机器学习方法在产后抑郁预测中的应用[j]卡塔尔世界杯8强波胆分析范围审查%A Saqib,Kiran %A Khan,Amber Fozia %A Butt,Zahid Ahmad %+滑铁卢大学公共卫生科学学院，滑铁卢大学西大街200号，安大略省，N2L 3G1，加拿大，1 5198884567 ext 45107, zahid.butt@uwaterloo.ca %K机器学习%K产后抑郁症%K大数据%K手机%D 2021 %7 24.11.2021 %9审查% jjmir Ment Health %G English %X背景:机器学习(ML)提供了强有力的统计和概率技术，可以使用大量数据成功预测某些临床状况。鉴于近年来技术的快速发展，对ML和大数据研究分析在孕产妇抑郁症中的应用进行综述是恰当和及时的。目的:本研究旨在综合ML和大数据分析在孕产妇心理健康，特别是产后抑郁症(PPD)预测方面的文献。方法:我们使用Arksey和O 'Malley框架的范围审查方法来快速绘制ML中预测PPD的研究活动。2020年9月，两名独立研究人员检索了PsycINFO、PubMed、IEEE explore和ACM数字图书馆，以确定过去12年的相关出版物。数据从文章的ML模型、数据类型和研究结果中提取。结果:共纳入14项研究。所有的研究都报道了使用监督学习技术来预测产后抑郁症。 Support vector machine and random forest were the most commonly used algorithms in addition to Naive Bayes, regression, artificial neural network, decision trees, and XGBoost (Extreme Gradient Boosting). There was considerable heterogeneity in the best-performing ML algorithm across the selected studies. The area under the receiver operating characteristic curve values reported for different algorithms were support vector machine (range 0.78-0.86), random forest method (0.88), XGBoost (0.80), and logistic regression (0.93). Conclusions: ML algorithms can analyze larger data sets and perform more advanced computations, which can significantly improve the detection of PPD at an early stage. Further clinical research collaborations are required to fine-tune ML algorithms for prediction and treatment. ML might become part of evidence-based practice in addition to clinical knowledge and existing research evidence. %M 34822337 %R 10.2196/29838 %U https://mental.www.mybigtv.com/2021/11/e29838 %U https://doi.org/10.2196/29838 %U http://www.ncbi.nlm.nih.gov/pubmed/34822337

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