Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome Data.
Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome Data.
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DOI：
10.1145/3368555.3384466
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Salleb-Aouissi A
中科院分区：
文献类型：
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作者：
Hooven TA;Lin AYC;Salleb-Aouissi A
坏死性小肠结肠炎(NEC)是一种威胁生命的肠道疾病，主要影响早产儿出生后的第一周。与NEC相关的死亡率为15%-30%，存活的婴儿容易出现多种严重的长期并发症。这种疾病是零星的，以目前可用的工具来说，是不可预测的。我们正在创建一种早期预警系统，该系统使用粪便微生物组特征，结合临床和人口统计信息，来识别患有NEC的高风险婴儿。我们的方法使用多实例学习，基于神经网络的系统，可以用来生成每天或每周的早产儿NEC预测。该方法的选择是为了有效地利用粪便微生物组分析中稀疏和弱标注的数据集。在这里，我们描述了我们的系统的初步验证，使用来自161名早产儿的嵌套病例对照研究的临床和微生物组数据。我们显示接受者-操作者曲线区大于0.9，75%的受NEC影响的婴儿的主要预测样本至少在发病前24小时被识别。我们的结果为利用有限的基本临床和人口学细节结合粪便微生物组数据开发NEC实时预警系统铺平了道路。
Necrotizing enterocolitis (NEC) is a life-threatening intestinal disease that primarily affects preterm infants during their first weeks after birth. Mortality rates associated with NEC are 15-30%, and surviving infants are susceptible to multiple serious, long-term complications. The disease is sporadic and, with currently available tools, unpredictable. We are creating an early warning system that uses stool microbiome features, combined with clinical and demographic information, to identify infants at high risk of developing NEC. Our approach uses a multiple instance learning, neural network-based system that could be used to generate daily or weekly NEC predictions for premature infants. The approach was selected to effectively utilize sparse and weakly annotated datasets characteristic of stool microbiome analysis. Here we describe initial validation of our system, using clinical and microbiome data from a nested case-control study of 161 preterm infants. We show receiver-operator curve areas above 0.9, with 75% of dominant predictive samples for NEC-affected infants identified at least 24 hours prior to disease onset. Our results pave the way for development of a real-time early warning system for NEC using a limited set of basic clinical and demographic details combined with stool microbiome data.