ArticleHypertension research : official journal of the Japanese Society of Hypertension2023
Machine-learning predictive model of pregnancy-induced hypertension in the first trimester.
Article in Hypertension research : official journal of the Japanese Society of Hypertension, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Assisted Reproductive Technology as Predictor for Preeclampsia: A Systematic Review of Current Clinical Prediction Models.Reproductive sciences (Thousand Oaks, Calif.) · 2026Pooled it
- High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection.Scientific reports · 2026Article
- A novel approach to preeclampsia early prediction addressing predictive uncertainty due to missing data in clinical dataset.Scientific reports · 2026Article
- Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.International journal of women's health · 2026Review
- [Nomogram and machine learning models for predicting in-hospital mortality in sepsis patients with deep vein thrombosis].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters.medRxiv : the preprint server for health sciences · 2024Article
- Understanding preeclampsia from cutting edge knowledge-protecting maternal and fetal health.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Review
- Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning-Based Multicohort (RIGIPREV) Study.Journal of medical Internet research · 2024Article
- Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China.Frontiers in endocrinology · 2024Article
- Thyroid-stimulating hormone and total bile acids can predict adverse pregnancy outcome among patients with gestational hypertension.American journal of translational research · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (LASSO logistic regression, random forest, backpropagation neural network, and support vector machines) to predict the occurrence of PIH in a prospective cohort. Candidate features for predicting the occurrence of middle and late PIH were acquired using a LASSO algorithm. The performance of predictive models was assessed using receiver operating characteristic analysis. Finally, a nomogram was established with the model scores, age, and nulliparity. Calibration, clinical usefulness, and internal validation were used to assess the performance of the nomogram. In the training set (2258 pregnant women), eleven candidate factors in the first trimester were significantly associated with the occurrence of PIH (P < 0.001 in the training set). Four models showed AUCs from 0.780 to 0.816 in the training set. For the validation set (939 pregnant women), AUCs varied from 0.516 to 0.795. The nomogram showed good discrimination, with an AUC of 0.847 (95% CI: 0.805-0.889) in the training set and 0.753 (95% CI: 0.653-0.853) in the validation set. Decision curve analysis suggested that the model was clinically useful. The model developed using LASSO logistic regression achieved the best performance in predicting the occurrence of PIH. The derived nomogram, which incorporates the model score and maternal risk factors, can be used to predict PIH in clinical practice. We develop a model with good performance for clinical prediction of PIH in the first trimester.
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