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Using Kernel Methods and Model Selection for Prediction of Preterm Birth [article]

Ilia Vovsha, Ansaf Salleb-Aouissi, Anita Raja, Thomas Koch, Alex Rybchuk, Axinia Radeva, Ashwath Rajan, Yiwen Huang, Hatim Diab, Ashish Tomar,, Ronald Wapner
2016 arXiv   pre-print
We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model  ...  based on decision rules prescribed by physician experts for prediction of preterm birth.  ...  Tara Randis and Dr. Mary McCord for their invaluable contributions on preterm birth background and insightful discussions in an early stage of this project.  ... 
arXiv:1607.07959v2 fatcat:ejinz33bqvcijfuztgozmmtvaq

Hybrid Support Vector Machine to Preterm Birth Prediction

Noviyanti Santoso, Sri Pingit Wulandari
2018 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)  
This research will be made the prediction model of preterm birth using hybrid multivariate adaptive regression splines (MARS) and Support Vector Machine (SVM).  ...  Preterm birth is one of the major contributors to perinatal and neonatal mortality.  ...  ACKNOWLEDGEMENT We would like to thank Department of Bussines Statistics ITS for support and special thanks to the anonymous hospital in Sumenep for providing maternity medical records that made this research  ... 
doi:10.22146/ijeis.35817 fatcat:ejucrnss25bv7oyewqoybxtpym

Biomarkers of Exposure to Phthalate Mixtures and Adverse Birth Outcomes in a Puerto Rico Birth Cohort

Amber L. Cathey, Deborah J. Watkins, Zaira Y. Rosario, Carmen Vélez, Bhramar Mukherjee, Akram N. Alshawabkeh, José F. Cordero, John D. Meeker
2022 Environmental Health Perspectives  
Pregnancy outcomes including preterm birth (PTB), spontaneous PTB, small and large for gestational age (SGA, LGA), birth weight z-score, and gestational age at delivery were abstracted from medical records  ...  Environmental risk scores (ERS) were calculated as a weighted linear combination of the phthalates from ridge regression and adaptive elastic net, which are variable selection methods to handle correlated  ...  D.J.W. and Z.Y.R. contributed to acquisition of data. B.M. assisted with interpretation of data. All authors provided substantial revisions and gave approval for the final version to be published.  ... 
doi:10.1289/ehp8990 pmid:35333099 pmcid:PMC8953418 fatcat:xel75v4lwrhulit2p6ja5taojq

Spontaneous preterm birth prediction using convolutional neural networks [article]

Tomasz Włodarczyk, Szymon Płotka, Przemysław Rokita, Nicole Sochacki-Wójcicka, Jakub Wójcicki, Michał Lipa, Tomasz Trzciński
2020 arXiv   pre-print
Our method obtained better results in the prediction of preterm birth based on transvaginal ultrasound images compared to state-of-the-art methods.  ...  Our method efficiently segments different types of cervixes in transvaginal ultrasound images while simultaneously predicting a preterm birth based on extracted image features without human oversight.  ...  To the best of our knowledge, this is the best result of a segmentation and classification method for spontaneous preterm birth prediction using transvaginal ultrasound images.  ... 
arXiv:2008.07000v2 fatcat:ctkjqfuvzfa2tj3k6d2rytzjsu

Stress and Metabolomics for Prediction of Spontaneous Preterm Birth: A Prospective Nested Case-Control Study in a Tertiary Hospital

Dongni Huang, Zheng Liu, Xiyao Liu, Yuxiang Bai, Mengshi Wu, Xin Luo, Hongbo Qi
2021 Frontiers in Pediatrics  
Spontaneous preterm birth (sPTB) is the leading cause of infant morbidity and mortality worldwide. Deficiency of effective predict methods is an urgent problem that needs to be solved.  ...  In conclusion, this study establishes a prediction model of sPTB with five variables, which may predict sPTB more accurately and sensitively in the second trimester.  ...  Only Lasso was used for feature selection, and then a supporting vector machine prediction model was established using radial basis function (RBF), polynomial, and Sigmoid kernel function.  ... 
doi:10.3389/fped.2021.670382 pmid:34557457 pmcid:PMC8452860 fatcat:h4ytnxwpmvdfzdjv43jsow63ny

