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At least 1,009 records · Page 56Linked to original sources
PubMed1995

The rural and global medical informatics consortium and network for radiology services.

Telemedicine systems that provide health delivery services to rural clinics and hospitals are being demonstrated in clinical settings. This paper summarizes the research and consortia projects at the University of Arizona Medical Center. The paper describes the Rural and Global Picture Archiving and Communications (PACS) environment developed under a National Science Foundation grant. The Rural and Global PACS environment includes many workstations, database, and networking components and is treated as a large distributed system. These components are described in this paper. The multimedia services for radiology provided by the Rural and Global PACS are described and their performance is measured. Finally, the current research work using the Open Software Foundation's distributed computing environment (OSF DCE) services is described. An OSF DCE testbed for the Rural and Global PACS is described and the rationale of using OSF DCE in the project is presented.

Arizona↗
PubMed1995

Informatics in family practice--an Asia-Pacific perspective.

Recent advances in computer hardware, software and telecommunications, and particularly in the development of the electronic medical record, mean that family practitioners around the world now have access to a multiplicity of tools which offer the potential for significant time savings and improved quality of health care provision. Areas such as practice management medication management and prescription generation, clinical record keeping, decision support, medical research and continuing medical education can all be aided through the use of information technology in a family practice setting. Yet family medicine, or general practice, has largely been slow to take up the challenge of implementing information technology in most parts of the Asia-Pacific region. This contrasts sharply with many other areas of medicine which have been very active in embracing this technology. This paper examines the potential advantages and the difficulties of computerisation for general practitioners and their patients in the Asia-Pacific region. It is hoped that the lessons already learned in some countries in this region can be adapted and applied elsewhere.

Asia↗
PubMed1995

bioTk:componentry for genome informatics graphical user interfaces.

bioTk is a collection of graphical "widgets" and utilities that support application programming in the domain of bioinformatics. It is intended to establish a framework that encourages the development of communicating window-based applications and flexible, non-modal user interaction. The current release of bioTk has domain-specific widgets for chromosome ideogram displays, genome maps, and scrolling sequence windows.

Base Sequence↗
PubMed2005

Informatics infrastructure of CAD system.

A computer aided diagnosis (CAD) system requires several components which influence its effectiveness. An image processing methodology is responsible for the analysis, database structure archives and distributes the patient demographics, clinical information, and image data. A graphical user interface is applied in order to enter the data and present it to the user. By designing dynamic Web pages a remote access to the entire is granted. The computer aided diagnosis system includes three layers, which might be installed on various platforms. Elements of the application software are designed independently. Integration of all components is another issue discussed in the presented paper. Implementation of a computer aided diagnosis system improves and accelerates the analysis by giving to the user objective measurement tools. It also standardizes the decision-making process and solves the problem of replicability. Finally, it permits a set of images and features to be collected and recognized as a medical standard and be applied in education and research.

Diagnosis, Computer-Assisted↗
PubMed2006

Cross-cultural factors necessary to enable design of flexible consumer health informatics systems (CHIS).

Examples from on-going work are analyzed to point out cross-cultural factors that need consideration as they are likely to influence the design of consumer health information systems (CHIS). Tailored and flexible presentation of information, appropriate communication services and access to health information can contribute to CHIS use in diverse local and international contexts. It is argued that being mindful of socio-cultural environments, age groups, and health conditions will enhance design and use of CHIS. This will augment current approaches by developing more suitable and culturally sensitive CHIS.

Consumer Health Information↗
PubMed2005

An eco-informatics tool for microbial community studies: supervised classification of Amplicon Length Heterogeneity (ALH) profiles of 16S rRNA.

Support vector machines (SVM) and K-nearest neighbors (KNN) are two computational machine learning tools that perform supervised classification. This paper presents a novel application of such supervised analytical tools for microbial community profiling and to distinguish patterning among ecosystems. Amplicon length heterogeneity (ALH) profiles from several hypervariable regions of 16S rRNA gene of eubacterial communities from Idaho agricultural soil samples and from Chesapeake Bay marsh sediments were separately analyzed. The profiles from all available hypervariable regions were concatenated to obtain a combined profile, which was then provided to the SVM and KNN classifiers. Each profile was labeled with information about the location or time of its sampling. We hypothesized that after a learning phase using feature vectors from labeled ALH profiles, both these classifiers would have the capacity to predict the labels of previously unseen samples. The resulting classifiers were able to predict the labels of the Idaho soil samples with high accuracy. The classifiers were less accurate for the classification of the Chesapeake Bay sediments suggesting greater similarity within the Bay's microbial community patterns in the sampled sites. The profiles obtained from the V1+V2 region were more informative than that obtained from any other single region. However, combining them with profiles from the V1 region (with or without the profiles from the V3 region) resulted in the most accurate classification of the samples. The addition of profiles from the V 9 region appeared to confound the classifiers. Our results show that SVM and KNN classifiers can be effectively applied to distinguish between eubacterial community patterns from different ecosystems based only on their ALH profiles.

Artificial Intelligence↗