Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Informatics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,153 records · Page 64Linked to original sources
PubMedDate not supplied

Informatics in radiology (infoRAD): A complete continuous-availability PACS archive server.

The operational reliability of the picture archiving and communication system (PACS) server in a filmless hospital environment is always a major concern because server failure could cripple the entire PACS operation. A simple, low-cost, continuous-availability (CA) PACS archive server was designed and developed. The server makes use of a triple modular redundancy (TMR) system with a simple majority voting logic that automatically identifies a faulty module and removes it from service. The remaining two modules continue normal operation with no adverse effects on data flow or system performance. In addition, the server is integrated with two external mass storage devices for short- and long-term storage. Evaluation and testing of the server were conducted with laboratory experiments in which hardware failures were simulated to observe recovery time and the resumption of normal data flow. The server provides maximum uptime (99.999%) for end users while ensuring the transactional integrity of all clinical PACS data. Hardware failure has only minimal impact on performance, with no interruption of clinical data flow or loss of data. As hospital PACS become more widespread, the need for CA PACS solutions will increase. A TMR CA PACS archive server can reliably help achieve CA in this setting.

Computer Communication Networks↗
PubMedDate not supplied

Informatics in radiology (infoRAD): portable toolkit for providing straightforward access to medical image data.

Computer-aided analysis of medical images usually involves the development of custom software applications that interpret, process, and ultimately display medical image data. The interpretation stage involves decoding the image data and presenting them to the application developer for further processing. A toolkit has been created specifically for interpreting medical image data; it thus acts as a platform for development of medical imaging applications. The toolkit, which is referred to as NeatMed, is intended to reduce development time by eliminating the need for the application developer to deal directly with medical image data. NeatMed was implemented by using Java, a programming language with a range of attractive features including ease of use, extensive support material, and portability. NeatMed was developed specifically for use in a research environment. Straightforward to use and well documented, it is intended as an alternative to commercially available medical imaging toolkits. NeatMed currently provides support for the Digital Imaging and Communications in Medicine and Analyze medical image file formats. Support material including sample source code is available via the Internet; links to related resources are also provided. Most important, NeatMed is freely available and its continuing development is motivated by requests and suggestions from end users.

Confidentiality↗
PubMedDate not supplied

Informatics in radiology (infoRAD): NeatVision: visual programming for computer-aided diagnostic applications.

A free visual programming-based image analysis development environment for medical imaging applications called NeatVision was developed to provide high-level access to a wide range of image processing algorithms through a well-defined, easy-to-use graphical interface. The system contains over 300 image manipulation, processing, and analysis algorithms. For more advanced users, an upgrade path is provided to extend the core library with use of the developer's interface, giving users access to additional plug-in features, automatic source code generation, compilation with full error feedback, and dynamic algorithm updates. NeatVision was designed to allow users at all levels of expertise to focus on the computer vision design task for computer-aided diagnostic (CAD) applications rather than the subtleties of a particular programming language. The environment allows the designers of image analysis-based CAD techniques to implement their ideas in a dynamic and straightforward manner. Both NeatVision standard and developer's versions can be downloaded free of charge from the Internet and can run on a variety of computer platforms.

Diagnosis, Computer-Assisted↗
PubMedDate not supplied

Informatics in Radiology (infoRAD): Magnetic Resonance Imaging Workbench: analysis and visualization of dynamic contrast-enhanced MR imaging data.

Magnetic Resonance Imaging Workbench (MRIW) allows analysis of T1- and T2*-weighted dynamic contrast-enhanced magnetic resonance imaging data sets to extract tissue permeability and perfusion characteristics by using standard pharmacokinetic models. Parametric maps are calculated from individual pixel enhancement curves in regions of interest (ROIs) and displayed as color overlays on the anatomic images. User-defined ROIs can be saved to ensure consistency of later reanalysis. Individual parametric maps are visualized together with user-selected parameter time-series plots. The following selections are available: overall ROI enhancement curve and fit, histogram, and individual pixel enhancement curve and fit. Summary data (transfer constant, leakage space, rate constant, integrated area under the gadolinium curve after 60 seconds, relative blood volume, relative blood flow, and mean transit time) may be exported to permanent storage along with per-pixel results for statistical analysis. Numerical values for parameters are displayed below the plot for easy reference. The dynamic range of plots and parametric map overlays is interactively adjustable. Viewing individual enhancement curves and parametric maps allows radiologists to investigate the heterogeneity of contrast agent kinetics for lesion characterization and to scrutinize serial changes in response to therapy. MRIW is written in IDL, enabling it to be used on a variety of computer systems.

Computer Graphics↗
PubMedDate not supplied

APA summit on medical student education task force on informatics and technology: steps to enhance the use of technology in education through faculty development, funding and change management.

OBJECTIVE: This article provides an overview of how trainees, faculty, and institutions use technology for acquiring knowledge, skills, and attitudes for practicing modern medicine. METHOD: The authors reviewed the literature on medical education, technology, and change, and identify the key themes and make recommendations for implementing technology in medical education. RESULTS: Administrators and faculty should initially assess their own competencies with technology and then develop a variety of teaching methods that use technology to improve their curricula. Programs should decrease the general knowledge-based content of curricula and increase the use of technology for learning skills. For programs to be successful, they must address faculty development, change management, and funding. CONCLUSIONS: Willingness for change, collaboration, and leadership at all levels are essential factors for successfully implementing technology.

Advisory Committees↗
PubMed2001

Voxels in the brain: neuroscience, informatics and changing notions of objectivity.

