Computers and biomedical research, an international journal最新文献

The purpose of this study was to use a health information network and innovative technology to coordinate tuberculosis care. An innercity medical center, a local health department, and a home care nurse service in northern Manhattan were used. The organizations were linked with computer networks. An automated decision support system with a natural language processor was used to detect tuberculosis cases and report them to the health department, and to select patients for respiratory isolation. Educational materials were placed on the World Wide Web and a Web-based kiosk. Home care nurses were outfitted with wireless pen-based computers, and data were relayed to the medical center. Automated tuberculosis case reporting resulted in time savings but not improved accuracy. Automated rules resulted in significant improvements in respiratory isolation. Kiosk educational materials were well-used. Wireless computing led to better access to information for both nurses and physicians, but not to reduction of workload. The key success element was recognition of critical priorities. It is concluded that innovative technology can facilitate the coordination of clinical care, public health, and home care.

CHRONOMERGE is a database application that facilitates merging and display of multiple time-stamped data streams. Each stream is a table containing time-stamped values of one or more parameters (such as a panel of laboratory tests) for multiple patients, and is typically created by querying a clinical data respiratory. The data within a single stream therefore represent a pool of multiple time series. The merge operation is complex because of the numerous options to be considered, such as the granularity of the time interval for merge, and the choice of statistical aggregates. CHRONOMERGE combines multiple streams into a single stream based on patient and time, or time alone (if aggregates are to be computed across patients). It allows specification of various options through a graphical user interface and generates appropriate SQL code (or invokes procedural routines) to perform the merge. The resultant stream, or subsets of it, can then be displayed graphically. CHRONOMERGE is intended to facilitate the analysis of time-stamped data that have been extracted from repositories when standard tools (such as the time-series modules of statistics packages) are inadequate.

Intensity- or edge-based methods of segmentation are often insufficiently robust to be applied to images containing complex anatomical objects, such as those seen in high-resolution magnetic resonance imaging systems. Split-and-merge techniques attempt to overcome these difficulties by using homogeneity measures. Simple modifications to the basic 2D split-and-merge method, based on the principles of simulated annealing and controlled boundary elimination, are developed and discussed. Simulated annealing reduced the number of regions by 22% with a further reduction of 21% achieved through boundary elimination. Smoother regional boundaries are also produced. These methods are extended to true 3D and quantitatively compared with their 2D counterparts. The main advantage of 3D methods is that they produce segmented volumes by directly preserving the connectivity between slices, whereas in 2D, segments have to be grouped together in a post-split-and-merge process. Finally, the properties of the 3D approach are demonstrated by the automatic quantitation of brain ventricle volume, producing estimates to within 7% of validated manual methods.

For research in areas such as developmental biology and cancer, understanding the formation and regrowth of tissue is of great importance. Since complex system behavior makes it difficult to interpret dynamics and pattern formation of tissue growth, it is helpful to have a way to simulate the cell systems and test hypotheses about the mechanisms by which the system is responding. Computer “modeling experiments” can serve this purpose, as we show with an example of the small intestine epithelial cells' response to cytosine arabinoside. This example demonstrates that nonhomogeneities in the cell population can play an important role and emphasizes the need to use a modeling approach, like our spatial modeling, that addresses the differences within the tissue structure. These types of “modeling experiments” can guide researchers with further experiments and provide clues as to how complex cell proliferation behavior is linked to underlying molecular phenomena.

We have developed a distributed software package which allows neurophysiologists to have at hand a set of network and graphic tools in order to perform the analysis of data collected from the electrical activity of neurons, studied in the experimental laboratory. This system is built around three major components. First, the experimental data are processed in order to select specific data files from the networked computer and display the spike trains as raster dots. The second part selects one type of analysis (e.g., cross-correlation, out of a set of possible choices) after configuration of some specific parameters. Finally, the third component deals with the results of data analyses, which are selected and displayed on a multi-parameter graphic “ring binder.” We have used the Java programming language to implement this application. This recent object-oriented language offers platform-independent software tools including graphic package and standard network protocols.

The correct detection of the beginning of inspiration and expiration in the respiratory signals is an essential prerequisite for accurate lung function testing in newborns. Five algorithms for breath detection using pneumotachographically measured flow and volume signals were investigated with regard to the error rate. To compare and to evaluate the reliability of these algorithms 12 minimally and 12 severely disturbed flow and volume signals from spontaneously breathing newborns were used. With the exception of an algorithm based on Walsh-transformed signals, all algorithms work reliably (error rate <1.1%) if disturbances are minimal. In severely disturbed signals there is a great difference between the algorithms. The most robust algorithm tested (trigger of the flow signal with an additional plausibility check of the recognized breath) resulted in an error rate of <3.4%. Not all algorithms tested are suitable for real-time applications because they differ considerably in delay time for breath detection.

There is an ever-increasing demand for statistical information in health-care societies, and voluminous statistics are collected every year. When one needs information on survey results, it is generally not allowed to access the original data, and the summary tables are the most important source of information which can be shared. In order to store summary tables as a well-organized database and to perform flexible retrieval, a formal study has been made on the structure of summary tables. We introduce the notion of summary table scheme and define the operations called table transformation. A statistical database management system has been implemented based on the study. In our study, a statistical database is a collection of summary tables obtained from a survey. If a survey is conducted periodically, a database consists of all the tables obtained in the past. System utilities have been implemented for handling time-oriented information.

In this paper, multilevel techniques are introduced as a fast numerical method to compute 3-D potential field in nerve stimulation configurations. It is shown that with these techniques the computing time is reduced significantly compared to conventional methods. Consequently, these techniques greatly enhance the possibilities for parameter studies and electrode design. Following a general description of the model of nerve stimulation configurations, the basic principles of multilevel solvers for the numerical solution of partial differential equations are briefly summarized. Subsequently, some essential elements for successful application are discussed. Finally, results are presented for the potential field in a nerve bundle induced by tripolar stimulation with a cuff electrode surrounding part of the nerve.

New computationally intensive tools for medical survival analyses include recursive patitioning (also called CART) and artificial neural networks. A challenge that remains is to better understand the behavior of these techniques in effort to know when they will be effective tools. Theoretically they may overcome limitations of the traditional multivariable survival technique, the Cox proportional hazards regression model. Experiments were designed to test whether the new tools would, in practice, overcome these limitations. Two datasets in which theory suggests CART and the neural network should outperform the Cox model were selected. The first was a published leukemia dataset manipulated to have a strong interaction that CART should detect. The second was a published cirrhosis dataset with pronounced nonlinear effects that a neural network should fit. Repeated sampling of 50 training and testing subsets was applied to each technique. The concordance indexCwas calculated as a measure of predictive accuracy by each technique on the testing dataset. In the interaction dataset, CART outperformed Cox (P< 0.05) with aCimprovement of 0.1 (95% CI, 0.08 to 0.12). In the nonlinear dataset, the neural network outperformed the Cox model (P< 0.05), but by a very slight amount (0.015). As predicted by theory, CART and the neural network were able to overcome limitations of the Cox model. Experiments like these are important to increase our understanding of when one of these new techniques will outperform the standard Cox model. Further research is necessary to predict which technique will do besta prioriand to assess the magnitude of superiority.