Machine learning opportunities to predict obstetric haemorrhages

Abstract

Obstetric hemorrhages (OH) are the main preventable cause of morbidity, mortality and cases of "near miss" among obstetric complications worldwide. Early preventive measures based on the OH prediction allow to profoundly reduce the rate of female mortality and morbidity as well as prevent the economic costs of patient intensive care, blood transfusion, surgical treatment and long-term hospitalization. Postpartum haemorrhage (PPH) is the most frequent obstetric haemorrhage determined by one of the four causes: a uterine tonus disorder, maternal birth trauma, retention of placenta parts and blood-clotting disorder. There is still a need for the continued search for an accurate and reliable prediction method despite multiple attempts to develop an effective system for predicting OH. The solution to this may be reasonably considered an innovative method such as artificial intelligence (AI) including computer technologies capable of obtaining conclusions similar to human thinking. One of the particular AI variants is presented by machine learning (ML), which develops accurate predictive models using computer analysis. Machine learning is based on computer algorithms, the most common among them in medicine are the decision tree (DT), naive Bayes classifier (NBC), random forest (RF), support vector machine (SVM), artificial neural network (ANNs), deep neural network (DNN) or deep learning (DL) and convolutional neural network (CNN). Here, we review the main stages of ML, the principles of algorithms action, and the prospects for using AI to predict OH in real-life clinical practice.