Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.
Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.
复制标题
DOI：
10.1093/bib/bbaa369
复制
发表时间：
2021-09-02
影响因子：
9.5
通讯作者：
Boland MR
中科院分区：
文献类型：
--
作者：
Davidson L;Boland MR
开发新的信息学方法，重点是改善妊娠结局仍然是一个活跃的研究领域。本研究的目的是系统地回顾人工智能（AI）和机器学习（ML）（包括深度学习（DL））方法可以为怀孕期间的患者护理提供信息并改善结果的方式。检索EMBASE、PubMed和SCOPUS中的英文文献。检索词包括ML、AI、妊娠和信息学。我们包括研究文章和书籍章节，不包括会议论文，社论和笔记。我们从我们的查询中确定了127项与我们的主题相关的不同研究，并将其纳入综述。我们发现，监督学习方法（n = 69）比无监督方法（n = 9）更受欢迎。流行的方法包括支持向量机（n = 30），人工神经网络（n = 22），回归分析（n = 17）和随机森林（n = 16）。DL等方法开始获得牵引力（n = 13）。AI和ML方法使用最多的妊娠领域的常见领域包括产前护理（例如胎儿异常、胎盘功能）（n = 73）;围产期护理、分娩和分娩（n = 20）;以及早产（n = 13）。将人工智能转化为临床护理的努力包括临床决策支持系统（n = 24）和移动的健康应用程序（n = 9）。总体而言，我们发现ML和AI方法正在用于优化妊娠结局，包括现代DL方法（n = 13）。未来的研究应集中在研究较少的妊娠领域，包括产后和产后护理（n = 2）。此外，还需要在人工智能方法的临床采用以及这种采用的伦理影响方面开展更多工作。
Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes. We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes. We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9). Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.
登录
查看更多内容
影响因子：
3.5
作者：
Balakrishnan R;Gopichandran V;Chaturvedi S;Chatterjee R;Mahapatra T;Chaudhuri I
通讯作者：
Chaudhuri I
样本熵和支持向量机在宫腔电图中区分妊娠和临产
影响因子：
--
作者：
Chen, Lili;Hao, Yaru
通讯作者：
Hao, Yaru
影响因子：
2.9
作者：
Bakibinga, Pauline;Kamande, Eva;Kyobutungi, Catherine
通讯作者：
Kyobutungi, Catherine
影响因子：
4.6
作者：
Assaleh, Khaled
通讯作者：
Assaleh, Khaled
影响因子：
6.1
作者：
Akbulut, Akhan;Ertugrul, Egemen;Topcu, Varol
通讯作者：
Topcu, Varol