2 results about "Gestational hypertension" patented technology

This invention relates to a group of genes associated with gestational hypertension, a method for predicting gestational hypertension in early pregnancy using these genes, and related applications. Specifically, this invention obtains transcript start site feature values ​​from at least one of the following genes from the cfDNA sample of a pregnant woman: LOC124902572, EEF1A1P16, LOC105369767, GOLM2, CDRT7, MEIS3, and LOC105376108. These transcript start site feature values ​​are then input into a prediction model, and the output results are used to determine the risk of gestational hypertension. The number of sequences in the transcript start site region after correction based on the transcript start site feature values ​​is also considered. The prediction model obtained by this invention can effectively and robustly predict the risk of gestational hypertension (including preeclampsia) in early pregnancy, assisting doctors in early pregnancy intervention and reducing the risk of gestational hypertension.

The application discloses a kind of classification model generation method and device for predicting gestational hypertension, and the method one specific embodiment includes: carrying label fundus image as first training sample, obtain first sample dataset;Wherein, label includes normal label and gestational hypertension label;Gestational hypertension label includes: early gestational hypertension label, mild preeclampsia label and severe preeclampsia label;Based on first sample dataset, model training is generated two-classification model;And according to the gestational hypertension classification result obtained by second classification model to first sample dataset prediction, obtain second sample dataset;Then based on second sample dataset, model training is generated three-classification model;Finally, based on two-classification model and three-classification model, gestational hypertension classification model is generated.Therefore, the difficulty of model to the recognition of gestational hypertension is effectively reduced, and the accuracy of gestational hypertension classification is improved.