多变量Logistic回归与其他机器学习算法在妊娠护理预后预测研究中的卡塔尔世界杯8强波胆分析比较系统评价与元分析%A Sufriyana,Herdiantri %A Husnayain,Atina %A Chen,Ya-Lin %A Kuo, zhao - yang %A Singh,Onkar %A Yeh,Tso-Yang %A Wu,Yu-Wei %A Su,Emily Chia-Yu %+台北医科大学医学科技学院生物医学资讯研究所，台北，11031，台北，五行街250号，886 2 663 82736 ext 1515，emilysu@tmu.edu.tw %K机器学习%K妊娠并发症%K预后%K临床预测规则%K荟萃分析%K系统评价%D 2020 %7 17.11.2020 %9综述%J JMIR Med Inform %G英语%X背景:由于多因素的相互作用，妊娠护理预测是复杂的。因此，仅使用一种算法或建模方法，单个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的预测性能，以开发或验证多变量妊娠护理预后预测模型，为临床医生提供决策依据。方法:对MEDLINE、Scopus、Web of Science和Google Scholar上的研究文章进行回顾性分析，并遵循包括偏倚风险(ROB)评估在内的预后预测研究指南。我们根据PRISMA(系统评价和荟萃分析首选报告项目)指南报告结果。研究主要分为PICOTS(人群、指数、比较物、结果、时间和环境):人群:参与生殖管理的男性或女性、孕妇、胎儿或新生儿;指数:使用非lr算法进行风险分类的多变量预后预测模型，为临床医生的决策提供信息;比较器:应用LR的模型; 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 τ2 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%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=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 %M 33200995 %R 10.2196/16503 %U http://medinform.www.mybigtv.com/2020/11/e16503/ %U https://doi.org/10.2196/16503 %U http://www.ncbi.nlm.nih.gov/pubmed/33200995