ArticlePeerJ2023
Prediction model for missed abortion of patients treated with IVF-ET based on XGBoost: a retrospective study.
Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- Machine learning for predictive risk stratification in recurrent miscarriage: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Determining risk factors predicting miscarriage among couples undergoing assisted reproductive treatment: a systematic review.BMC pregnancy and childbirth · 2026Pooled it
- Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis.Journal of assisted reproduction and genetics · 2026Article
- Uterine artery hemodynamic parameters as key predictors of missed abortion risk: clinical validation based on a multimodal predictive model.American journal of translational research · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Prediction of the Need for Surgical Evacuation in Patients with Incomplete Abortion.Risk management and healthcare policy · 2026Article
- Enrichment of decidual CD11c + CD8 + T cells with altered immune function in early pregnancy loss.Nature communications · 2025Article
- Risk prediction for recurrent pregnancy loss based on routine inspections in the first trimester of pregnancy, a retrospective study in China.Frontiers in medicine · 2025Article
- Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.Clinical interventions in aging · 2025Article
- Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications.Mayo Clinic proceedings. Digital health · 2024Review
- High-risk factors for massive haemorrhage in medical abortion patients with missed miscarriage.BMC pregnancy and childbirth · 2024Article
- Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
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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
Aim: In this study, we established a model based on XGBoost to predict the risk of missed abortion in patients treated with Methods: We retrospectively collected the clinical data of 1,017 infertile women treated with IVF-ET. The independent risk factors were screened by performing a univariate analysis and binary logistic regression analysis, and then, all cases were randomly divided into the training set and the test set in a 7:3 ratio for constructing and validating the model. We then constructed the prediction models by the traditional logical regression method and the XGBoost method and tested the prediction performance of the two models by resampling. Results: The results of the binary logistic regression analysis showed that several factors, including the age of men and women, abnormal ovarian structure, prolactin (PRL), anti-Müllerian hormone (AMH), activated partial thromboplastin time (APTT), anticardiolipin antibody (ACA), and thyroid peroxidase antibody (TPO-Ab), independently influenced missed abortion significantly ( Conclusions: We established a prediction model based on the XGBoost algorithm, which can accurately predict the risk of missed abortion in patients with IVF-ET. This model performed better than the traditional logical regression model.
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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.