Ensemble Learning Based Postpartum Hemorrhage Diagnosis for 5G Remote Healthcare
Yawei Zhang, Xin Wang, Ningyu Han, Rong Zhao
Abstract
The fifth-generation (5G) communications enables various promising applications that was once impossible, e.g. remote healthcare with the help of fast and reliably delivery of medical data. Post-partum hemorrhage (PPH) refers to the massive blood loss after the birthing stage (within 24 hours), i.e. >500ml for the vaginal delivery, and >1000ml for the cesarean section. PPH is by far the most common cause of the mortality rate of pregnant women, as well as a primary cause of current pregnant mortality in China. Despite the great potential of prediction of PPH, there is currently no effective tool based on the limited raw data from the clinical trials. In the study, we retrospectively study the 3842 vaginal delivery cases in 2017 collected from Beijing Obstetrics and Gynecology Hospital, Capital Medical University. In particular, we obtain the prediction based diagnostic model relying on machine learning, and we adopt the ensemble learning to accomplish this task, by combining the results of various candidate methods. According to the experimental results, the accuracy of correct PPH diagnosis would approach 96.7%; the total disseminated intravascular coagulation (DIC) prediction accuracy approaches 90.3%. In this regard, we may conclude the proposed model based on machine learning would allow us to predict successfully the risk of PPH, and assess the critical level of PPH patient. We anticipate our study results would contribute to the reduction the mortality of pregnant women.