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Predicting Preterm Birth in Maternity Care by Means of Data Mining

机译:通过数据挖掘预测产妇保健中的早产

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Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients' observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.
机译:在全球范围内,大约9%的孩子出生时的劳动时间少于37周,这给早产儿带来了风险,早产儿不准备发展许多在出生后不久就开始的基本功能。为了确保产科医生能够及时地监控这些风险怀孕以避免这些问题,本研究引入了数据挖掘(DM)模型,使用来自3376名入院患者(女性)的数据来预测真实环境中的早产在波尔图的Centro Hospitalar的产妇和围产期护理部门工作。开发了一种预测早产的灵敏指标,以帮助医生进行有关患者观察的决策过程。可能获得有希望的结果,灵敏度和特异性值分别达到96%和98%。

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