相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest
Jin Peng et al.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2020)
An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets
Gyorgy Kovacs
APPLIED SOFT COMPUTING (2019)
Smote-variants: A python implementation of 85 minority oversampling techniques
Gyorgy Kovacs
NEUROCOMPUTING (2019)
Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation
Linda J. E. Meertens et al.
ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA (2018)
Detection of preterm labor by partitioning and clustering the EHG signal
Mehdi Shahrdad et al.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2018)
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
Alberto Fernandez et al.
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2018)
Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches
Miriam Seoane Santos et al.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2018)
A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis
Mosabber U. Ahmed et al.
ENTROPY (2017)
Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records
Nafissa Sadi-Ahmed et al.
JOURNAL OF MEDICAL SYSTEMS (2017)
Can stress biomarkers predict preterm birth in women with threatened preterm labor?
Ana Garcia-Blanco et al.
PSYCHONEUROENDOCRINOLOGY (2017)
The QUiPP App: a safe alternative to a treat-all strategy for threatened preterm labor
H. A. Watson et al.
ULTRASOUND IN OBSTETRICS & GYNECOLOGY (2017)
Timing of delivery in a high-risk obstetric population: a clinical prediction model
Dane A. De Silva et al.
BMC PREGNANCY AND CHILDBIRTH (2017)
Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals
U. Rajendra Acharya et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2017)
Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the Sustainable Development Goals
Li Liu et al.
LANCET (2016)
Advanced artificial neural network classification for detecting preterm births using EHG records
Paul Fergus et al.
NEUROCOMPUTING (2016)
Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models
Rok Blagus et al.
BMC BIOINFORMATICS (2015)
Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women
A. J. Hussain et al.
NEUROCOMPUTING (2015)
Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals
Peng Ren et al.
PLOS ONE (2015)
NEATER: Filtering of Over-Sampled Data Using Non-Cooperative Game Theory
B. A. Almogahed et al.
2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) (2014)
Monitoring uterine activity during labor: a comparison of 3 methods
Tammy Y. Euliano et al.
AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY (2013)
Prediction of Preterm Deliveries from EHG Signals Using Machine Learning
Paul Fergus et al.
PLOS ONE (2013)
LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data
Munehiro Nakamura et al.
BIODATA MINING (2013)
Obesity in Pregnancy
Gregory A. L. Davies et al.
JOURNAL OF OBSTETRICS AND GYNAECOLOGY CANADA (2010)
Learning from Imbalanced Data
Haibo He et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2009)
A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups
G. Fele-Zorz et al.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2008)
Monitoring contractions in obese parturients - Electrohysterography compared with traditional monitoring
Tammy Y. Euliano et al.
OBSTETRICS AND GYNECOLOGY (2007)
PhysioBank, PhysioToolkit, and PhysioNet - Components of a new research resource for complex physiologic signals
AL Goldberger et al.
CIRCULATION (2000)