ArticleBMC pregnancy and childbirth2025
Organ-system-based subclassification of preeclampsia using machine learning predicts pregnancy outcomes.
Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Unsupervised Machine Learning for the Identification of Latent First-Trimester Obstetric Phenotypes Associated with Maternal and Perinatal Morbidity.Diagnostics (Basel, Switzerland) · 2026Article
- Phenotypic subclassification of preeclampsia through cluster analysis of preterm birth-related factors.BMC medical informatics and decision making · 2026Article
- Risk stratification and prediction of emergency delivery in early-onset preeclampsia using machine learning.BMC medical informatics and decision making · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundPreeclampsia (PE) is a complex disorder with significant variability in organ involvement. It remains unclear whether machine learning can identify organ-system-based subclasses of PE. This study aimed to identify novel subclasses of PE using organ-system biomarkers to improve pregnancy outcome prediction.
methodsWe retrospectively analyzed clinical data from PE patients at Fujian Maternity and Child Health Hospital. K-means clustering was applied using organ system function indicators, with 10-fold cross-validation. Functional indicators and pregnancy outcomes were compared across subclasses. Heatmap and sankey diagrams were used to reveal the distribution of patients across combined organ system clusters.
resultsThe analysis included 7,531 PE patients treated between 2013 and 2023. 10-fold cross-validation confirmed clustering robustness with mean ARI of 0.8806 ± 0.0099 and NMI of 0.7800 ± 0.0123. Three heart function clusters were identified using five indicators, with H-Cluster 1 showing the poorest heart function and the highest complication rates. Five kidney clusters were determined from ten indicators. K-Cluster 1 and K-Cluster 5 showed distinct biomarker patterns but similar complication rates (P > 0.05). Liver function analysis using thirteen indicators revealed four clusters. L-Cluster 1 exhibited elevated liver enzymes and bilirubin with higher severe PE and intrahepatic cholestasis rates, whereas L-Cluster 3 had lower protein levels but higher anemia, fetal distress and hemorrhage incidence (P < 0.05). Five coagulation clusters were determined from nine indicators, showing significant differences in indicators and complication rates (P < 0.05). Heatmap and sankey diagram analyses revealed significant overlap between high-risk clusters, with the most frequent combination being H-Cluster 1, K-Cluster 1, L-Cluster 1 and C-Cluster 5.
conclusionsMachine learning identified distinct PE subclasses based on organ system dysfunction patterns, each demonstrating unique pregnancy outcomes. This suggests potential clinical utility of computational approaches for PE subclassification and generates hypotheses for further investigation of its biological mechanisms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.