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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3557145 | PMC |
| http://dx.doi.org/10.1186/1472-6947-12-143 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Soil contamination by heavy metals (HM) is a critical area of research. Traditional methods involving sample collection and lab analysis are effective but costly and time-consuming. This study explores whether geostatistical analysis with GIS and open data can provide a faster, more precise, and cost-effective alternative for HM contamination assessment without extensive sampling.
View Article and Find Full Text PDFThe Impact of Temperature on Extracting Information From Clinical Trial Publications Using Large Language Models.
Cureus
December 2024
Department of Radiation Oncology, Cantonal Hospital Winterthur, Winterthur, CHE.
Introduction The application of natural language processing (NLP) for extracting data from biomedical research has gained momentum with the advent of large language models (LLMs). However, the effect of different LLM parameters, such as temperature settings, on biomedical text mining remains underexplored and a consensus on what settings can be considered "safe" is missing. This study evaluates the impact of temperature settings on LLM performance for a named entity recognition and a classification task in clinical trial publications.
View Article and Find Full Text PDFMining Druggable Sites in Influenza A Hemagglutinin: Binding of the Pinanamine-Based Inhibitor M090.
ACS Med Chem Lett
January 2025
Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Facultat de Farmàcia i Ciències de l'Alimentació - Campus Torribera, Universitat de Barcelona, Prat de la Riba 171, 08921 Santa Coloma de Gramenet, Spain.
Assessing the binding mode of drug-like compounds is key in structure-based drug design. However, this may be challenged by factors such as the structural flexibility of the target protein. In this case, state-of-the-art computational methods can be valuable to explore the linkages between structural and pharmacological data.
View Article and Find Full Text PDFHigh-Throughput Evaluation of Natural Diversity of F-Type ATP Synthase Rotor Ring Stoichiometries.
Proteins
January 2025
Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Russia.
Adenosine triphosphate (ATP) synthases are large enzymes present in every living cell. They consist of a transmembrane and a soluble domain, each comprising multiple subunits. The transmembrane part contains an oligomeric rotor ring (c-ring), whose stoichiometry defines the ratio between the number of synthesized ATP molecules and the number of ions transported through the membrane.
View Article and Find Full Text PDFMind the Data Gap: Using a Multi-Measurement Synthesis for Identifying the Challenges and Opportunities in Studying Plant Drought Response and Recovery.
Plant Cell Environ
January 2025
Civil, Environmental, and Mining Engineering, University of Western Australia, Perth, Western Australia, Australia.
Understanding and predicting plant water dynamics during and after water stress is increasingly important but challenging because the high-dimensional nature of the soil-plant-atmosphere system makes it difficult to identify mechanisms and constrain behaviour. Datasets that capture hydrological, physiological and meteorological variation during changing water availability are relatively rare but offer a potentially valuable resource to constrain plant water dynamics. This study reports on a drydown and re-wetting experiment of potted Populus trichocarpa, which intensively characterised plant water fluxes, water status and water sources.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!