Prognostic prediction of prelabor rupture of membrane (PROM) lacks of sample size and external validation. We compared a statistical model, machine learning algorithms, and a deep-insight visible neural network (DI-VNN) for PROM and estimating the time of delivery. We selected visits, including PROM (n=23,791/170,730), retrospectively from a nationwide health insurance dataset. DI-VNN achieved the best prediction (area under receiver operating characteristics curve [AUROC] 0.73, 95% CI 0.72 to 0.75). Meanwhile, random forest using principal components achieved the best estimation with root mean squared errors ± 2.2 and 2.6 weeks respectively for the predicted event and nonevent. DI-VNN outperformed previous models by an external validation set, including one using a biomarker (AUROC 0.641; n=1,177). We deployed our models as a web application requiring diagnosis/procedure codes and dates. In conclusion, our models may be used solely in low-resource settings or as a preliminary model to reduce a specific test requiring high-resource setting.
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