Machine learning modeling for preterm birth prediction using health record: A systematic review (Preprint)

Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016-2019

BACKGROUND

A birth interval of less than 33 months was considered short, and in low- income countries like Ethiopia, a short birth interval is the primary cause of approximately 822 maternal deaths every day. Due to that this study aimed to predict short birth interval and associated factors among women (15-49) in Ethiopia using ensemble learning algorithms.

METHODS

A secondary data analysis of Ethiopian demographic health servey from 2016 to 2019 was performed. a total of weighted sample of 12,573 women in the reproductive age group was included in this study. Data have been extracted and processed with Stata version 17. The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. An ensemble Machin learning algorithm using different classification models were implemented. All analysis and calculation were performed using Python 3 programming language in Jupyter Notebook using imblearn, sklearn, and xgboost pakages.

RESULTS

Random forest demonstrated the best performance with an accuracy 97.84%, recall of 99.70%, F1-score of 97.81%, 98.95% precision on test data and AUC (98%). Region, residency, age of women, sex of child, respondent education, distance health facility, husband education and religion were top predicting factors of short birth interval among women in Ethiopia.

CONCLUSION

Random forest was best predictive models with improved performance. "The most significant features that contribute to the accuracy of the top-performing models, notably the Random Forest should be highlighted because they outperformed the other model in the analysis.In general, ensemble learning algorithms can accurately predict short birth interval status, making them potentially useful as decision-support tools for the pertinent stakeholders.

Predicting preterm birth using electronic medical records from multiple prenatal visits

This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation on data from 8,830 women in the Nulliparous Pregnancy Outcomes Study: New Mothers-to-Be (nuMoM2b) dataset at three prenatal visits: 6 0 - 13 6 , 16 0 - 21 6 , and 22 0 - 29 6 weeks of gestational age (GA). The models' performance, assessed using Area Under the Curve (AUC), sensitivity, specificity, and accuracy, consistently improved with the incorporation of data from later prenatal visits. AUC scores increased from 0.6161 in the first visit to 0.7087 in the third visit, while sensitivity and specificity also showed notable improvements. The addition of ultrasound measurements, such as cervical length and Pulsatility Index, substantially enhanced the models' predictive ability. Notably, the model achieved a sensitivity of 0.8254 and 0.9295 for predicting very preterm and extreme preterm births, respectively, at the third prenatal visit. These findings highlight the importance of ultrasound measurements and suggest that incorporating machine learning-based risk assessment and routine late-pregnancy ultrasounds into prenatal care could improve maternal and neonatal outcomes by enabling timely interventions for high-risk women.

Clinical and dental predictors of preterm birth using machine learning methods: the MOHEPI study

Preterm birth (PTB) is one of the most common and serious complications of pregnancy, leading to mortality and severe morbidities that can impact lifelong health. PTB could be associated with various maternal medical condition and dental status including periodontitis. The purpose of this study was to identify major predictors of PTB among clinical and dental variables using machine learning methods. Prospective cohort data were obtained from 60 women who delivered singleton births via cesarean section (30 PTB, 30 full-term birth [FTB]). Dependent variables were PTB and spontaneous PTB (SPTB). 15 independent variables (10 clinical and 5 dental factors) were selected for inclusion in the machine learning analysis. Random forest (RF) variable importance was used to identify the major predictors of PTB and SPTB. Shapley additive explanation (SHAP) values were calculated to analyze the directions of the associations between the predictors and PTB/SPTB. Major predictors of PTB identified by RF variable importance included pre-pregnancy body mass index (BMI), modified gingival index (MGI), preeclampsia, decayed missing filled teeth (DMFT) index, and maternal age as in top five rankings. SHAP values revealed positive correlations between PTB/SPTB and its major predictors such as premature rupture of the membranes, pre-pregnancy BMI, maternal age, and MGI. The positive correlations between these predictors and PTB emphasize the need for integrated medical and dental care during pregnancy. Future research should focus on validating these predictors in larger populations and exploring interventions to mitigate these risk factors.

Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus

OBJECTIVES

This study aims to develop and validate a prediction model for preterm birth in women with gestational diabetes mellitus (GDM).

DESIGN

We conducted a retrospective study on women with GDM who gave birth at the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, between November 2017 and July 2021. We divided 1879 patients into a development set (n = 1346) and a validation set (n = 533). The development set was used to construct the prediction model for preterm birth using the stepwise logistic regression model. A nomogram and a web calculator were established based on the model. Discrimination and calibration were assessed in both sets.

PATIENTS AND MEASUREMENTS

Patients were women with GDM. Data were collected from medical records. GDM was diagnosed with 75-g oral glucose tolerance test during 24-28 gestational weeks. Preterm birth was definied as gestational age at birth <37 weeks.

RESULTS

The incidence of preterm birth was 9.4%. The predictive model included age, assisted reproductive technology, hypertensive disorders of pregnancy, reproductive system inflammation, intrahepatic cholestasis of pregnancy, high-density lipoprotein, homocysteine, and fasting blood glucose of 75-g oral glucose tolerance test. The area under the receiver operating characteristic curve for the development and validation sets was 0.722 and 0.632, respectively. The model has been adequately calibrated using a calibration curve and the Hosmer-Lemeshow test, demonstrating a correlation between the predicted and observed risk.

CONCLUSION

This study presents a novel, validated risk model for preterm birth in pregnant women with GDM, providing an individualized risk estimation using clinical risk factors in the third trimester of pregnancy.

Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12-15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms

We aimed to develop, train, and validate machine learning models for predicting preterm birth (<37 weeks' gestation) in singleton pregnancies at different gestational intervals. Models were developed based on complete data from 22,603 singleton pregnancies from a prospective population-based cohort study that was conducted in 51 midwifery clinics and hospitals in Wenzhou City of China between 2014 and 2016. We applied Catboost, Random Forest, Stacked Model, Deep Neural Networks (DNN), and Support Vector Machine (SVM) algorithms, as well as logistic regression, to conduct feature selection and predictive modeling. Feature selection was implemented based on permutation-based feature importance lists derived from the machine learning models including all features, using a balanced training data set. To develop prediction models, the top 10%, 25%, and 50% most important predictive features were selected. Prediction models were developed with the training data set with 5-fold cross-validation for internal validation. Model performance was assessed using area under the receiver operating curve (AUC) values. The CatBoost-based prediction model after 26 weeks' gestation performed best with an AUC value of 0.70 (0.67, 0.73), accuracy of 0.81, sensitivity of 0.47, and specificity of 0.83. Number of antenatal care visits before 24 weeks' gestation, aspartate aminotransferase level at registration, symphysis fundal height, maternal weight, abdominal circumference, and blood pressure emerged as strong predictors after 26 completed weeks. The application of machine learning on pregnancy surveillance data is a promising approach to predict preterm birth and we identified several modifiable antenatal predictors.

Establishment of a model for predicting preterm birth based on the machine learning algorithm

BACKGROUND

The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future.

METHODS

This was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis. In this study, a logical regression model, decision tree, Naive Bayes, support vector machine, and AdaBoost are used to construct the prediction model. Accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified.

RESULTS

A total of 5411 participants were included and were used for model construction. AdaBoost model has the best prediction ability among the five models. The accuracy of the model for the prediction of "non-preterm birth" was the highest, reaching 100%, and that of "preterm birth" was 72.73%.

CONCLUSIONS

By constructing a preterm birth prediction model based on electronic health records, we believe that machine algorithms have great potential for preterm birth identification. However, more relevant studies are needed before its application in the clinic.

