Section of Neonatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA.
Departments of Anesthesiology, Pediatrics, and Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Pediatr Res. 2023 Jan;93(2):308-315. doi: 10.1038/s41390-022-02181-x. Epub 2022 Jul 8.
Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.
组学评估、生物信息学和人工智能的技术进步使我们重新思考改善患者预后的方法。现在可以以较低的成本快速周转，对包括基因组学、表观基因组学、代谢组学和蛋白质组学在内的生物数据进行集体定量和特征描述。通过机器学习对这些多组学数据集的集成方法的重大进展使我们对疾病发病机制有了整体的认识，并为疾病诊断和预后提供了生物标志物。使用机器学习工具和算法，可以将多组学数据与临床信息集成，开发预测模型，在疾病临床症状明显之前识别风险，从而促进早期干预，改善患者的健康轨迹。在这篇综述中，我们旨在向读者介绍人工智能在围产医学领域整合多组学和临床数据集方面的最新进展，重点介绍新生儿重症监护和精准医学的机会。我们打算简要讨论在医疗保健中使用人工智能的潜在负面社会和伦理后果。我们正处于一个新的医学时代，生物和临床数据集的计算分析将使精准医学成为现实。 影响：生物技术的进步使得多组学评估成为可能，多组学数据的整合可以提供疾病病理生理学的整体视图。人工智能和机器学习工具越来越多地用于医疗保健领域的诊断、预后和结果预测。利用人工智能和机器学习工具整合多组学和临床数据将为围产医学的精准医学铺平道路。
