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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.

This paper illustrates how our development team has used some information technologies to let physicians obtain an instant abnormal laboratory result report for critical patient care services. We have implemented a healthcare message alerting system (HMAS) on a healthcare short message service (HSMS) engine and the distributed healthcare-oriented service environment (DiHOSE) in the National Taiwan University Hospital (NTUH). The HSMS engine has a general interface for all applications which could easily send any kind of alerting messages. Fundamentally, the DiHOSE uses HL7 standard formats to process the information exchange behaviors and can be flexibly extended for reasonable user requirements. The disease surveillance subsystem is an integral part of NTUH new hospital information system which is based on DiHOSE and the disease surveillance subsystem would send alerting messages through the HSMS engine. The latest cell phone message alerting subsystem, a case study, in NTUH proved that the DiHOSE could integrate the user required functions without much work. We concluded that both HSMS and DiHOSE can generalize and extend application demands efficiently.

In this study, higher order statistics-based radial basis function network (RBF) was proposed for evoked potentials (EPs). EPs provide useful information on diagnosis of the nervous system. They are time-varying signals typically buried in ongoing EEG, and have to be extracted by special methods. RBF with least mean square (LMS) algorithm is an effective method to extract EPs. However, using LMS algorithm usually encounters gradient noise amplification problem, i.e., its performance is sensitive to the selection of step sizes and additional noise. Higher order statistics technique, which can effectively suppress Gaussian and symmetrically distributed non-Gaussian noises, was used to reduce gradient noise amplification problem on adaptation in this study. Simulations and human experiments were also carried out in this study.

In this paper, a higher-order-statistics (HOS)-based radial basis function (RBF) network for signal enhancement is introduced. In the proposed scheme, higher order cumulants of the reference signal were used as the input of HOS-based RBF. An HOS-based supervised learning algorithm, with mean square error obtained from higher order cumulants of the desired input and the system output as the learning criterion, was used to adapt weights. The motivation is that the HOS can effectively suppress Gaussian and symmetrically distributed non-Gaussian noise. The influence of a Gaussian noise on the input of HOS-based RBF and the HOS-based learning algorithm can be mitigated. Simulated results indicate that HOS-based RBF can provide better performance for signal enhancement under different noise levels, and its performance is insensitive to the selection of learning rates. Moreover, the efficiency of HOS-based RBF under the nonstationary Gaussian noise is stable.

This paper illustrates a sustained conceptual service quality improvement process for the management of software development within a healthcare enterprise. Our proposed process is revised from Niland's healthcare quality information system (HQIS). This process includes functions to survey the satisfaction of system functions, describe the operation bylaws on-line, and provide on-demand training. To achieve these goals, we integrate five information systems in National Taiwan University Hospital, including healthcare information systems, health quality information system, requirement management system, executive information system, and digital learning system, to form a full Deming cycle. A preliminary user satisfaction survey showed that our outpatient information system scored an average of 71.31 in 2006.

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.

This paper is to present evoked potential (EP) processing using adaptive interference cancel (AIC) filter with second and high order cumulants. In conventional ensemble averaging method, people have to conduct repetitively experiments to record the required data. Recently, the use of AIC structure with second statistics in processing EP has proved more efficiency than traditional averaging method, but it is sensitive to both of the reference signal statistics and the choice of step size. Thus, we proposed higher order statistics-based AIC method to improve these disadvantages. This study was experimented in somatosensory EP corrupted with EEG. Gradient type algorithm is used in AIC method. Comparisons with AIC filter on second, third, fourth order statistics are also presented in this paper. We observed that AIC filter with third order statistics has better convergent performance for EP processing and is not sensitive to the selection of step size and reference input.