Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

Guedalia, Joshua; Lipschuetz, Michal; Persky, Michal Novoselsky; Cohen, S. M.; Rottenstreich, Amihai; Unger, Ron; Sompolinsky, Yishai

doi:10.1016/j.ajog.2020.05.025

Cited by 25 publications

(24 citation statements)

References 40 publications

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“…In a similar study, Guedalia et al 38 developed another ML model in order to predict successful vaginal deliveries. Achieving a successful vaginal delivery is a matter of crucial importance, taking into consideration that the health and the development of the child are highly influenced by the quality of delivery 38 .…”

Section: Discussionmentioning

confidence: 99%

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Barbounaki¹,

Vivilaki²

2021

Eur J Midwifery

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INTRODUCTION Machine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics. METHODS A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted. RESULTS Thirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc. CONCLUSIONS This systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.

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Section: Discussionmentioning

confidence: 99%

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Barbounaki¹,

Vivilaki²

2021

Eur J Midwifery

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“…age, parity, smoking status, gestational diabetes) and neonatal characteristics (e.g. gestational age, estimated fetal weight), as described in our previous study 31 (Table S1). Features that are gathered postpartum were excluded from the training of the machine learning model (e.g.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Our previous studies showed that machine learning models are reliable in predicting obstetric outcomes, such as the need for emergency caesarean delivery (CD) 31 or a successful vaginal birth after CD 32 . Building on these studies, in the present study we aimed to use machine learning tools to develop a prediction model based on data available to the clinician before the onset of the second stage of labour, in order to predict adverse neonatal outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, alongside traditional statistical analysis tools, machine learning methods have been applied to analyse enormous and complex data in many scientific fields, [25][26][27] including medicine 28 and obstetrics. [29][30][31][32] These methods can perform tasks such as prediction or classification, relying on identifying patterns in the data rather than receiving explicit instructions regarding the possible associations between features. Another advantage of machine learning is the ability to analyse features that may be nonmonotonic in their association with the outcome, for example, if patient age correlates with disease in a non-linear pattern.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study

Guedalia

Sompolinsky

Persky

et al. 2021

BJOG

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Objective To create a personalised machine learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labour. Design Retrospective Electronic‐Medical‐Record (EMR) ‐based study. Population A cohort of 73 868 singleton, term deliveries that reached the second stage of labour, including 1346 (1.8%) deliveries with SANO. Methods A gradient boosting model was created, analysing 21 million data points from antepartum features (e.g. gravidity and parity) gathered at admission to the delivery unit, and intrapartum data (e.g. cervical dilatation and effacement) gathered during the first stage of labour. Deliveries were allocated to high‐risk and low‐risk groups based on the Youden index to maximise sensitivity and specificity. Main outcome measures SANO was defined as either umbilical cord pH levels ≤7.1 or 1‐minute or 5‐minute Apgar score ≤7. Results The model for prediction of SANO yielded an area under the receiver operating curve (AUC) of 0.761 (95% CI 0.748–0.774). A third of the cohort (33.5%, n = 24 721) were allocated to a high‐risk group for SANO, which captured up to 72.1% of these cases (odds ratio 5.3, 95% CI 4.7–6.0; high‐risk versus low‐risk groups). Conclusions Data acquired throughout the first stage of labour can be used to predict SANO during the second stage of labour using a machine learning model. Stratifying parturients at the beginning of the second stage of labour in a ‘time out’ session, can direct a personalised approach to management of this challenging aspect of labour, as well as improve allocation of staff and resources. Tweetable abstract Personalised prediction score for severe adverse neonatal outcomes in labour using machine learning model.

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Utilizing machine learning to predict unplanned cesarean delivery

Meyer

Weisz

Eilenberg

et al. 2022

Intl J Gynecology & Obste

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Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries

Cited by 25 publications

References 40 publications

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study

Utilizing machine learning to predict unplanned cesarean delivery

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