Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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Conf Proc IEEE Eng Med Biol Soc · Jan 2010
Estimated venous return surface and cardiac output curve precisely predicts new hemodynamics after volume change.
In our extended Guyton's model, the ability of heart to pump blood is characterized by a cardiac output curve and the ability of vasculature to pool blood by a venous return surface. These intersect in a three-dimensional coordinate system at the operating right atrial pressure, left atrial pressure, and cardiac output. ⋯ Using the average values for two logarithmic function parameters, and for two slopes of a surface, we were able to estimate cardiac output curve and venous return surface. The estimated curve and surface predicted new hemodynamics after volume change precisely.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2010
Anaesthesia synchronization software: target controlled infusion system evaluation.
Target Controlled Infusion (TCI) systems are based in drug Pharmacokinetic (Pk) and Pharmacodynamic (Pd) models implemented in an algorithm to drive an infusion device. Several studies had compare manual titration of anesthesia and TCI system use; some studies evaluate the performance of the control algorithms for TCI systems, and a considerable number of studies assess the performance of Pk/Pd models implemented into TCI systems. ⋯ The goal of the current study was to assess the performance of the TCI system, Anaesthesia Synchronization Software (ASYS), on clinical set up to evaluate communication consistence (computer - infusion device) and controller performance in real time. These measures provided quantitative and qualitative evidences of software robustness and accuracy to be used at clinical environment.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2010
Statewide validation of a patient admissions prediction tool.
We validate a proprietary system to predict hospital emergency department presentations. A key advantage in planning health service delivery requirements and catering for the large numbers of people presenting to hospitals is the ability to predict their numbers. Year-ahead forecasts of daily hospital presentations were generated for 27 public hospitals in Queensland, Australia from five years of historic data. ⋯ Emergency Department presentations were found to be not random and can be predicted with an accuracy of around 90%. Highest accuracy was over weekends and summer months, and Public Holidays had the greatest variance in forecast accuracy. Forecasts for urban facilities were generally more accurate than regional (accuracy is related to sample size).
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Conf Proc IEEE Eng Med Biol Soc · Jan 2010
Symbolic learning supporting early diagnosis of melanoma.
We present a classification analysis of the pigmented skin lesion images taken in white light based on the inductive learning methods by Michalski (AQ). Those methods are developed for a computer system supporting the decision making process for early diagnosis of melanoma. ⋯ Classification performance with the wavelet features, although achieved with simple rules, is very high. Symbolic learning applied to our skin lesion data seems to outperform other classical machine learning methods, and is more comprehensive both in understanding, and in application of further improvements.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2010
Clinical TrialAssessment of the depth of anesthesia based on symbolic dynamics of the EEG.
Methodologies based on symbolic dynamics have successfully demonstrated to reflect the nonlinear behavior of biological signals. In the present study, symbolic dynamics was applied to the electroencephalogram (EEG) in order to describe the level of depth of anesthesia. ⋯ Words of three symbols were built from this symbolic series. The results obtained from the EEGs of 36 patients undergoing anesthesia showed that the probabilities of the word types were able to reflect the depth of anesthesia in a similar way to the auditory evoked potential index AAI, a commercial index.