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Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks

Addy Cecilia Helguera-Repetto, María Dolores Soto-Ramírez, Oscar Villavicencio-Carrisoza, Samantha Yong-Mendoza, Angélica Yong-Mendoza, Moisés León-Juárez, Jorge A. González-y-Merchand, Verónica Zaga-Clavellina, Claudine Irles
2020 Frontiers in Pediatrics  
A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and  ...  Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms.  ...  We also acknowledge the Instituto Nacional de Perinatología Isidro Espinosa de los Reyes [grant numbers #2017-2-65 to CI and #212250-3210-11007-04-14 to AH-R]; CONACyT [grant numbers PDCPN2013-01-  ... 
doi:10.3389/fped.2020.00525 pmid:33042902 pmcid:PMC7518045 fatcat:65ggbtuwbbcthhxjoco6oho7xu

Intelligent Neonatal Sepsis Early Diagnosis System for Very Low Birth Weight Infants

Fabio Tarricone, Antonio Brunetti, Domenico Buongiorno, Nicola Altini, Vitoantonio Bevilacqua, Antonio Del Vecchio, Flavia Petrillo
2021 Applied Sciences  
In this work, the authors propose an optimised artificial neural network model able to diagnose sepsis early based on the HeRO score along with a series of parameters strictly connected to the risk of  ...  neonatal sepsis.  ...  ANN Classifier The intelligent neonatal sepsis early diagnosis system (I.N.E.S.) proposed in this work allows the early detection of sepsis by using an appropriate artificial neural network, processing  ... 
doi:10.3390/app11010404 fatcat:e36k2upikvd3tkbuzicixmswfq

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

Daniele Roberto Giacobbe, Alessio Signori, Filippo Del Puente, Sara Mora, Luca Carmisciano, Federica Briano, Antonio Vena, Lorenzo Ball, Chiara Robba, Paolo Pelosi, Mauro Giacomini, Matteo Bassetti
2021 Frontiers in Medicine  
In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical  ...  In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in  ...  In the long run, a rigorous multidisciplinary approach to enrich our understanding of the application of machine learning techniques to the early recognition of sepsis may be worth the trip and truly augment  ... 
doi:10.3389/fmed.2021.617486 pmid:33644097 pmcid:PMC7906970 fatcat:pdrnnb44urhtlcrrv4h3iflg5y

Kinematics approach with neural networks for early detection of sepsis (KANNEDS)

Márcio Freire Cruz, Naoaki Ono, Ming Huang, Md Altaf-Ul-Amin, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante
2021 BMC Medical Informatics and Decision Making  
Applying our novel approach for early detection of sepsis using neural networks will prove to be an invaluable, more accurate method than considering only simple vital signs as input variables.  ...  We imputed these kinematics features as explanatory variables of long short-term memory (LSTM), convolutional neural network (CNN) and linear neural network (LNN) and compared the prediction accuracies  ...  Acknowledgements We would like to thank Editage (www. edita ge. com) for English language editing.  ... 
doi:10.1186/s12911-021-01529-3 pmid:34016115 fatcat:7avycg5qgncu7ejopy243drflm

Nearest-Neighbor and Logistic Regression Analyses of Clinical and Heart Rate Characteristics in the Early Diagnosis of Neonatal Sepsis

Yuping Xiao, M. Pamela Griffin, Douglas E. Lake, J. Randall Moorman
2009 Medical decision making  
Conclusion-We propose nearest-neighbor analysis in addition to regression in the early diagnosis of sub-acute, potentially catastrophic illnesses like neonatal sepsis, and we recommend it as an approach  ...  Objectives-To test the hypothesis that nearest-neighbor analysis adds to logistic regression in the early diagnosis of late-onset neonatal sepsis.  ...  Acknowledgments We thank WE King for suggesting nearest-neighbor analysis to us.  ... 
doi:10.1177/0272989x09337791 pmid:19541797 pmcid:PMC2962439 fatcat:n4ezwpl5tngvdm63vil43r73k4

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors

Claudine Irles, Gabriela González-Pérez, Sandra Carrera Muiños, Carolina Michel Macias, César Sánchez Gómez, Anahid Martínez-Zepeda, Guadalupe Cordero González, Estibalitz Laresgoiti Servitje
2018 International Journal of Environmental Research and Public Health  
The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding  ...  The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique.  ...  role in predicting intestinal perforation associated with NEC, we performed a sensitivity analysis to the trained and validated neural network, as previously described ( [26, 27] and Garson algorithm  ... 
doi:10.3390/ijerph15112509 pmid:30423965 fatcat:v3ade4wwszdf5mve43srkqnb3q

Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review [article]

Michael Moor, Bastian Rieck, Max Horn, Catherine Jutzeler, Karsten Borgwardt
2020 biorxiv/medrxiv   pre-print
A multitude of machine learning algorithms were applied to refine the early prediction of sepsis.  ...  Conclusions and key findings: A growing number of studies employs machine learning to31optimise the early prediction of sepsis through digital biomarker discovery.  ...  ACKNOWLEDGMENTS This manuscript has been released as a preprint at medRxiv (75) .  ... 
doi:10.1101/2020.08.31.20185207 fatcat:5dpw3plbnfe6jdn64ftusdfho4

Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review

Michael Moor, Bastian Rieck, Max Horn, Catherine R. Jutzeler, Karsten Borgwardt
2021 Frontiers in Medicine  
A multitude of machine learning algorithms were applied to refine the early prediction of sepsis.  ...  of studies employs machine learning to optimize the early prediction of sepsis through digital biomarker discovery.  ...  ACKNOWLEDGMENTS This manuscript has been released as a preprint at medRxiv (75) .  ... 
doi:10.3389/fmed.2021.607952 pmid:34124082 pmcid:PMC8193357 fatcat:qysauh7vznfj3kuyxihour7qay

Performance Comparison of Systemic Inflammatory Response Syndrome with Logistic Regression Models to Predict Sepsis in Neonates

Jyoti Thakur, Sharvan Pahuja, Roop Pahuja
2017 Children  
In this paper, we examined the accuracy of SIRS in predicting sepsis in neonates, irrespective of their gestational age (i.e., pre-term, term, and post-term).  ...  In 2012, Hofer et al. investigated the predictive power of SIRS for term neonates.  ...  To this end, we created an android application that can predict sepsis in neonates using all three models; namely, SIRS, Model A and Model B.  ... 
doi:10.3390/children4120111 pmid:29257099 pmcid:PMC5742756 fatcat:kvzaz23iqbeulkmnggqssofx3q

Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review

Cheyenne Mangold, Sarah Zoretic, Keerthi Thallapureddy, Axel Moreira, Kevin Chorath, Alvaro Moreira
2021 Neonatology  
Number of features ranged from 3 to 66 with timing of prediction as early as 5 min of life to a maximum of 7 days of age.  ...  The average number of models per study was 4, with neural network, random forest, and logistic regression comprising the most used models (58.3%).  ...  Studies were eligible if they included AI-based algorithms (e.g., ML, deep learning, neural network, etc.) to predict neonatal death.  ... 
doi:10.1159/000516891 pmid:34261070 fatcat:wf7u3ivj3ff4ta23ktmawj7xbq

Metabolomics: Which Role in Asphyxia and Sepsis?

Paola Scano
2014 Journal of Anesthesia & Clinical Research  
The purpose of this review is also to highlight the ability of metabolomics to find early biomarkers for these conditions as well as to predict the development of side\effects due to the therapy.  ...  Metabolomics is a new "omics" approach concerning the high-throughput identification, quantification and characterization of endogenous and exogenous metabolites.  ...  Using these metabolites, a predictive model called radial basis function neural network was created for an early prognostic evaluation of sepsis.  ... 
doi:10.4172/2155-6148.1000420 fatcat:ne3h7dabyrcidbhdhraxkhgxb4

Machine learning in critical care: state-of-the-art and a sepsis case study

Alfredo Vellido, Vicent Ribas, Carles Morales, Adolfo Ruiz Sanmartín, Juan Carlos Ruiz Rodríguez
2018 BioMedical Engineering OnLine  
the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ...  Results: The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost  ...  Artificial neural networks (ANNs) were, from very early on, picked up as building blocks of the design of monitoring alarms, specifically for monitoring of patients under anesthesia [28, 29] .  ... 
doi:10.1186/s12938-018-0569-2 pmid:30458795 pmcid:PMC6245501 fatcat:g6pdswcqwvd5bg3svjtlarvgsi

Feed-forward neural networks using cerebral MR spectroscopy and DTI might predict neurodevelopmental outcome in preterm neonates

T. Janjic, S. Pereverzyev, M. Hammerl, V. Neubauer, H. Lerchner, V. Wallner, R. Steiger, U. Kiechl-Kohlendorfer, M. Zimmermann, A. Buchheim, A. E. Grams, E. R. Gizewski
2020 European Radiology  
We aimed to evaluate the ability of feed-forward neural networks (fNNs) to predict the neurodevelopmental outcome (NDO) of very preterm neonates (VPIs) at 12 months corrected age by using biomarkers of  ...  FNNs might be able to predict motor and cognitive development of VPIs at 12 months corrected age when employing biomarkers of cerebral 1H-MRS and DTI quantified at TEA. • A feed-forward neuronal network  ...  Acknowledgements The authors would like to thank Lukas Lamplmayr for the help in the realisation of the considered predictors, obtaining numerical results and the preparation of the manuscript.  ... 
doi:10.1007/s00330-020-07053-8 pmid:32683551 fatcat:e3jetpmtvbgk7jffwqvrztusha

Machine Learning for High Risk Pregnancies Pre-Term Birth Prediction: A Retrospective

M Ramla, S Sangeetha, S Nickolas
2018 International Journal of Engineering & Technology  
Moreover, it is to be addressed in the global scenario for sustainable development. Predicting stillbirths is still a distant reality.  ...  The primary focus of the paper is to throw light on the challenging issue of Preterm Birth Prediction.  ...  Reusing the model to predict other neonatal diseases such as jaundice and sepsis can be done further [9] .  ... 
doi:10.14419/ijet.v7i2.22.11799 fatcat:kpfwal6vdzdapdn5x6qmm2fkri

Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit

Mary K. Olive, Gabe E. Owens
2018 Translational Pediatrics  
Footnote Conflicts of Interest: The authors have no conflicts of interest to declare.  ...  A neural network model was built using vital signs and laboratory data of patients admitted to a medical ward with hematologic malignancies, and then used to predict clinical decompensations, with excellent  ...  In 2001 Griffin and colleagues showed that infants in neonatal intensive care units who developed sepsis demonstrated a loss of heart rate variability hours prior to other clinical signs of deterioration  ... 
doi:10.21037/tp.2018.04.03 pmid:29770293 pmcid:PMC5938248 fatcat:zaqicha6cjfvtearmfrsyo76sm
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