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A Fuzzy Expert System to Predict the Risk of Postpartum Hemorrhage

Yussriya Hanaa Doomah, Song-Yuan Xu, Li-Xia Cao, Sheng-Lian Liang, Gloria Francisca Nuer-Allornuvor, Xiao-Yan Ying.


Introduction: The American College of Obstetricians and Gynecologists (ACOG) defines postpartum hemorrhage (PPH) as a blood loss of >500mL following vaginal delivery or >1000mL following cesarean section. PPH is widely recognized as a common cause of maternal death. However, there is currently no effective method to predict its risk of occurrence. Aim: To develop a fuzzy expert system to predict the risk of developing PPH and to evaluate its performance in the clinical setting. Methods: This system was developed using MATLAB software. Mamdani inference was used to simulate reasoning of experts in the field. To evaluate the performance of the system, a dataset of 1705 patients admitted at the Labor and Delivery ward of The Second Affiliated Hospital of Nanjing Medical University from 2017-10 to 2018-04, was considered. Results: The Negative Predictive value (NPV), Positive Predictive value PPV), Specificity and Sensitivity were calculated and were 99.72%, 18.50%, 87.48% and 92.16% respectively. Conclusions: Our findings suggest that the fuzzy expert system can be used to predict PPH in clinical settings and thus decrease maternal mortality rate due to hemorrhage.

Key words: Postpartum hemorrhage, maternal death, uterine inertia, retained placenta.

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