Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth

Abstract

Postpartum hemorrhage is the leading cause of maternal morbidity. Clinical prediction of postpartum hemorrhage remains challenging, particularly in the case of a vaginal birth. We studied machine learning models to predict postpartum hemorrhage. Women who underwent vaginal birth at the Tokyo Women Medical University East Center between 1995 and 2020 were included. We used 11 clinical variables to predict a postpartum hemorrhage defined as a blood loss of > 1000 mL. We constructed five machine learning models and a deep learning model consisting of neural networks with two layers after applying the ensemble learning of five machine learning classifiers, namely, logistic regression, a support vector machine, random forest, boosting trees, and decision tree. For an evaluation of the performance, we applied the area under the curve of the receiver operating characteristic (AUC), the accuracy, false positive rate (FPR) and false negative rate (FNR). The importance of each variable was evaluated through a comparison of the feature importance calculated using a Boosted tree. A total of 9,894 patients who underwent vaginal birth were enrolled in the study, including 188 cases (1.9%) with blood loss of > 1000 mL. The best learning model predicted postpartum hemorrhage with an AUC of 0.708, an accuracy of 0.686, FPR of 0.312, and FNR of 0.398. The analysis of the importance of the variables showed that pregnant gestation of labor, the maternal weight upon admission of labor, and the maternal weight before pregnancy were considered to be weighted factors. Machine learning model can predict postpartum hemorrhage during vaginal delivery. Further research should be conducted to analyze appropriate variables and prepare big data, such as hundreds of thousands of cases.

Design of a postpartum hemorrhage and transfusion risk calculator

“…In order to develop these objectives, we chose the Naïve Bayes algorithm for its ability to provide results based on the sum of probabilities and the learning and improvement with the sum of information, being a statistical strategy different from the one used in other studies interested in achieving similar objectives to this study [ [28] , [29] , [30] ]. or similar in those employing artificial intelligence tools [ 31 , 32 ].…”

Prediction of the Reactivation of Retinopathy of Prematurity After Anti-VEGF Treatment Using Machine Learning in Small Numbers

“…Positive and negative cases are uneven, which limits learning from positive samples. [50] More prospective studies with larger sample sizes should be conducted to further evaluate the model's accuracy, and we will also collect cases of retinal laser photocoagulation and other anti-VEGF treatments. For small numbers, lowshot learning and few-shot learning were developed.…”

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

“…Conventional general programming algorithms produce outputs using the input data and the given rules, whereas AI can produce rules and patterns using the input and output data. The pattern recognition and prediction performance of AI have been demonstrated in multiple realistic tasks 8 9. This systematic review aims to identify PPH predictors using machine learning (ML) approaches that have been reported in previous studies in this field.…”