Browsing by Author "Chen, Han-Ping"

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Newborn Screening for Phenylketonuria: Machine Learning vs Clinicians
(2012)
Chen, Wei-Hsin
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Chen, Han-Ping
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Tseng, Yi-Ju
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Hsu, Kai-Ping
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Hsieh, Sheau-Ling
The metabolic disorders may hinder an infant\'s normal physical or mental development during the neonatal period. The metabolic diseases can be treated by effective therapies if the diseases are discovered in the early stages. Therefore, newborn screening program is essential to prevent neonatal from these damages. In the paper, a support vector machine (SVM) based algorithm is introduced in place of cut-off value decision to evaluate the analyte elevation raw data associated with Phenylketonuria. The data were obtained from tandem mass spectrometry (MS/MS) for newborns. In addition, a combined feature selection mechanism is proposed to compare with the cut-off scheme. By adapting the mechanism, the number of suspected cases is reduced substantially; it also handles the medical resources effectively and efficiently.
Temperature-oriented experiment and simulation as corroborating evidence of MOSFET backscattering theory
(2007-02-01)
Chen, Ming-Jer
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Yan, Shih-Guei
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Chen, Rong-Ting
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Hsieh, Chen-Yu
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Huang, Pin-Wei
The MOSFET backscattering theory relies on a so-called "k(B)T" layer. In this letter, we adopt two different approaches to examine the temperature dependencies of the width spanned by this critical zone. First of all, a 55-nm channel length n-MOSFET is extensively characterized at three temperatures of 233 K, 263 K, and 298 K while undergoing a parameter decoupling/transformation process. A unique relationship is straightforwardly created and is comparable with that in the literature: The width of the "kBT" layer is proportional to the square root of temperature. The case of 77 K is also projected. Other corroborating evidence is a Monte Carlo particle simulation conducted on an 80-nm-long silicon conductor with the "kBT" layer's width proportional to the temperature in the high field region. Without adjusting any parameters, the backscattering theory is shown to work well for the demonstrated temperatures down to 77 K.
Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians
(2013-05-01)
Chen, Wei-Hsin
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Hsieh, Sheau-Ling
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Hsu, Kai-Ping
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Chen, Han-Ping
;
Su, Xing-Yu
Background: A 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. Objective: 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 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. Methods: The 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. Results: The 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. Conclusions: This 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.