ArticleBMC medical informatics and decision making2025
Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integration of angiogenic pathway-related proteomics and clinical features via ensemble learning for precision risk stratification of early-onset preeclampsia.European journal of obstetrics & gynecology and reproductive biology: X · 2026Article
- Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.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
- Association of early-pregnancy platelet-to-high-density lipoprotein cholesterol ratio with preeclampsia: a retrospective cohort study.American journal of clinical and experimental immunology · 2026Article
- Artificial Intelligence-Based Prediction of Preeclampsia Using First-Trimester Biomarkers.Cureus · 2025Article
- Early-Onset Versus Late-Onset Preeclampsia in Bogotá, Colombia: Differential Risk Factor Identification and Evaluation Using Traditional Statistics and Machine Learning.Biomedicines · 2025Article
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
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Authors and funding
44 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Preterm PE) were developed using maternal characteristics and laboratory data.
methodsBetween January 2019 and December 2021, we retrospectively recruited 144 EOPE, 363 LOPE, 231 Preterm PE, and 1458 healthy participants from six hospitals. We utilized all available clinical and laboratory data obtained during routine prenatal visits in early pregnancy. The models for EOPE, LOPE, and Preterm PE were created using ensemble machine learning models with patient clinical and laboratory data.
resultsBy comparing laboratory variables between PE patients and healthy controls, we identified 7, 18, 8, 15, 7,29 laboratory markers for EOPE, LOPE, and Preterm PE, severe PE, superimposed PE, first-time PE respectively. The ensemble EOPE and LOPE models incorporating clinical and laboratory predictors outperformed the clinical factor models respectively. The ensemble EOPE model demonstrated good sensitivity (72.22%,95% confidence interval [CI]: 57.59%-86.85%) and specificity (85.25%,95% CI: 80.54%-89.97%) in distinguishing EOPE from controls in early pregnancy. Similarly, the ensemble LOPE model showed good accuracy in differentiating LOPE from healthy participants (sensitivity: 69.57%, 95% CI: 56.27%-82.86%; specificity: 85.25%, 95% CI: 80.54%-89.97%). The prediction scores demonstrated notable positive correlations with blood pressure at admission, while they showed inverse correlations with 24-hour urine protein levels and fetal growth restriction among PE patients. In conclusion, our study identified key laboratory indicators for forecasting PE. The developed models exhibited good predictive capability for assessing preeclampsia risk and severity based on clinical and laboratory data. CLINICAL TRIAL NUMBER: Not applicable.
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