@文章{info:doi/10.2196/16503，作者="Sufriyana, Herdiantri和Husnayain, Atina和Chen，雅琳和Kuo，朝阳和Singh, Onkar和Yeh, Tso-Yang和Wu, Yu-Wei和Su, Emily Chia-Yu"，标题="多变量Logistic回归和其他机器学习算法在妊娠护理预后预测研究中的比较:“系统回顾与meta分析”，期刊=“JMIR Med Inform”，年=“2020”，月=“11”，日=“17”，卷=“8”，数=“11”，页=“e16503”，关键词=“机器学习;妊娠并发症;预后;临床预测规则;荟萃分析;背景:怀孕护理中的预测是复杂的，因为多种因素之间的相互作用。因此，仅使用一种算法或建模方法的单一预测器很难预测妊娠结局。目的:本研究旨在回顾和比较逻辑回归(LR)和其他机器学习算法之间的预测性能，以开发或验证怀孕护理的多变量预后预测模型，为临床医生的决策提供参考。方法:回顾MEDLINE、Scopus、Web of Science和谷歌Scholar的研究文章，遵循预后预测研究的几项指南，包括偏倚风险(ROB)评估。 We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using $\tau$2 and I2. Results: Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8{\%}) and artificial neural networks (20/142, 14.1{\%}) among non-LR algorithms. Only 16.9{\%} (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95{\%} CI 1.49-3.53; I2=86{\%}; $\tau$2=0.77) and pre-eclampsia (logit AUROC 1.2, 95{\%} CI 0.72-1.67; I2=75{\%}; $\tau$2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95{\%} CI 1.39-3.13; I2=75{\%}; $\tau$2=0.43) and gestational diabetes (logit AUROC 1.03, 95{\%} CI 0.69-1.37; I2=83{\%}; $\tau$2=0.07). Conclusions: Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display{\_}record.php?RecordID=136106 ", issn="2291-9694", doi="10.2196/16503", url="http://medinform.www.mybigtv.com/2020/11/e16503/", url="https://doi.org/10.2196/16503", url="http://www.ncbi.nlm.nih.gov/pubmed/33200995" }