Browsing by Author "Chen, Chi-Huang"

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A Multi-Voting Enhancement for Newborn Screening Healthcare Information System
(2010-08-01)
Hsieh, Sung-Huai
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Cheng, Po-Hsun
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Chen, Chi-Huang
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Huang, Kuo-Hsuan
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Chen, Po-Hao
The clinical symptoms of metabolic disorders during neonatal period are often not apparent. If not treated early, irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is essential, imperative to prevent neonatal from these damages. In the paper, we establish a newborn screening model that utilizes Support Vector Machines (SVM) techniques and enhancements to evaluate, interpret the Methylmalonic Acidemia (MMA) metabolic disorders. The model encompasses the Feature Selections, Grid Search, Cross Validations as well as multi model Voting Mechanism. In the model, the predicting accuracy, sensitivity and specificity of MMA can be improved dramatically. The model will be able to apply to other metabolic diseases as well.
Bio-signal Analysis System Design with Support Vector Machines based on Cloud Computing Service Architecture
(2010-01-01)
Shen, Chia-Ping
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Chen, Wei-Hsin
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Chen, Jia-Ming
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Hsu, Kai-Ping
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Lin, Jeng-Wei
Today, many bio-signals such as Electroencephalography (EEG) are recorded in digital format. It is an emerging research area of analyzing these digital bio-signals to extract useful health information in biomedical engineering. In this paper, a bio-signal analyzing cloud computing architecture, called BACCA, is proposed. The system has been designed with the purpose of seamless integration into the National Taiwan University Health Information System. Based on the concept of. NET Service Oriented Architecture, the system integrates heterogeneous platforms, protocols, as well as applications. In this system, we add modern analytic functions such as approximated entropy and adaptive support vector machine (SVM). It is shown that the overall accuracy of EEG bio-signal analysis has increased to nearly 98% for different data sets, including open-source and clinical data sets.
Design Ensemble Machine Learning Model for Breast Cancer Diagnosis
(2012-10-01)
Hsieh, Sheau-Ling
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Hsieh, Sung-Huai
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Cheng, Po-Hsun
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Chen, Chi-Huang
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Hsu, Kai-Ping
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.
Killer cell immunoglobulin-like receptors (KIR) and human leukocyte antigen-C (HLA-C) allorecognition patterns in women with endometriosis
(2020-03-17)
Chou, Ya-Ching
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Chen, Chi-Huang
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Chen, Ming-Jer
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Chang, Ching-Wen
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Chen, Pi-Hua
Endometriosis shares similarities with several autoimmune diseases. The human leukocyte antigen (HLA)-C genotype is associated with several human autoimmune diseases. HLA-C is a ligand of killer cell immunoglobulin receptors (KIRs) and is an essential regulator of natural killer cell activity, which is associated with endometriosis progression. Polymorphisms in HLA-C and KIR affect the activity of NK cells and susceptibility to several diseases. Therefore, we attempted to investigate an association between HLA-C genotype and KIR polymorphism and the occurrence of endometriosis. We tested the association of certain KIR and HLA-C combinations and the development of endometriosis by characterizing both KIR and HLA-C genes in 147 women with endometriosis and 117 controls. The HLA-C genotypes and KIR polymorphisms were analyzed via DNA-based method for higher-resolution genotyping. We found that the occurrence of HLA-C*03:03*01 was increased in endometriosis than in control groups. Analysis of various KIR haplotypes revealed differences between the endometriosis and control cohorts. The number of KIR centromeric A/A haplotypes was increased in the endometriosis group than controls. Moreover, the endometriosis cohort was characterized by reduced number of KIR2DS2-positive individuals in the Han Chinese population. Our current findings suggest that the KIR and HLA-C genotypes are associated with the pathogenesis of endometriosis.
Newborn Screening Healthcare Information System Based on Service-Oriented Architecture
(2010-08-01)
Hsieh, Sung-Huai
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Hsieh, Sheau-Ling
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Chien, Yin-Hsiu
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Weng, Yung-Ching
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Hsu, Kai-Ping
In this paper, we established a newborn screening system under the HL7/Web Services frameworks. We rebuilt the NTUH Newborn Screening Laboratory's original standalone architecture, having various heterogeneous systems operating individually, and restructured it into a Service-Oriented Architecture (SOA), distributed platform for further integrity and enhancements of sample collections, testing, diagnoses, evaluations, treatments or follow-up services, screening database management, as well as collaboration, communication among hospitals; decision supports and improving screening accuracy over the Taiwan neonatal systems are also addressed. In addition, the new system not only integrates the newborn screening procedures among phlebotomy clinics, referral hospitals, as well as the newborn screening center in Taiwan, but also introduces new models of screening procedures for the associated, medical practitioners. Furthermore, it reduces the burden of manual operations, especially the reporting services, those were heavily dependent upon previously. The new system can accelerate the whole procedures effectively and efficiently. It improves the accuracy and the reliability of the screening by ensuring the quality control during the processing as well.
