Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile.
Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile.
Front Endocrinol (Lausanne). 2023 May 19;14:1130139. doi: 10.3389/fendo.2023.1130139. eCollection 2023.
Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.
To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.
Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.
ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.
To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.
The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
机器学习（ML）对应于广泛的方法，这些方法使用数学、统计学和计算科学来同时从多个变量中学习。通过模式识别，ML 方法能够找到隐藏的关联并对不同条件做出准确的预测。ML 已成功地用于解决心理学、经济学、生物学和化学等不同科学领域的各种问题。因此，我们想知道它在妇产科领域的应用程度。
描述 ML 在妊娠疾病和并发症方面的应用现状。
在 PubMed、Web of Science 和 Google Scholar 上搜索出版物。考虑了七个感兴趣的主题：妊娠期糖尿病、子痫前期、围产期死亡、自然流产、早产、剖宫产和胎儿畸形。
ML 已广泛应用于所有纳入的研究主题。其应用多种多样，最常见的是预测围产期疾病。其他 ML 应用包括（但不限于）生物标志物发现、风险估计、相关性评估、药理治疗预测、药物筛选、数据采集和数据提取。审查的文章大多发表在过去五年。该领域最常用的 ML 方法是非线性方法。除了逻辑回归，很少使用线性方法。
改善来自不同现实的医疗和研究环境中的数据记录、存储和更新。使用来自先进仪器的数据开发更准确和可理解的 ML 模型。对现有高精度 ML 模型进行验证和影响分析研究。
ML 在妊娠疾病和并发症中的应用相当新，并且在过去几年中有所增加。应用多种多样，不仅指向诊断，还指向围产期改变的管理、治疗和病理生理学理解。面对与处理不同类型的数据、处理越来越多的信息、新兴技术的发展以及转化研究的需求相关的挑战，可以预计 ML 在妇产科领域的应用将继续增长。
