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PubMed2003

Managing the transcription revolution. Industry forces shape future of field.

You may be struggling with contract issues with a vendor. Or maybe you're contemplating the pros and cons of working with outsource, at-home, or overseas transcriptionists. It's a fact: if transcription processes aren't working efficiently, the entire HIM department may be adversely affected. Factor in additional concerns such as data capture for electronic health records, compliance, and patient safety, and the importance of ensuring quality and cost-efficient transcription becomes even more apparent. To help you answer some of these questions, the Journal of AHIMA is launching a four-part series dedicated to transcription issues from the HIM professional's point of view. In this issue, we begin with MTIA president Scott Faulkner's overview of the industry and where it's going next. In upcoming issues, other experts will look at controlling cost and monitoring quality, navigating new technologies, and dealing with contract-related issues.

Computer Communication Networks↗
PubMed2004

Advances in biomedical image analysis--past, present and future challenges.

Starting from raw data files coding eight bits of gray values per image pixel and identified with no more than eight characters to refer to the patient, the study, and technical parameters of the imaging modality, biomedical imaging has undergone manifold and rapid developments. Today, rather complex protocols such as Digital Imaging and Communications in Medicine (DICOM) are used to handle medical images. Most restrictions to image formation, visualization, storage and transfer have basically been solved and image interpretation now sets the focus of research. Currently, a method-driven modeling approach dominates the field of biomedical image processing, as algorithms for registration, segmentation, classification and measurements are developed on a methodological level. However, a further metamorphosis of paradigms has already started. The future of medical image processing is seen in task-oriented solutions integrated into diagnosis, intervention planning, therapy and follow-up studies. This alteration of paradigms is also reflected in the literature. As German activities are strongly tied to the international research, this change of paradigm is demonstrated by selected papers from the German annual workshop on medical image processing collected in this special issue.

Electronic Data Processing↗
PubMed1982

The automated analysis of data from single ionic channels.

The development of single channel recording has brought with it the need to analyse enormous amounts of data. The data analysis is time consuming and subject to observer biases since the events are random in time and are contaminated with uncorrelated noise. We have developed a heuristic pattern recognition program which identifies with high precision single channel currents and rejects contaminating noise. The program interactively provides for a variety of amplitude and duration measures. Analysis is flexible and rapid: a file containing over 10,000 events can be analysed in under 2 h. Specific detection features include variable lowpass filtering, automatic baseline restoration, and adaptive amplitude thresholds. A record is analysed through duration histograms, binomial estimates of the number of active channels present, cross-correlation estimates between parameters, spectral analysis of events and background noise, and stationarity of mean channel current. The graphic output facilities can plot raw data (after filtering and baseline restoration) with the idealized signal superimposed or with detected events underlined. A batch processing facility has been included to allow processing of data during periods of low computer demand.

Animals↗
PubMed1987

Managing research data with self-documenting files.

Processing biomedical research data is frequently complex due to the evolutionary nature of experiments and the requisite modification of analysis software. For the past several years we have been evolving a set of software tools designed to improve our ability to respond to evolving experimental designs. These tools allow the investigator to easily manipulate the research data and specify desired data transformations at run time. Coupling of analysis software to research data files is dependent on data files that are commented in a manner similar to that used in programming languages. The resulting annotated data files are self-documenting, and their use facilitates visual interpretation of displayed data as well as automatic processing of subsets of data. Here we present a formal description of a self-documenting file and describe several software tools that facilitate processing of biomedical research data.

Electronic Data Processing↗
PubMed1990

[A microcomputer processing system of evoked potentials of brain slices and applied examples].

Processing the evoked potentials of neurons with a computer can greatly increase the efficiency of data processing and obtain much more information from the data. Besides, it renders possibility of large quantities and prolonged observation. In this system, DMA mode is used for data transfer from A/D card to the computer, so total processing program including sampling subroutine was made in BASIC language. This article introduces the functions of this program in processing data and the technique in making this program.

Brain↗
PubMed1999

The "psychic" neuron of the cerebral cortex.

Remarkable advances in the identification, cloning, and localization of ion channels and receptors in the central nervous system have opened up unprecedented possibilities for relating structure to physiological function at the subcellular level of analysis. A singularly advanced property of select central nervous system neurons is their ability to exhibit increases in firing rate in relation to the mnemonic trace of a preceding event, a property that has been referred to as "working memory." Single-cell recordings from the prefrontal cortex of nonhuman primates have revealed neurons in the prefrontal cortex that possess "memory fields" analogous to the receptive field properties of sensory neurons. The integrity of these neurons has been shown to be essential for accurate performance in memory tasks performed by trained monkeys (and humans). We can now show that the excitability and/or tuning of these prefrontal neurons are subject to modulatory influences by dopamine, serotonin, GABA, and glutamate among other peptides and conventional neurotransmitters. I will describe the dopaminergic, serotonergic, and GABAergic innervation of pyramidal neurons engaged in working memory and the localization of neurotransmitter receptors through which they exert their actions. The findings reveal a remarkable degree of diversity in the subcellular localization and functionality of the five cloned dopamine receptors (D1, D2, D3, D4, and D5) and two serotonin (5HT2A and 5HT3) receptors that have been examined to date. The potential now exists for linking systems neurobiology with molecular biophysics to comprehend the highest functions of information processing that distinguish our species.

Animals↗
PubMed1988

[Logical structure and meaning in information processing].

Investigating the influence the dimensions "Meaning", and "Logical Structure" feature in the process of solving verbally-presented problems is the purpose of this study. 120 13-year-old boys and girls were administered 12 problems built around two different logical structures with six different "Meaning" dimensions. The results indicate that the logical structure of the problems is an important factor when information processing is at stake.

Adolescent↗
PubMed2000

Current state of automated crystallographic data analysis.

A goal of structural biology--and of structural genomics in particular--is to improve the underlying methodology for high-throughput determination of three-dimensional structures of biological macromolecules. Here we address issues related to the development, automation and streamlining of the process of macromolecular X-ray crystal structure solution.

Automation↗
PubMedDate not supplied

Automatic particle pickup method using a neural network has high accuracy by applying an initial weight derived from eigenimages: a new reference free method for single-particle analysis.

The single-particle analysis is a structure-determining method for electron microscope (EM) images which does not require crystal. In this method, the projections are picked up and averaged by the images of similar Euler angles to improve the signal to noise ratio, and then create a 3-D reconstruction. The selection of a large number of particles from the cryo-EM micrographs is a pre-requisite for obtaining a high resolution. To pickup a low-contrast cryo-EM protein image, we have recently found that a three-layer pyramidal-type neural network is successful in detecting such a faint image, which had been difficult to detect by other methods. The connection weights between the input and hidden layers, which work as a matching filter, have revealed that they reflect characters of the particle projections in the training data. The images stored in terms of the connection weights were complex, more similar to the eigenimages which are created by the principal component analysis of the learning images rather than to the averages of the particle projections. When we set the initial learning weights according to the eigenimages in advance, the learning period was able to be shortened to less than half the time of the NN whose initial weights had been set randomly. Further, the pickup accuracy increased from 90 to 98%, and a combination of the matching filters were found to work as an integrated matching filter there. The integrated filters were amazingly similar to averaged projections and can be used directly as references for further two-dimensional averaging. Therefore, this research also presents a brand-new reference-free method for single-particle analysis.

Animals↗