Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning.
Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning.
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DOI：
10.1038/s41598-021-85878-z
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发表时间：
2021-03-30
影响因子：
4.6
通讯作者：
Angelini ED
中科院分区：
文献类型：
--
作者：
Greenbury SF;Ougham K;Wu J;Battersby C;Gale C;Modi N;Angelini ED
我们使用不可知的、无监督的机器学习来对英国新生儿病房收治的婴儿信息的大型临床数据库进行聚类。我们的目的是获得营养实践的见解，在新生儿护理的核心重要性领域，利用英国国家新生儿研究数据库（NNRD）。我们对胎龄小于32周的极早产儿（n = 45，679）在6年期间的每日营养摄入量的时间序列数据进行了聚类。这揭示了46个营养集群的大小不均匀，显示了常见的可解释的临床实践以及罕见的方法。具有相似入院特征的营养组群揭示了营养实践、地理位置和结果之间的关联。我们展示了营养亚组如何被视为不同的干预措施，并测试了与可衡量结果的关联。我们举例说明了潜在的营养实践和结果之间的关系，出院体重和支气管肺发育不良（BPD）。我们确定了配方奶对更大的出院体重的众所周知的影响，以及支持合理的，但证据不足的观点，即母乳对BPD有保护作用。我们的框架强调了不可知机器学习方法在提供临床实践见解和使用常规数据生成假设方面的潜力。
We used agnostic, unsupervised machine learning to cluster a large clinical database of information on infants admitted to neonatal units in England. Our aim was to obtain insights into nutritional practice, an area of central importance in newborn care, utilising the UK National Neonatal Research Database (NNRD). We performed clustering on time-series data of daily nutritional intakes for very preterm infants born at a gestational age less than 32 weeks (n = 45,679) over a six-year period. This revealed 46 nutritional clusters heterogeneous in size, showing common interpretable clinical practices alongside rarer approaches. Nutritional clusters with similar admission profiles revealed associations between nutritional practice, geographical location and outcomes. We show how nutritional subgroups may be regarded as distinct interventions and tested for associations with measurable outcomes. We illustrate the potential for identifying relationships between nutritional practice and outcomes with two examples, discharge weight and bronchopulmonary dysplasia (BPD). We identify the well-known effect of formula milk on greater discharge weight as well as support for the plausible, but insufficiently evidenced view that human milk is protective against BPD. Our framework highlights the potential of agnostic machine learning approaches to deliver clinical practice insights and generate hypotheses using routine data.
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DOI：
10.1198/016214504000001187
发表时间：
2004-09-01
影响因子：
3.7
作者：
Imai, K;van Dyk, DA
通讯作者：
van Dyk, DA
影响因子：
2
作者：
COLE, TJ;GREEN, PJ
通讯作者：
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影响因子：
2.5
作者：
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通讯作者：
Hay WW Jr
影响因子：
168.9
作者：
Harding, Jane E.;Cormack, Barbara E.;Bloomfield, Frank H.
通讯作者：
Bloomfield, Frank H.
影响因子：
4.6
作者：
Miotto R;Li L;Kidd BA;Dudley JT
通讯作者：
Dudley JT