Abstract：
OBJECTIVE: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution, we propose using machine learning to predict the optimal order of tests for each child, reducing the number of tests required to identify the most severe neurocognitive deficiencies.
METHODS: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098).
RESULTS: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively.
CONCLUSIONS: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.
METHODS: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098).
RESULTS: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively.
CONCLUSIONS: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.
摘要：
目的：危重患儿在ICU（重症监护病房）出院数年后可能会出现神经认知功能受损。为了评估神经认知功能，这些孩子要接受固定的测试。接受所有测试，然而,对于以前的儿科ICU患者来说，导致一些神经认知缺陷仍未被发现的中断评估。作为解决方案,我们建议使用机器学习来预测每个孩子的最佳测试顺序，减少确定最严重的神经认知缺陷所需的测试数量。
方法：我们将当前的临床方法与几种机器学习方法进行了比较，主要采用多目标回归和标签排序方法。我们还提出了一种新方法，该方法构建了多个多目标预测模型，并将输出结果合并为一个排名，优先考虑较差的神经认知结果。我们使用了出院时可用的数据，来自参与PEPaNIC-RCT试验（ClinicalTrials.gov-NCT01536275）的儿童，以及来自2年随访研究的数据。每个参与地点的机构审查委员会也批准了这项后续研究(ML8052;NL49708.078;Pro00038098)。
结果：我们提出的方法成功优于其他机器学习方法以及当前的临床实践。准确地说，当考虑前4个结果时，我们的方法达到了大约80%的精度，与目前的临床实践和标签排名中最先进的方法获得的65%和78%相比，分别。
结论：我们的实验表明，机器学习可以具有竞争力，甚至优于目前临床实践中使用的测试顺序。这表明我们的模型可以用来严重减少每个孩子所需的测试数量。此外，结果表明,可能的长期不良结局早在ICU出院时就已经可以预测.因此，我们的工作可以被视为ICU出院后允许更多个性化随访的第一步,从而实现预防性护理,而不是治愈性护理.
方法：我们将当前的临床方法与几种机器学习方法进行了比较，主要采用多目标回归和标签排序方法。我们还提出了一种新方法，该方法构建了多个多目标预测模型，并将输出结果合并为一个排名，优先考虑较差的神经认知结果。我们使用了出院时可用的数据，来自参与PEPaNIC-RCT试验（ClinicalTrials.gov-NCT01536275）的儿童，以及来自2年随访研究的数据。每个参与地点的机构审查委员会也批准了这项后续研究(ML8052;NL49708.078;Pro00038098)。
结果：我们提出的方法成功优于其他机器学习方法以及当前的临床实践。准确地说，当考虑前4个结果时，我们的方法达到了大约80%的精度，与目前的临床实践和标签排名中最先进的方法获得的65%和78%相比，分别。
结论：我们的实验表明，机器学习可以具有竞争力，甚至优于目前临床实践中使用的测试顺序。这表明我们的模型可以用来严重减少每个孩子所需的测试数量。此外，结果表明,可能的长期不良结局早在ICU出院时就已经可以预测.因此，我们的工作可以被视为ICU出院后允许更多个性化随访的第一步,从而实现预防性护理,而不是治愈性护理.
