Sheau-Ling Hsieh

Middleware based Inpatient Healthcare Information System

The paper presents a multi-tier, integrated, distributed, inpatient healthcare information system based on service oriented architecture (SOA) .NET environment in National Taiwan University Hospital (NTUH). The architecture and outcomes of the newly developed inpatient information system (IIS) platform are discussed in details. We also present mechanisms of integration as well as interoperability among the components and multi-database in IIS via health level seven (HL7) Middleware layer. The preliminary performance of the current operating IIS is evaluated and analyzed to verify the efficiency and effectiveness of the architecture we designed.

Design and Implementation of Web-Based Mobile Electronic Medication Administration Record

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.

Design Ensemble Machine Learning Model for Breast Cancer Diagnosis

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.

An Integrated Healthcare Enterprise Information Portal and Healthcare Information System Framework

The paper presents an integrated, distributed healthcare enterprise information portal (HEIP) and hospital information systems (HIS) framework over wireless/wired infrastructure at National Taiwan University Hospital (NTUH). A single sign-on solution for the hospital customer relationship management (CRM) in HEIP has been established. The outcomes of the newly developed outpatient information systems (OIS) in HIS are discussed. The future HEIP blueprints with CRM oriented features: e-learning, remote consultation and diagnosis (RCD), as well as on-line vaccination services are addressed. Finally, the integrated HEIP and HIS architectures based on the middleware technologies are proposed along with the feasible approaches. The preliminary performance of multi-media, time-based data exchanges over the wireless HEIP side is collected to evaluate the efficiency of the architecture

Newborn Screening Healthcare Information System Based on Service-Oriented Architecture

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.

Application of Portable CDA for Secure Clinical-document Exchange

Health Level Seven (HL7) organization published the Clinical Document Architecture (CDA) for exchanging documents among heterogeneous systems and improving medical quality based on the design method in CDA. In practice, although the HL7 organization tried to make medical messages exchangeable, it is still hard to exchange medical messages. There are many issues when two hospitals want to exchange clinical documents, such as patient privacy, network security, budget, and the strategies of the hospital. In this article, we propose a method for the exchange and sharing of clinical documents in an offline model based on the CDA—the Portable CDA. This allows the physician to retrieve the patient’s medical record stored in a portal device, but not through the Internet in real time. The security and privacy of CDA data will also be considered.

Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

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.

Semantic similarity measure in biomedical domain leverage Web Search Engine

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.

A Multi-Voting Enhancement for Newborn Screening Healthcare Information System

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.

Leukemia cancer classification based on Support Vector Machine

In the paper, we classify cancer with the Leukemia cancer of medical diagnostic data. Information gain has been adapted for feature selections. A Leukemia cnacer model that utilizes Information Gain based on Support Vector Machines (IG-SVM) techniques and enhancements to evaluate, interpret the cacer classification. The experimental results indicate that the SVM model illustrates the highest accuracy of classifications for Leukemia cancer.