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
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
Support vector machine classification of multi-channel EEG traces: a new tool to analyze the brain response to morphine treatment.
The analgesic effect of morphine is highly individual, calling for objective methods to predict the subjective pain relief. Such methods might be based on alteration of brain response caused by morphine during painful stimuli. The study included 11 healthy volunteers subjectively quantifying perception of painful electrical stimulations in the esophagus. ⋯ The most discriminative feature was a decrease in the delta band (0.5 - 4 Hz) after morphine for volunteers with analgesic effect. Volunteers with no effect of morphine showed an increase in the delta band after drug administration. As only a proportion of patients benefit from opioid treatment, the new approach may help to identify non-responders and guide individualized tailored analgesic therapy.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2010
A novel continuous cardiac output monitor based on pulse wave transit time.
Monitoring cardiac output (CO) is important for the management of patient circulation in an operation room (OR) or intensive care unit (ICU). We assumed that the change in pulse wave transit time (PWTT) obtained from an electrocardiogram (ECG) and a pulse oximeter wave is correlated with the change in stroke volume (SV), from which CO is derived. The present study reports the verification of this hypothesis using a hemodynamic analysis theory and animal study. ⋯ Animal study was performed to verify the above-mentioned assumption. The correlation coefficient of PWTT and SV became r = -0.710 (p 〈 0.001), and a good correlation was admitted. It has been confirmed that accurate continuous CO and SV measurement is only possible by monitoring regular clinical parameters (ECG, SpO2, and NIBP).
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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.