The ability of pregnant women to detect early of a high risk pregnancy is still below the average which is one of the causes of complications that can endanger the wellbeing of the mother and fetus. The purpose of this study is to determine the relationship between factors of parity, knowledge and exposure to information on the independence of mothers in early detection of the risk of pregnancy. This type of analytic observational research uses a cross sectional design. A total sample of 125 pregnant women was chosen by “Stratified Random Sampling” technique. The results of Multiple Logistic Regression Analysis show that the knowledge variable Exp (B) 6.657 is a significant variable, the exposure to danger information variable Exp (B) 7.657 is a significant variable and the parity variable Exp (B) 8.060 is also a significant variable. Midwives and health workers further increase counseling so that pregnant women and families can receive more information about the danger signs of pregnancy, especially the high risk of pregnancy and being able to do early detection.

Objective: To evaluate the maternal-neonatal outcome in magnesium (Mg)-intoxicated women with preeclampsia with severe features (PESF) treated with magnesium sulfate (MgSO 4 ). Methods: A total of 19 Mg intoxicated PESF women (cases) were compared with 166 PESF women without signs of intoxication (controls). Results: Mg serum levels of cases was higher compared to control group (12.36 ± 3.54 mg/dl versus 2.69 ± 0.83 mg/dl). 3 women died and 3 had major maternal morbidity in cases group compared with zero in the control group (P = 0.009). Mg intoxication was also significantly associated with perinatal deaths and low Apgar scores at 1 and 5 minutes. Conclusion: Mg intoxication is associated with a increased risk of maternal and perinatal mortality and morbidity.

Pre-eclampsia still dominates maternal mortality cases in Indonesia. One effort that can be done is to establish early detection of the risk of pre-eclampsia in pregnant women. Automated devices with high accuracy are needed to detect the risk of pre-eclampsia so that the maternal mortality ratio can be reduced. This study aims to design an early detection system for the risk of pre-eclampsia based on artificial neural networks. The system is designed with 11 input parameters in the form of risk factors and output in the form of positive or negative risk of pre-eclampsia. The classification tool used in this study is backpropagation neural network with cross validation scenario at the training stage. The advantage of this system is the weighting of risk factor parameters by obstetric and gynecology specialists so that the results of testing the device show high accuracy. In addition, the device for early detection of pre-eclampsia was also conducted by user acceptance tests for a number of pregnant women.

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