ArticleEBioMedicine2020
Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.Current hypertension reports · 2024Pooled it
- Pooled it
- Decoding preeclampsia: A fusion of multi-view machine learning and multi-omics to identify putative inflammation-related mechanisms.Molecular therapy. Nucleic acids · 2026Article
- Extracting Signs and Symptoms of Hypertensive Disorders in Pregnancy from Clinical Notes Using Natural Language Processing.Maternal and child health journal · 2026Observational
- Development of Machine Learning Models to Predict Health Insurance Claim Costs Among Older Indonesians: A Retrospective Predictive Modeling Study.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2026Article
- AI-driven high-risk pregnancy prediction: balancing early detection, anxiety, and discrimination in digital public health.Frontiers in public health · 2026Article
- Indonesian National Health Insurance scheme longitudinal sample data 2015-2020: overview and potential uses for health policy analysis.BMC health services research · 2025Article
- Machine learning predictive system to predict the risk of developing pre-eclampsia.BMJ health & care informatics · 2025Article
- Machine learning-augmented biomarkers in mid-pregnancy Down syndrome screening improve prediction of small-for-gestational-age infants.Orphanet journal of rare diseases · 2025Article
- Application of Proteomics in Maternal and Neonatal Health: Advancements and Future Directions.Proteomics. Clinical applications · 2025Review
- Prediction of hypertension and diabetes in twin pregnancy using machine learning model based on characteristics at first prenatal visit: national registry study.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Article
- Widely accessible prognostication using medical history for fetal growth restriction and small for gestational age in nationwide insured women.Scientific reports · 2025Article
- Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.BMC medical informatics and decision making · 2025Article
- Preeclampsia prediction via machine learning: a systematic literature review.Health systems (Basingstoke, England) · 2025Review
- The Role of Digital Technology in Preventing Anemia Among Pregnant Women: A Scoping Review.International journal of telemedicine and applications · 2025Review
- Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohort.AJOG global reports · 2024Article
- Deep survival analysis for interpretable time-varying prediction of preeclampsia risk.Journal of biomedical informatics · 2024Article
- Prevention of Pregnancy Complications Using a Multimodal Lifestyle, Screening, and Medical Model.Journal of clinical medicine · 2024Review
- Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension.Clinics and practice · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia.
methodsThe BPJS Kesehatan dataset have been preprocessed using a nested case-control design into preeclampsia/eclampsia (n = 3318) and normotensive pregnant women (n = 19,883) from all women with one pregnancy. The dataset provided 95 features consisting of demographic variables and medical histories started from 24 months to event and ended by delivery as the event. Six algorithms were compared by area under the receiver operating characteristics curve (AUROC) with a subgroup analysis by time to the event. We compared our model to similar prediction models from systematically reviewed studies. In addition, we conducted a text mining analysis based on natural language processing techniques to interpret our modeling results.
findingsThe best model consisted of 17 predictors extracted by a random forest algorithm. Nine∼12 months to the event was the period that had the best AUROC in external validation by either geographical (0.88, 95% confidence interval (CI) 0.88-0.89) or temporal split (0.86, 95% CI 0.85-0.86). We compared this model to prediction models in seven studies from 869 records in PUBMED, EMBASE, and SCOPUS. This model outperformed the previous models in terms of the precision, sensitivity, and specificity in all validation sets.
interpretationOur low-cost model improved preliminary prediction to decide pregnant women that will be predicted by the models with high specificity and advanced predictors.
fundingThis work was supported by grant no. MOST108-2221-E-038-018 from the Ministry of Science and Technology of Taiwan.
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