ArticleCurrent drug targets2026
Clinical Deployment of Interpretable AI: Bridging Routine Clinical Tests and Proteomic Signatures for Preeclampsia Risk Stratification.
Article in Current drug targets, 2026. 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.
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Interferon-primed immune landscapes predict immune-related adverse events during immune checkpoint inhibitor therapy.Frontiers in cell and developmental biology · 2026Article
- Tumor ecosystem subtyping of breast cancer based on somatic mutations and network propagation reveals distinct prognostic and genomic landscapes.Frontiers in genetics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
introductionPreeclampsia (PE) is the second-leading global cause of maternal mortality, affecting 5% of primigravidas. Owing to the substantial heterogeneity of clinical manifestations in PE, an urgent need arises to quantitatively evaluate the efficacy of existing diagnostic methods based on positive proteinuria (PRO) and to develop novel biomarkers to enhance diagnostic accuracy.
methodsWe based 1,215 pregnant women obtained from who delivery at the hospital in January 2018 and April 2022 and involved predictors of 66 routine clinical laboratory tests (RCLTs). In addition, from 362 peripheral blood proteomic samples obtained from published datasets. Compared, evaluated, and explored the performances of 5 machine learning models to constructed prediction models.
resultsWe pioneered the application of machine learning to assess the diagnostic efficiency of PRO quantitatively, AUROC of 0.771. Next, a more comprehensive assessment was discussed, including 66 RCTIs from blood and urine test items, the AUROC increased to 0.920. Furthermore, the feature selection strategy trained a superior routine clinical prediction model with 5 RCLTs (PRO, alkaline phosphatase (ALP), amylase (AMY), Uric Acid (UA), and Lactate Dehydrogenase (LDH)) for PE to ensure practicality and high performance. In addition, we constructed a protein prediction model for PE based on peripheral blood proteome. Subsequently, EphA1 has been identified as a protein candidate marker for PE, and is highly expressed in placentals. Finally, we established a user-friendly and interpretable PE risk prediction webserver (http://bioinfor. imu.edu.cn/lbppe/) to assist improve the PE diagnosis efficiency. DISCUSSION: The predictive platform developed in this study enhances PE early detection, addressing the clinical need for rapid screening tools. Future multi-center trials should validate the models' generalizability.
conclusionThis study assessed the diagnostic efficiency of proteinuria quantitatively and constructed a cost-effective PE prediction system, which is crucial for improving the diagnostic accuracy of PE.
Indexed as
Identifiers
41084250What 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.