authors.Data collection and analysisThree review authors independently selected trials, assessed trial quality and extracted data from included studies. We performed fixed-effect analyses and expressed treatment effects as mean difference (MD), risk ratio (RR) and risk difference (RD) with 95% confidence intervals (CIs). Our primary outcomes of interest were rate of decline of serum bilirubin, and kernicterus. We used the GRADE approach to assess the certainty of evidence.Main resultsWe included 12 RCTs (1600 infants) in the review. There is one ongoing study and four awaiting classification. There was little or no difference between intermittent phototherapy and continuous phototherapy with respect to rate of decline of bilirubin in jaundiced newborn infants (MD -0.09 micromol/L/hr, 95% CI -0.21 to 0.03; I² = 61%; 10 studies; 1225 infants; low-certainty evidence). One study involving 60 infants reported no incidence of bilirubin induced brain dysfunction (BIND). It is uncertain whether either intermittent or continuous phototherapy reduces BIND because the certainty of this evidence is very low. There was little or no difference in treatment failure (RD 0.03, 95% CI 0.08 to 0.15; RR 1.63, 95% CI 0.29 to 9.17; 1 study; 75 infants; very low-certainty evidence) or infant mortality (RD -0.01, 95% CI -0.03 to 0.01; RR 0.69, 95% CI 0.37 to 1.31 I² = 0%; 10 studies, 1470 infants; low-certainty evidence). AUTHORS' CONCLUSIONS: The available evidence detected little or no difference between intermittent and continuous phototherapy with respect to rate of decline of bilirubin. Continuous phototherapy appears to be more effective in preterm infants, however, the risks of continuous phototherapy and the potential benefits of a slightly lower bilirubin level are unknown. Intermittent phototherapy is associated with a decrease in the total number of hours of phototherapy exposure. There are theoretical benefits to intermittent regimens but there are important safety outcomes that were inadequately addressed. Large, well designed, prospective trials are needed in both preterm and term infants before it can be concluded that intermittent and continuous phototherapy regimens are equally effective.

Project description:Floods are among the devastating types of disasters in terms of human life, social and financial losses. Authoritative data from flood gauges are scarce in arid regions because of the specific type of dry climate that dysfunctions these measuring devices. Hence, social media data could be a useful tool in this case, where a wealth of information is available online. This study investigates the reliability of flood related data quality collected from social media, particularly for an arid region where the usage of flow gauges is limited. The data (text, images and videos) of social media, related to a flood event, was analyzed using the Machine Learning approach. For this reason, digital data (758 images and 1413 video frames) was converted into numeric values through ResNet50 model using the VGG-16 architecture. Numeric data of images, videos and text was further classified using different Machine Learning algorithms. Receiver operating characteristics (ROC) curve and area under curve (AUC) methods were used to evaluate and compare the performance of the developed machine learning algorithms. This novel approach of studying the quality of social media data could be a reliable alternative in the absence of real-time flow gauges data. A flash flood that occurred in the United Arab Emirates (UAE) from March 7-11, 2016 was selected as the focus of this study. Random forest showed the highest accuracy of 80.18% among the five other classifiers for images and videos. Precipitation/rainfall data were used to validate social media data, which showed a significant relationship between rainfall and the number of posts. The validity of the machine learning models was assessed using the area under the curve, precision-recall curve, root mean square error, and kappa statistics to confirm the validity and accuracy of the model. The data quality of YouTube videos was found to have the highest accuracy followed by Facebook, Flickr, Twitter, and Instagram. These results showed that social media data could be used when gauge data is unavailable.

Dataset Information

Applying data mining techniques to improve diagnosis in neonatal jaundice.

Background

Methods

Results

Conclusions

Publications

Applying data mining techniques to improve diagnosis in neonatal jaundice.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets