Electronic medical record based machine learning methods for adverse pregnancy outcome prediction

Pregnancy complications put gestational women at risk, especially for those who are over 35, which can seriously threaten the safety of the mother and the fetus. This paper is aimed at detecting comprehensive adverse pregnancy outcomes based on Electronic Medical Records (EMRs) from the obstetrical department. However, EMR data is usually incomplete, imbalanced and high-dimensional with sparsity. Therefore, missing value imputation and data balancing methods were applied to improve the data quality. Also, manual feature selection based on medical prior knowledge and automatic feature selection methods were implemented to extract risk factors and evaluated for classification. The experimental results show that our system is capable of identifying patients at risk, and achieved the best accuracy of 0.8707 and the best recall of 0.7454. Besides, the extracted risk factors offer the opportunity to assist clinical diagnosis and improve labor processing procedures.