Improving preterm newborn identification in low-resource settings with machine learning

Katelyn J. Rittenhouse, Bellington Vwalika, Alexander Keil, Jennifer Winston, Marie Stoner, Joan T. Price, Monica Kapasa, Mulaya Mubambe, Vanilla Banda, Whyson Muunga, Jeffrey S. A. Stringer, Chelsea Dobbins
2019 PLoS ONE  
Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs).  ...  Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation  ...  preterm birth prediction models.  ... 
doi:10.1371/journal.pone.0198919 pmid:30811399 pmcid:PMC6392324 fatcat:3sjb4viqqjdp3a34kalcpfnq7m

Machine Learning Methods for Neonatal Mortality and Morbidity Classification

Joel Jaskari, Janne Myllarinen, Markus Leskinen, Ali Bahrami Rad, Jaakko Hollmen, Sture Andersson, Simo Sarkka
2020 IEEE Access  
Preterm birth is the leading cause of mortality in children under the age of five. In particular, low birth weight and low gestational age are associated with an increased risk of mortality.  ...  In this work, we use machine learning for prediction of neonatal mortality as well as neonatal morbidities of bronchopulmonary dysplasia, necrotizing enterocolitis, and retinopathy of prematurity, among  ...  In addition, the gestational age and birth weight were used as inputs to the model.  ... 
doi:10.1109/access.2020.3006710 fatcat:4j7arnhll5cg3fodd462pndqdi

EHG-Based Preterm Delivery Prediction Algorithm Driven by Transfer Learning [chapter]

Yanjun Deng, Yefei Zhang, Shenguan Wu, Lihuan Shao, Xiaohong Zhang
2021 Advances in Transdisciplinary Engineering  
Therefore, it is particularly important to effectively monitor the uterine contraction of perinatal pregnant women, and to make effective prediction and timely treatment for the possibility of preterm  ...  Electromyography (EHG) signal, an important measurement to predict preterm delivery in clinical practice, shows obvious consistency and correlation with the frequency and intensity of uterine contraction  ...  Hence, the assisted prediction of preterm based on EHG signal has gradually become the most important method for diagnosing preterm delivery.  ... 
doi:10.3233/atde210243 fatcat:65ojben6rbaf5k6y5rapxxjrjy

Prediction of neonatal deaths in NICUs: development and validation of machine learning models

Abbas Sheikhtaheri, Mohammad Reza Zarkesh, Raheleh Moradi, Farzaneh Kermani
2021 BMC Medical Informatics and Decision Making  
The aim of this study was to present a neonatal death risk prediction model using machine learning techniques. Methods This study was conducted in Tehran, Iran in two phases.  ...  The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models.  ...  We appreciate "Maternal, Fetal and Neonatal Research Center", Tehran University of Medical Sciences, Tehran, Iran to provide data for this study. 1  ... 
doi:10.1186/s12911-021-01497-8 pmid:33874944 fatcat:qtay4ifpenfv3ahfhb6apseni4

Automatic Abnormal Electroencephalograms Detection of Preterm Infants

Daniel Schang, Pierre Chauvet, Sylvie Nguyen The Tich, Bassam Daya, Nisrine Jrad, Marc Gibaud
2018 Journal of Data Analysis and Information Processing  
An accurate detection of abnormal EEG for preterm infants is feasible.  ...  Many preterm infants suffer from neural disorders caused by early birth complications. The detection of children with neurological risk is an important challenge.  ...  Gelfi and R. Woodward for their help in the improvement of the quality of this paper. Conflicts of Interest The authors declare no conflicts of interest.  ... 
doi:10.4236/jdaip.2018.64009 fatcat:m5nkkfj4mffbhkxcin6qcgjeuq

