Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.

中文翻译：

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超越随机临床试验：创新数据科学弥合小儿证据空白。

尽管对儿科试验数据应用了先进的统计和药理学方法，但仍存在较大的儿科证据空白。在这里，我们讨论如何通过使用电子健康记录，移动应用程序，可穿戴设备和社交媒体中的真实数据从儿童那里收集更多数据。用这些方法收集的大型数据集启用并且可能要求使用人工智能和机器学习，以允许对数据进行分析以进行决策。提出了这种方法的应用，包括对未来临床并发症的预测，医学图像分析，新的儿科终点和生物标志物的识别，治疗无反应者的预测以及用于试验富集的安慰剂反应者的预测。最后，我们讨论了如何将机器学习从科学带入儿科临床实践。我们得出结论，应该利用数据科学和机器学习中的创新所提供的当前机会来缩小儿科证据的差距。