Centro Hospitalar Tâmega e Sousa, EPE, Lugar do Tapadinho, Penafiel, 4564-007, Portugal.
BMC Med Inform Decis Mak. 2012 Dec 7;12:143. doi: 10.1186/1472-6947-12-143.
Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques.
This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology.This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa--EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer.Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia.
The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic.
The findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.
由于出生后住院时间缩短，高胆红素血症在新生儿中已成为一个日益普遍的问题。黄疸是新生儿最常见的疾病，虽然在大多数情况下是良性的，但如果评估不当，可能会导致严重的神经后果。在医学的不同领域，数据挖掘技术有助于提高其他方法的结果。因此，本研究旨在应用数据挖掘技术改善新生儿黄疸的诊断。
本研究遵循 Cross Industry Standard Process for Data Mining 模型的不同阶段作为其方法学。这项观察性研究于 2011 年 2 月至 3 月在一家中心医院（Centro Hospitalar Tâmega e Sousa--EPE）的妇产科进行。共纳入 227 名胎龄 35 周或以上的健康新生儿。收集并分析了超过 70 个变量。此外，从出生到出院，使用非侵入性胆红素计，每隔 8 小时测量一次经皮胆红素水平。使用 Weka 数据挖掘软件中包含的算法（如决策树（J48）和神经网络（多层感知器）），使用不同的属性子集来训练和测试分类模型。将准确性结果与传统的高胆红素血症预测方法进行比较。
将不同的分类算法应用于收集的数据，可以准确预测随后的高胆红素血症。特别是在新生儿出生后 24 小时，高胆红素血症的预测准确率为 89%。使用以下算法可获得最佳结果：朴素贝叶斯、多层感知器和简单逻辑回归。
我们的研究结果表明，新方法（如数据挖掘）可以支持医疗决策，有助于改善新生儿黄疸的诊断。