An idiosyncratic MIMBO-NBRF based automated system for child birth mode prediction

Predicting the mode of child birth is still remains one of the most complex and challenging tasks in ancient times. Also, there is no such strong methodologies are developed in the conventional works for birth mode prediction. Therefore, the proposed work objects to develop a novel and distinct optimization based machine learning technique for creating the child birth mode prediction system. This framework includes the modules of data imputation, feature selection, classification, and prediction. Initially, the data imputation process is performed to improve the quality of dataset by normalizing the attributes and filling the missed fields. Then, the Multivariate Intensified Mine Blast Optimization (MIMBO) technique is implemented to choose the best set of features by estimating the optimal function. After that, an integrated Naïve Bayes - Random Forest (NBRF) technique is developed by incorporating the functions of conventional NB and RF techniques. The novel contribution of this technique, a Bird Mating (BM) optimization technique is used in NBRF classifier for estimating the likelihood parameter to generate the Bayesian rules. The main idea of this paper is to develop a simple as well as efficient automated system with the use of hybrid machine learning model for predicting the mode of child birth. For this purpose, advanced algorithms such as MIMBO based feature selection, and NBRF based classification are implemented in this work. Due to the inclusion of MIMBO and BM optimization techniques, the performance of classifier is greatly improved with low computational burden and increased prediction accuracy. Moreover, the combination of proposed MIMBO-NBRF technique outperforms the existing child birth prediction methods with superior results in terms of average accuracy up to 99 %. In addition, some other parameters are also estimated and compared with the existing techniques for proving the overall superiority of the proposed framework.

Challenges and Opportunities for Data Science in Women's Health

The intersection of women's health and data science is a field of research that has historically trailed other fields, but more recently it has gained momentum. This growth is being driven not only by new investigators who are moving into this area but also by the significant opportunities that have emerged in new methodologies, resources, and technologies in data science. Here, we describe some of the resources and methods being used by women's health researchers today to meet challenges in biomedical data science. We also describe the opportunities and limitations of applying these approaches to advance women's health outcomes and the future of the field, with emphasis on repurposing existing methodologies for women's health.

Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review

OBJECTIVES

To identify ML tools in hospital settings and how they were implemented to inform decision-making for patient care through a scoping review. We investigated the following research questions: What ML interventions have been used to inform decision-making for patient care in hospital settings? What strategies have been used to implement these ML interventions?

DESIGN

A scoping review was undertaken. MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (CDSR) were searched from 2009 until June 2021. Two reviewers screened titles and abstracts, full-text articles, and charted data independently. Conflicts were resolved by another reviewer. Data were summarised descriptively using simple content analysis.

SETTING

Hospital setting.

PARTICIPANT

Any type of clinician caring for any type of patient.

INTERVENTION

Machine learning tools used by clinicians to inform decision-making for patient care, such as AI-based computerised decision support systems or "'model-based'" decision support systems.

PRIMARY AND SECONDARY OUTCOME MEASURES

Patient and study characteristics, as well as intervention characteristics including the type of machine learning tool, implementation strategies, target population. Equity issues were examined with PROGRESS-PLUS criteria.

RESULTS

After screening 17 386 citations and 3474 full-text articles, 20 unique studies and 1 companion report were included. The included articles totalled 82 656 patients and 915 clinicians. Seven studies reported gender and four studies reported PROGRESS-PLUS criteria (race, health insurance, rural/urban). Common implementation strategies for the tools were clinician reminders that integrated ML predictions (44.4%), facilitated relay of clinical information (17.8%) and staff education (15.6%). Common barriers to successful implementation of ML tools were time (11.1%) and reliability (11.1%), and common facilitators were time/efficiency (13.6%) and perceived usefulness (13.6%).

CONCLUSIONS

We found limited evidence related to the implementation of ML tools to assist clinicians with patient healthcare decisions in hospital settings. Future research should examine other approaches to integrating ML into hospital clinician decisions related to patient care, and report on PROGRESS-PLUS items.

FUNDING

Canadian Institutes of Health Research (CIHR) Foundation grant awarded to SES and the CIHR Strategy for Patient Oriented-Research Initiative (GSR-154442). SCOPING REVIEW REGISTRATION: https://osf.io/e2mna.