Beyond the randomized clinical trial: innovative data science to close the pediatric evidence gap
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Date
2020-04
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
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.
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Publication status
published
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Book title
Journal / series
Volume
107 (4)
Pages / Article No.
786 - 795
Publisher
Wiley
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
Pediatrics; Machine learning; Data science; Artificial intelligence
Organisational unit
09670 - Vogt, Julia / Vogt, Julia