Machine learning approach to predict postpartum haemorrhage: a systematic review protocol

Boujarzadeh, Banafsheh; Ranjbar, Amene; Banihashemi, Farzaneh; Mehrnoush, Vahid; Darsareh, Fatemeh; Saffari, Mozhgan

doi:10.1136/bmjopen-2022-067661

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…Globally, pregnancy-related fatalities pose a significant concern and lead to the premature loss of around 500,000 women each year [5]. Among these fatalities, approximately one-fourth are linked to PPH [6]. Despite an overall reduction in maternal mortality rates [7], PPH remains a significant threat, affecting 1-5% of deliveries worldwide and standing as the leading cause of maternal morbidity and mortality [2].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models

Holcroft,

Karangwa,

Little

et al. 2024

IJERPH

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Postpartum haemorrhage (PPH) is a significant cause of maternal morbidity and mortality worldwide, particularly in low-resource settings. This study aimed to develop a predictive model for PPH using early risk factors and rank their importance in terms of predictive ability. The dataset was obtained from an observational case–control study in northern Rwanda. Various statistical models and machine learning techniques were evaluated, including logistic regression, logistic regression with elastic-net regularisation, Random Forests, Extremely Randomised Trees, and gradient-boosted trees with XGBoost. The Random Forest model, with an average sensitivity of 80.7%, specificity of 71.3%, and a misclassification rate of 12.19%, outperformed the other models, demonstrating its potential as a reliable tool for predicting PPH. The important predictors identified in this study were haemoglobin level during labour and maternal age. However, there were differences in PPH risk factor importance in different data partitions, highlighting the need for further investigation. These findings contribute to understanding PPH risk factors, highlight the importance of considering different data partitions and implementing cross-validation in predictive modelling, and emphasise the value of identifying the appropriate prediction model for the application. Effective PPH prediction models are essential for improving maternal health outcomes on a global scale. This study provides valuable insights for healthcare providers to develop predictive models for PPH to identify high-risk women and implement targeted interventions.

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Section: Introductionmentioning

confidence: 99%

Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models

Holcroft,

Karangwa,

Little

et al. 2024

IJERPH

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“…Artificial intelligence has been increasingly used in health and medicine in recent years. The use of artificial intelligence in obstetrics and gynaecology has piqued the scientific community’s interest 6 7…”

Section: Introductionmentioning

confidence: 99%

“…The use of artificial intelligence in obstetrics and gynaecology has piqued the scientific community’s interest. 6 7 …”

Section: Introductionmentioning

confidence: 99%

Machine learning models for predicting pre-eclampsia: a systematic review protocol

Ranjbar,

Taeidi,

Mehrnoush

et al. 2023

BMJ Open

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IntroductionPre-eclampsia is one of the most serious clinical problems of pregnancy that contribute significantly to maternal mortality worldwide. This systematic review aims to identify and summarise the predictive factors of pre-eclampsia using machine learning models and evaluate the diagnostic accuracy of machine learning models in predicting pre-eclampsia.Methods and analysisThis review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This search strategy includes the search for published studies from inception to January 2023. Databases include the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus and Google Scholar. Search terms include ‘preeclampsia’ AND ‘artificial intelligence’ OR ‘machine learning’ OR ‘deep learning’. All studies that used machine learning-based analysis for predicting pre-eclampsia in pregnant women will be considered. Non-English articles and those that are unrelated to the topic will be excluded. PROBAST (Prediction model Risk Of Bias ASsessment Tool) will be used to assess the risk of bias and the applicability of each included study.Ethics and disseminationEthical approval is not required, as our review will include published and publicly accessible data. Findings from this review will be disseminated via publication in a peer-review journal.PROSPERO registration numberThis review is registered with PROSPERO (ID: CRD42023432415).

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“…It may aid health researchers in analyzing and visualizing large amounts of complex data and processing results quickly and accurately. As a result, creating a single customized algorithm based on the comparison and evaluation of various techniques can be a proper solution for the current application [ 5 , 6 ]. The application of machine learning has dramatically evolved in the medical field over the past decade [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, creating a single customized algorithm based on the comparison and evaluation of various techniques can be a proper solution for the current application [ 5 , 6 ]. The application of machine learning has dramatically evolved in the medical field over the past decade [ 5 ]. However, in the diagnosis of intrauterine growth restriction, the evidence is limited.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Approach to Predict Intrauterine Growth Restriction

Taeidi,

Ranjbar,

Montazeri

et al. 2023

Cureus

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Introduction: Creating a prediction model incorporating multiple risk factors for intrauterine growth restriction is vital. The current study employed a machine learning model to predict intrauterine growth restriction. Methods: This cross-sectional study was carried out in a tertiary hospital in Bandar Abbas, Iran, from January 2020 to January 2022. Women with singleton pregnancies above the gestational age of 24 weeks who gave birth during the study period were included. Exclusion criteria included multiple pregnancies and fetal anomalies. Four statistical learning algorithms were used to build a predictive model: (1) Decision Tree Classification, (2) Random Forest Classification, (3) Deep Learning, and (4) the Gradient Boost Algorithm. The candidate predictors of intrauterine growth restriction for all models were chosen based on expert opinion and prior observational cohorts. To investigate the performance of each algorithm, some parameters, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and sensitivity, were assessed. Results: Of 8683 women who gave birth during the study period, 712 were recorded as having intrauterine growth restriction, with a frequency of 8.19%. Comparing the performance parameters of different machine learning algorithms showed that among all four machine learning models, Deep Learning had the greatest performance to predict intrauterine growth restriction with an AUROC of 0.91 (95% confidence interval, 0.85-0.97). The importance of the variables revealed that drug addiction, previous history of intrauterine growth restriction, chronic hypertension, preeclampsia, maternal anemia, and COVID-19 were weighted factors in predicting intrauterine growth restriction. Conclusions: A machine learning model can be used to predict intrauterine growth restriction. The Deep Learning model is an accurate algorithm for predicting intrauterine growth restriction.

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Machine learning approach to predict postpartum haemorrhage: a systematic review protocol

Cited by 9 publications

References 15 publications

Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models

Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models

Machine learning models for predicting pre-eclampsia: a systematic review protocol

Machine Learning-Based Approach to Predict Intrauterine Growth Restriction

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