以網路服務為基礎的新生兒代謝疾病篩檢系統

Abstract

新生兒篩檢是在早期判斷出新生兒代謝疾病的方法，透過新生兒的採血，由血液樣本進行串聯質譜儀的分析，可以及早防治與給予治療。為此我們在台大醫院開發了一套新生兒篩檢資料處理系統，這個系統包含了樣本收集、檢驗資料上傳分析、給予治療與追蹤病人的功能。在本研究中，我們使用了資料探勘的方法來提高新生兒代謝疾病的辨識率，首先，我們將2002年到2007年七月的紙本新生兒篩檢室的資料數位化，並且把所有新生兒的篩檢資料彙集成資料庫。在本研究中，我們的機器學習方法將應用於苯酮尿症、高甲硫胺酸血症與3-甲基巴豆醯輔酵素羧化酵素缺乏症，藉由嘗試新的特徵組合配合最佳特徵抽取的方法，我們得到了對不同的疾病的最佳模型，可以大幅的下降偽陽性的個案，並且可以正確的判斷出所有陽性的病人。由此可知，此系統可以準確的判斷新生兒篩檢相關疾病，並且可以更有效的利用醫療資源。

A Hospital Information System that integrates screening data and interpretation of the data is routinely requested. However, the accuracy of disease classification may be low because of the disease characteristics and analytes used for classification. The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system has been designed and deployed based on a Service-Oriented Architecture framework under the Web Services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. In this study, machine learning classification was used to predict the following: phenylketonuria, hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase deficiency. The classification methods used 435,682 newborn samples collected at the Center between 2006 and 2012. These samples include 229 newborns with values over the diagnostic cutoffs and 1822 over the screening cutoffs but that do not meet the diagnostic cutoffs. The feature selection strategies were defined as follows. The original 35 analytes and the manifested features are ranked based on the F-score. Next, the combinations of the top 20 ranked features were selected as input features to Support Vector Machines classifiers to obtain optimal feature sets. Finally, the feature sets were tested using 5-fold cross validation and the optimal models were generated. The datasets collected in year 2011 and 2012 were utilized as the predicting cases. By adopting the results of this study, the number of suspected cases could be reduced dramatically. Furthermore, the results of the research have been compared with those of other methodologies.