Does “idiopathic” preterm labor resulting in preterm birth exist?
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Predicting the risk of spontaneous premature births using clinical data and machine learning
2022, Informatics in Medicine UnlockedCitation Excerpt :Several clinical studies have identified the following as risk factors that influence sPTB: short cervix, multiple gestations, in vitro fertilization, ethnicity, stressful life events, chronic conditions such as high blood pressure and diabetes, smoking, uterine anomalies, short inter-pregnancy interval, maternal serum proteins, and prior spontaneous preterm birth [9–13]. However, most of these studies have focused on individual associations between these risk factors and sPTB rather understanding how the totality of life experiences and conditions affect sPTB risk as they interact and potentially amplify one another [14–19]. The increasing use of artificial intelligence to identify patterns, learn from experience, and make decisions has motivated recent investigations into preterm birth prediction [2,19–25].
Next generation strategies for preventing preterm birth
2021, Advanced Drug Delivery Reviews