If premature rupture of fetal membranes (PROM) can be forecasted, doctors can formulate individualized medical treatment plans and optimize the utilization efficiency of unevenly distributed medical resources in China to lower the odds of PROM, preterm birth, and neonatal mortality. We collected the medical records of 20,392 dyads of mothers and term-birth neonates who had received prenatal care services from January 1, 2014 to December 31, 2019 in Hangzhou, Zhejiang province, East China. According to participants' home and working addresses, maternal exposure to air pollution and meteorological conditions was estimated. Deep learning was used to predict the odds of PROM occurrence. The efficiency of Large Language Model-DeepSeek was tested in healthcare settings. Of 32 clinical covariates have been identified to be statistically significantly associated with PROM, 25 variables-7 positively and 18 negatively linked to PROM-can be detected at least one week before PROM or delivery. Using the Bonferroni correction as a stricter classification tool, 10 out of 32 clinical covariates were statistically associated with PROM. Air pollution exposure and meteorological conditions that were associated with PROM were identified. Based on these findings, approximately 86.1% of PROM cases can be forecasted using deep learning. Thus, individualized treatment can be crafted and vital medical resources can be allocated in advance. DeepSeek can facilitate healthcare processes and optimization of medical resources, showing its uniformity, thoroughness, and robustness. However, the improvement of prediction accuracy for PROM was accompanied by increasing false-positive cases, which is a paradox that needs to be solved.
Yang et al. (Mon,) studied this question.