Improving preterm newborn identification in low-resource settings with machine learning [article]

Katelyn Joy Rittenhouse, Bellington Vwalika, Alex Keil, Jen Winston, Marie Stoner, Monica Kapasa, Joan T Price, Mulaya Mubambe, Vanilla Banda, Whyson Munga, Jeffrey Stringer
2018 bioRxiv   pre-print
preterm birth prediction models.  ...  For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796.  ...  Future work to assess novel GA and preterm newborn prediction models using machine 268 learning techniques should include methods to impute missing data.  ... 
doi:10.1101/334904 fatcat:lhr64e6wgfcxhdd74ftvaydnny

Evidence for the Placenta-Brain Axis: Multi-Omic Kernel Aggregation Predicts Intellectual and Social Impairment in Children Born Extremely Preterm [article]

Hudson P Santos, Arjun Bhattacharya, Robert M. Joseph, Lisa Smeester, Karl CK Kuban, Carmen J Marsit, T. Michael O'Shea, Rebecca C Fry
2020 bioRxiv   pre-print
These molecular features were then integrated for a predictive analysis of IQ and SRS outcomes using kernel aggregation regression.  ...  Children born extremely preterm are at heightened risk for intellectual and social impairment.  ...  preterm and term birth: What can the placenta tell us?  ... 
doi:10.1101/2020.07.19.211029 fatcat:nkdhnzggfbdwpo7tr5hlau2cpm

Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances [chapter]

Colin J. Brown, Steven P. Miller, Brian G. Booth, Kenneth J. Poskitt, Vann Chau, Anne R. Synnes, Jill G. Zwicker, Ruth E. Grunau, Ghassan Hamarneh
2015 Lecture Notes in Computer Science  
Our method is tested on a dataset of 168 DTIs of 115 very preterm infants, scanned between 27 and 45 weeks post-menstrual age.  ...  We propose a method to identify preterm infants at highest risk of adverse motor function (identified at 18 months of age) using connectome features from a diffusion tensor image (DTI) acquired shortly  ...  We thank NSERC, CIHR, NeuroDevNet and the Michael Smith Foundation for Health Research for their financial support.  ... 
doi:10.1007/978-3-319-24553-9_9 fatcat:snhntnocmjcwrfvshlsm3skoba

Enhancing care strategies for preterm pregnancies by using a prediction machine to aid clinical care decisions

Ejay Nsugbe, Olusayo Obajemu, Oluwarotimi William Samuel, Ibrahim Sanusi
2021 Machine Learning with Applications  
/Term states, and also predict an associated delivery imminency for the pregnant patient.  ...  A B S T R A C T Preterm births are one of the main causes of death in children under the age of 5; they carry financial implications to the economy and cause exceptional psychological distress to mothers  ...  Acknowledgements The authors would like to thank Dr Michael Provost for providing thoughts and feedback on the systemic interaction of the proposed solution, Dr Aize Okojie for raising the awareness of  ... 
doi:10.1016/j.mlwa.2021.100110 fatcat:zv6lsubtvza3llvpqrvfqrh7ce

Machine Learning Methods for Preterm Birth Prediction: A Review

Tomasz Włodarczyk, Szymon Płotka, Tomasz Szczepański, Przemysław Rokita, Nicole Sochacki-Wójcicka, Jakub Wójcicki, Michał Lipa, Tomasz Trzciński
2021 Electronics  
In this work, we present a critical appraisal of popular methods that have employed machine learning methods for preterm birth prediction.  ...  Approximately 30% of preterm births are not correctly predicted due to the complexity of this process and its subjective assessment.  ...  Using machine learning methods to create a prediction model can allow for this foresight.  ... 
doi:10.3390/electronics10050586 doaj:40d0cd7fe240444a90fd6efcd5c4c7d8 fatcat:ge7qmyowwffchbche7beh77rfe
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