Early prediction of severe maternal morbidity using machine learning techniques

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. © Springer International Publishing AG 2016.

• Fecha de publicación: 2016
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/8999
• Acceso en línea: https://hdl.handle.net/20.500.12585/8999
• Palabra clave: Logistic regression; Machine learning; Severe maternal morbidity; Artificial intelligence; Diseases; Learning algorithms; Obstetrics; Early prediction; Learning models; Logistic regressions; Machine learning techniques; Maternal morbidity; Methods and materials; Pregnant woman; Public health issues; Learning systems
• Rights: restrictedAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/