Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.
Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks.
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DOI：
10.3389/fped.2020.00525
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发表时间：
2020
影响因子：
2.6
通讯作者：
Irles C
中科院分区：
文献类型：
--
作者：
Helguera-Repetto AC;Soto-Ramírez MD;Villavicencio-Carrisoza O;Yong-Mendoza S;Yong-Mendoza A;León-Juárez M;González-Y-Merchand JA;Zaga-Clavellina V;Irles C
新生儿败血症由于其非特异性体征和症状而难以诊断。传统的评分系统有助于区分脓毒症患者或非脓毒症患者，但它们没有考虑到每个患者的特殊性。因此，本研究的目的是开发一个早期和晚发型新生儿败血症的诊断模型，根据临床母亲和新生儿的数据，从电子记录，在临床怀疑的时候。预测模型通过训练和验证人工神经网络（ANN）算法获得，该算法具有由早产和足月非脓毒症或脓毒症新生儿（早发性和迟发性）组成的平衡数据集，分别具有阴性和阳性培养结果，使用25个母体和新生儿特征。模型的结局是是否发生脓毒症。使用独立数据集评估的模型的性能指标优于使用基于传统评分系统的相同特征的医生诊断，灵敏度为93.3%，特异性为80.0%，AUROC为94.4%，实际和模拟结果之间的回归系数为0.974。相对于使用类似母体/新生儿变量的最新方法，该模型也表现良好。估计败血症的前10位因素是母亲年龄、宫颈阴道炎和新生儿：发热、呼吸暂停、血小板计数、性别、呼吸缓慢、带状细胞、导尿管使用和出生体重。
Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.
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