Great Ormond Street Hospital, London, UK.
University College London Institute of Child Health, London, UK.
Pediatr Nephrol. 2018 Oct;33(10):1625-1627. doi: 10.1007/s00467-018-4021-4. Epub 2018 Jul 12.
In a recent article in Pediatric Nephrology, Olivier Niel and colleagues applied an artificial intelligence algorithm to a clinical problem that continues to challenge experienced pediatric nephrologists: optimizing the target weight of children on dialysis. They compared blood pressure, antihypertensive medication and intradialytic symptoms in children whose target weight was prescribed firstly by a nephrologist, then subsequently using a machine learning algorithm. Improvements in all outcome measures are reported. Their innovative approach to tackling this important clinical problem appears promising. In this editorial, we discuss the strengths and weaknesses of their study and consider to what extent machine learning strategies are suited to optimizing pediatric dialysis outcomes.
在最近的一篇《儿科肾脏病学》文章中，Olivier Niel 及其同事应用人工智能算法解决了一个持续困扰经验丰富的儿科肾脏病医生的临床问题：优化透析患儿的目标体重。他们比较了根据肾脏病医生首次处方和随后使用机器学习算法处方目标体重的患儿的血压、降压药物和透析中症状。所有结果指标均有改善。他们这种解决这一重要临床问题的创新方法似乎很有前景。在这篇社论中，我们讨论了他们研究的优缺点，并考虑了机器学习策略在多大程度上适合优化儿科透析结果。
