Journal of clinical monitoring and computing
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J Clin Monit Comput · Jan 2002
The automatic lung parameter estimator (ALPE) system: non-invasive estimation of pulmonary gas exchange parameters in 10-15 minutes.
Clinical measurements of pulmonary gas exchange abnormalities might help prevent hypoxaemia and be useful in monitoring the effects of therapy. In clinical practice single parameters are often used to describe the abnormality e.g., the "effective shunt." A single parameter description is often insufficient, lumping the effects of several abnormalities. A more detailed picture can be obtained from experiments where FiO2 is varied and two parameters estimated. These experiments have previously taken 30-40 minutes to complete, making them inappropriate for routine clinical use. However with automation of data collection and parameter estimation, the experimental time can be reduced to 10-15 minutes. ⋯ The ALPE system provides quick, non-invasive estimation of pulmonary gas exchange parameters and may have several clinical applications. These include, monitoring pulmonary gas exchange abnormalities in the ICU, assessing post-operative gas exchange abnormalities, and titrating diuretic therapy in patients with heart failure.
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Computer simulation models can be extremely valuable for teaching and for understanding real world processes. The discipline of creating a model forces the investigator to carefully define each relationship and test the result. ⋯ Models are typically comprised of systems of differential equations and are solved by numerical integration with computer programs. Spreadsheets, simulation software and custom programs may be used to calculate the numerical solution, draw graphs and animate the result.
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The World Wide Web is increasingly important for medical education. Internet served pages may also be used on a local hard disk or CD-ROM without a network or server. This allows authors to reuse existing content and provide access to users without a network connection. ⋯ Issues include file names, relative links, directory names, default pages, server created content, image maps, other file types and embedded programming. With care, it is possible to create server based pages that can be copied directly to CD-ROM. In addition, Web pages on CD-ROM may reference Internet served pages to provide the best features of both methods.
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J Clin Monit Comput · Jan 2002
Bayesian probabilistic network modeling of remifentanil and propofol interaction on wakeup time after closed-loop controlled anesthesia.
Until now, the knowledge of combining anesthetics to obtain an adequate level of anesthesia and to economize wakeup time has been empirical and difficult to represent in quantitative models. Since there is no reason to expect that the effect of non-opioid and opioid anesthetics can be modeled in a simple linear manner, the use of a new computational approach with Bayesian belief network software is demonstrated. ⋯ Model building and evaluation with Bayesian networks does not depend on underlying linear relationships. Bayesian relationships represent true features of the represented data sample. Data may be sparse, uncertain, stochastic, or imprecise. Multiple platform software that is easy to use is increasingly available. Bayesian networks promise to be versatile tools for building valid, nonlinear, predictive instruments to further gain insight into the complex interaction of anesthetics.
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Inadvertent sleep episodes are a recognized complication of sleep deprivation. Although such events can be life threatening, no system currently exists to detect and prevent sleep onset. Because sleep shares electroencephalographic similarities with the anesthetized state, we hypothesized that the BIS monitor, a currently available EEG-based monitor of anesthetic depth, would detect the onset of physiologic sleep. To test our hypothesis, we monitored volunteers during the transition from waking to sleep. ⋯ Although variability in the BIS value marking sleep onset was noted, the BIS monitor detected all episodes of sleep onset in our testing regimen. We conclude that a threshold BIS value can be defined to allow the BIS monitor to detect sleep onset.