计算方法预测早产和新生儿结局。
Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.
机构信息
Immunology Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Medical Scientist Training Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA.
Immunology Program, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA; Department of Biomedical Data Science, Stanford University, 300 Pasteur Drive, Grant S280, Stanford, CA 94305-5117, USA.
出版信息
Clin Perinatol. 2024 Jun;51(2):461-473. doi: 10.1016/j.clp.2024.02.005. Epub 2024 Mar 8.
Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. Increasing availability of multimodal high-dimensional data sets with concurrent advances in artificial intelligence (AI) have created a rich opportunity to gain novel insights into PTB, a clinically complex and multifactorial disease. Here, the authors review the use of AI to analyze 3 modes of data: electronic health records, biological omics, and social determinants of health metrics.
早产（PTB）及其相关并发症是导致婴儿死亡和发病的主要原因。准确的预测模型和对与 PTB 相关并发症的更好的生物学理解对于降低其不良影响至关重要。随着多模态高维数据集的可用性不断增加，以及人工智能（AI）的同步进步，为深入了解临床复杂且多因素的疾病 PTB 提供了新的机会。在这里，作者回顾了使用 AI 分析 3 种类型的数据：电子健康记录、生物组学和健康指标的社会决定因素。
Zotero 插件