Preterm Birth Prediction: Deriving Stable and Interpretable Rules from High Dimensional Data

Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth is difficult, even for the most experienced clinicians. The most well-designed clinical study thus far reaches a modest sensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a different approach by exploiting databases of normal hospital operations. We aims are twofold: (i) to derive an easy-to-use, interpretable prediction rule with quantified uncertainties, and (ii) to construct accurate classifiers for preterm birth prediction. Our approach is to automatically generate and select from hundreds (if not thousands) of possible predictors using stability-aware techniques. Derived from a large database of 15,814 women, our simplified prediction rule with only 10 items has sensitivity of 62.3% at specificity of 81.5%.Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, C

Hybrid Support Vector Machine to Preterm Birth Prediction

Preterm birth is one of the major contributors to perinatal and neonatal mortality. This issue became important in health research area especially human reproduction both in developed and developing country. In 2015 Indonesia rank fifth as the country with the highest number of premature babies in the world. The ability to reduce the number of preterm birth is to reduce risk factors associated with it. This research will be made the prediction model of preterm birth using hybrid multivariate adaptive regression splines (MARS) and Support Vector Machine (SVM). MARS used to select the attributes which suspected to affect premature babies. The result of this research is prediction model based on hybrid MARS-SVM obtains better performance than the other model

Modern possibilities of preterm birth prediction

Purpose of the study is to improve the method of preterm deliveries onset predicting in pregnant women at 24–34 weeks. Materials and methods. 49 pregnant women with gestational age 24–34 weeks and with diagnosis of “threatening premature deliveries" were comprehensively examined using transvaginal ultrasound. Pregnant women with a shortened cervix ≤25 mm were given a qualitative determination of fetal fibronectin. The average age of pregnant women ranged from 26–34 years and averaged out 31.2 ± 0,6 years. Study results. Clinically significant structural changes in the cervix were only in 21 (42.9%) pregnant women. The average value of the cervix length varied between 16–24 mm and averaged 18.2 ± 0.4 mm. Positive test on fetal fibronectin in vaginal secretions was in 7 (33.3%) of 21 pregnant women with clinically significant structural changes in the cervix. During 10 days premature birth occurred in 3 (42.9%) of 7 pregnant women with clinically significant cervix shortening and a positive test for fetal fibronectin in the gestation period of 32 weeks. Hospitalization in an obstetric hospital was found to be unjustified in 28 (57.1%) cases. Conclusions. Combination of a comprehensive assessment of the cervix state and vaginal fetal fibronectin evaluation in pregnant women with risk of preterm deliveries at the outpatient stage allows to predict the manifestation of preterm birth in critical terms also determine the volume and direction of therapy in obstetric department. Combination of this methods help to prevent unwarranted hospitalization of a pregnant woman in an obstetric hospital and as a result it limits the conduct of glucocorticoid therapy and prevent a prolong stay of a pregnant woman in a hospital and the associated contamination with nosocomial bacteria strains

Data Mining Approach in Preterm Birth Prediction

Data mining refers to the process of discovering patterns in data, typically with the aid of powerful algorithms to automate part of the search. These methods come from the disciplines such as statistics, machine learning, pattern recognition, neural networks and database. In particular this paper reveals out how the problem of preterm birth prediction is approached by a data mining analyst with a background in machine learning. In the health field, data mining applications have been growing considerably as it can be used to directly derive patterns, which are relevant to forecast different risk groups among the patients. Data mining technique such as clustering has not been used to predict preterm birth. Hence this paper made an attempt to identify patterns from the database of the preterm birth patients using clustering

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

[EN] One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR), and by the Generalitat Valenciana (AICO/2019/220)Nieto Del-Amor, F.; Beskhani, R.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.... (2021). Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. Sensors. 21(18):1-17. https://doi.org/10.3390/s21186071S117211

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set