Abstract
Severe Maternal Morbidity is a public health issue. It may occur during pregnancy, delivery, or puerperium due to conditions (hypertensive disorders, hemorrhages, infections and others) that put in risk the women’s or baby’s life. These conditions are really difficult to detect at an early stage. In response to the above, this work proposes using several machine learning techniques, which are considered most relevant in a bio-medical setting, in order to predict the risk level for Severe Maternal Morbidity in patients during pregnancy. The population studied correspond to pregnant women receiving prenatal care and final attention at E.S.E Clínica de Maternidad Rafael Calvo in Cartagena, Colombia. This paper presents the preliminary results of an ongoing project, as well as methods and materials considered for the construction of the learning models.
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Acknowledgements
Special thanks for their cooperation to the High-Performance Computing Laboratory (HPCLab) at Universidad Tecnológica de Bolívar and to research group on maternal safety of Center of research for maternal health, Perinatal and women at E.S.E Clińica de Maternidad Rafael Calvo.
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Rodríguez, E.A., Estrada, F.E., Torres, W.C., Santos, J.C.M. (2016). Early Prediction of Severe Maternal Morbidity Using Machine Learning Techniques. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_22
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