Semantic Similarity Measure in Biomedical Domain Leverage Web Search Engine
(2010)
Chen, Chi-Huang
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Hsieh, Sheau-Ling
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Weng, Yung-Ching
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Chang, Wen-Yung
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Lai, Feipei
Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.
Semantic Similarity Measures in the Biomedical Domain by Leveraging a Web Search Engine
(2013-07-01)
Hsieh, Sheau-Ling
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Chang, Wen-Yung
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Chen, Chi-Huang
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Weng, Yung-Ching
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交大名義發表
Various researches in web related semantic similarity measures have been deployed. However, measuring semantic similarity between two terms remains a challenging task. The traditional ontology-based methodologies have a limitation that both concepts must be resided in the same ontology tree(s). Unfortunately, in practice, the assumption is not always applicable. On the other hand, if the corpus is sufficiently adequate, the corpus-based methodologies can overcome the limitation. Now, the web is a continuous and enormous growth corpus. Therefore, a method of estimating semantic similarity is proposed via exploiting the page counts of two biomedical concepts returned by Google AJAX web search engine. The features are extracted as the co-occurrence patterns of two given terms P and Q, by querying P, Q, as well as P AND Q, and the web search hit counts of the defined lexico-syntactic patterns. These similarity scores of different patterns are evaluated, by adapting support vector machines for classification, to leverage the robustness of semantic similarity measures. Experimental results validating against two datasets: dataset 1 provided by A. Hliaoutakis; dataset 2 provided by T. Pedersen, are presented and discussed. In dataset 1, the proposed approach achieves the best correlation coefficient (0.802) under SNOMED-CT. In dataset 2, the proposed method obtains the best correlation coefficient (SNOMED-CT: 0.705; MeSH: 0.723) with physician scores comparing with measures of other methods. However, the correlation coefficients (SNOMED-CT: 0.496; MeSH: 0.539) with coder scores received opposite outcomes. In conclusion, the semantic similarity findings of the proposed method are close to those of physicians' ratings. Furthermore, the study provides a cornerstone investigation for extracting fully relevant information from digitizing, free-text medical records in the National Taiwan University Hospital database.
Web Services Based Bio-signal System Leveraging Support Vector Machines
(2009)
Hsieh, Sung-Huai
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Hsieh, Sheau-Ling
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Shen, Chia-Ping
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Chen, Wei-Hsin
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Cheng, Po-Hsun
Bio-signal analysis is one of the most important approaches to biomedical engineering. The health information such as ECG, PCG, EMG and EEG are often recorded in digital format to be analyzed. In this paper, a bio-signal analyzing web service system using Support Vector Machines (SVM) classifier technique is proposed. The bio-signals are recorded in digital format as the input of the system. In addition, based on the concept of Service-Oriented Architecture (SOA), the system under web services .NET is designed. The system provides features including heterogeneous platform, protocols, as well as applications. The system has been designed with the purpose of seamless integration into the Health Information System.
Web-based Healthcare Hand Drawing Management System
(2010)
Hsieh, Sheau-Ling
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Weng, Yung-Ching
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Chen, Chi-Huang
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Hsu, Kai-Ping
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Lin, Jeng-Wei
the paper addresses Medical Hand Drawing Management System architecture and implementation. In the system, we developed four modules: hand drawing management module; patient medical records query module; hand drawing editing and upload module; hand drawing query module. The system adapts windows-based applications and encompasses web pages by ASP. NET hosting mechanism under web services platforms. The hand drawings implemented as files are stored in a FTP server. The file names with associated data, e. g. patient identification, drawing physician, access rights, etc. are reposited in a database. The modules can be conveniently embedded, integrated into any system. Therefore, the system possesses the hand drawing features to support daily medical operations, effectively improve healthcare qualities as well. Moreover, the system includes the printing capability to achieve a complete, computerized medical document process. In summary, the system allows web-based applications to facilitate the graphic processes for healthcare operations.