In the 1990s, the 'Decade of the Brain', a number of digital and electronic resources have been created to enable the rationalization and integration of the various sub-fields of neuroscience. This approach has been described as 'neuroinformatics'. An important subset of tools (atlases of the brain) developed in the Human Brain Project is examined in detail in order to understand how the use of these tools changes the practice of science. In the course of the development of atlases, what constitutes 'objective' neuroscientific knowledge is redefined in important ways, according to both technological possibilities built into these tools and to the constraints of standardization inherent in projects that involve multiple measurements. The constitution of objectivity is examined across a number of levels (ontological, epistemic, pragmatic) and the concept of 'digital objectivity' is suggested as a label for a particular configuration of ideals, techniques and objects of knowledge in cyberscience.

Brain↗
PubMedDate not supplied

Medical informatics.

Explore the source record for details and available documents.

Data Collection↗
PubMed2006

Pharmacogenomics, informatics, and individual drug therapy in psychiatry: past, present and future.

The modern era of psychopharmacology began in the 10 year period from the late 1940s to the late 1950s. During this period, the first antidepressants, antipsychotics, anxiolytics and mood stabilizers were all discovered. In the 1960s, the pharmacology of these drugs was elucidated and theories about the mechanisms of action proposed. In the 1970s and 1980s, new, more selective compounds were developed based on improved structure-activity relationships derived from in vitro receptor binding studies and animal models. These compounds entered clinical testing in the 1980s and began to be marketed in the late 1980s and 1990s. All of these agents were approved to treat psychiatric syndromes which are conditions defined by a cluster of signs and symptoms. None of these agents was developed based on an understanding of the pathophysiology of the illnesses being treated. None of these agents are curative and virtually all have limited clinical efficacy. In the earliest days of the modern era, there were few drugs available to combine and many had such broad actions that they were often marginally tolerated or unsafe when used in combination (tricyclic antidepressants and monoamine oxidase inhibitors). With the advent of more medications, the frequency and extent of polypharmacy has exploded. In addition to simply having more drugs from which to select with different pharmacological profiles, many newer medications are also more selective in their pharmacological actions and thus are often better tolerated and safer when used in combination. In addition, there is the concern that the trade-off for more selective pharmacology may have been better tolerability at the expense of reduced efficacy, which clinicians then compensate for by using more medications in combination. For all of the above reasons, polypsychopharmacology has been present from the beginning of the modern era of psychopharmacotherapy and continues to be the rule rather than the exception. In fact, the frequency and the complexity of such polypsychopharmacology are both enormous and increasing. The percentage of patients being discharged from the Biological Branch of the National Institute of Mental Health on more than three psychiatric medications increased more than ten times between 1974-79, and 1990-95. The majority of patients seen in the Veterans Administration Medical System in the United States are on unique combinations of medications and the frequency and complexity of such polypharmacotherapy is increased in patients on psychiatric medications. Throughout the modern era, there have been attempts to determine whether there are populations of patients selectively responsible to specific agents (e.g. serotonin versus norepinephrine reuptake inhibitors). However, no compelling data have so far emerged. Instead, clinicians generally resort to combining drugs on the basis of symptoms such as psychosis and depression or anxiety and depression. Science has primarily informed the clinician about safety concerns rather than efficacy concerns when using such combinations. That will change in the future with a better understanding of the pathophysiology of psychiatric illnesses which in turn will lead to improved therapies and the potential for more rationally derived combination treatments.

Clinical Trials as Topic↗
PubMed2006

An informatics model for tissue banks--lessons learned from the Cooperative Prostate Cancer Tissue Resource.

BACKGROUND: Advances in molecular biology and growing requirements from biomarker validation studies have generated a need for tissue banks to provide quality-controlled tissue samples with standardized clinical annotation. The NCI Cooperative Prostate Cancer Tissue Resource (CPCTR) is a distributed tissue bank that comprises four academic centers and provides thousands of clinically annotated prostate cancer specimens to researchers. Here we describe the CPCTR information management system architecture, common data element (CDE) development, query interfaces, data curation, and quality control. METHODS: Data managers review the medical records to collect and continuously update information for the 145 clinical, pathological and inventorial CDEs that the Resource maintains for each case. An Access-based data entry tool provides de-identification and a standard communication mechanism between each group and a central CPCTR database. Standardized automated quality control audits have been implemented. Centrally, an Oracle database has web interfaces allowing multiple user-types, including the general public, to mine de-identified information from all of the sites with three levels of specificity and granularity as well as to request tissues through a formal letter of intent. RESULTS: Since July 2003, CPCTR has offered over 6,000 cases (38,000 blocks) of highly characterized prostate cancer biospecimens, including several tissue microarrays (TMA). The Resource developed a website with interfaces for the general public as well as researchers and internal members. These user groups have utilized the web-tools for public query of summary data on the cases that were available, to prepare requests, and to receive tissues. As of December 2005, the Resource received over 130 tissue requests, of which 45 have been reviewed, approved and filled. Additionally, the Resource implemented the TMA Data Exchange Specification in its TMA program and created a computer program for calculating PSA recurrence. CONCLUSION: Building a biorepository infrastructure that meets today's research needs involves time and input of many individuals from diverse disciplines. The CPCTR can provide large volumes of carefully annotated prostate tissue for research initiatives such as Specialized Programs of Research Excellence (SPOREs) and for biomarker validation studies and its experience can help development of collaborative, large scale, virtual tissue banks in other organ systems.

Databases as Topic↗