@文章{信息:doi/10.2196/29838，作者=“Saqib, Kiran和Khan, Amber Fozia和Butt, Zahid Ahmad”，标题=“预测产后抑郁症的机器学习方法:范围审查”，期刊=“JMIR Ment Health”，年=“2021”，月=“11月”，日=“24”，卷=“8”，数字=“11”，页=“e29838”，关键词=“机器学习;产后抑郁症;大数据;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疾病。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回顾是中肯和及时的。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(PPD)预测方面的文献。方法:我们使用了一种范围审查方法，使用Arksey和O'Malley框架来快速映射ML中的研究活动，以预测PPD。两名独立研究人员于2020年9月搜索了PsycINFO、PubMed、IEEE Xplore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:共确定了14项研究。 All studies reported the use of supervised learning techniques to predict PPD. 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. ", issn="2368-7959", doi="10.2196/29838", url="https://mental.www.mybigtv.com/2021/11/e29838", url="https://doi.org/10.2196/29838", url="http://www.ncbi.nlm.nih.gov/pubmed/34822337" }