BackgroundA hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification.ObjectiveThe 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 was designed and deployed according to a service-oriented architecture (SOA) 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.MethodsThe framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases.ResultsThe feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency.ConclusionsThis SOA Web service–based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

This article describes the successful experiences of National Taiwan University Hospital (NTUH) in moving from IBM Mainframe to connected networking computer systems. We use multi-tier architecture and HL7 standard to implement our new outpatient Hospital Information System (HIS). The NTUH HIS is a complex environment with several operating systems, databases, and information systems. We adopt ServiceOriented Architecture (SOA) to reduce the complex relations between systems and solve data consistency problems among databases. We also show that the distributed architecture can provide us stable and reasonable system performances. Our main contribution is proving that the distributed environment with HL7 standard and SOA can sustain in a highly demanding environment.

Patients' safety is the most essential, critical issue, however, errors can hardly prevent, especially for human faults. In order to reduce the errors caused by human, we construct Electronic Health Records (EHR) in the Health Information System (HIS) to facilitate patients' safety and to improve the quality of medical care. During the medical care processing, all the tasks are based upon physicians' orders. In National Taiwan University Hospital (NTUH), the Electronic Health Record committee proposed a standard of order flows. There are objectives of the standard: first, to enhance medical procedures and enforce hospital policies; secondly, to improve the quality of medical care; third, to collect sufficient, adequate data for EHR in the near future. Among the proposed procedures, NTUH decides to establish a web-based mobile electronic medication administration record (ME-MAR) system. The system, build based on the service-oriented architecture (SOA) as well as embedded the HL7/XML standard, is installed in the Mobile Nursing Carts. It also implement accompany with the advanced techniques like Asynchronous JavaScript and XML (Ajax) or Web services to enhance the system usability. According to researches, it indicates that medication errors are highly proportion to total medical faults. Therefore, we expect the ME-MAR system can reduce medication errors. In addition, we evaluate ME-MAR can assist nurses or healthcare practitioners to administer, manage medication properly. This successful experience of developing the NTUH ME-MAR system can be easily applied to other related system. Meanwhile, the SOA architecture of the system can also be seamless integrated to NTUH or other HIS system.

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