Computational and mathematical methods in medicine
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Comput Math Methods Med · Jan 2014
A patient-specific airway branching model for mechanically ventilated patients.
Respiratory mechanics models have the potential to guide mechanical ventilation. Airway branching models (ABMs) were developed from classical fluid mechanics models but do not provide accurate models of in vivo behaviour. Hence, the ABM was improved to include patient-specific parameters and better model observed behaviour (ABMps). ⋯ The ABMps model allows the estimation of airway pressure drop at each bronchial generation with patient-specific physiological measurements and can be generated from data measured at the bedside. The distribution of patient-specific α values indicates that the overall ABM can be readily improved to better match observed data and capture patient condition.
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The present review describes and validates a new ratio "S" created for matching predictability and balance between TP and TN. Validity of S was studied in a three-step process as follows: (i) S was applied to the data of a past study predicting cardiac output response to fluid bolus from response to passive leg raise (PLR); (ii) S was comparatively analyzed with traditional ratios by modeling different 2 ∗ 2 contingency tables in 1000 hypothetical patients; (iii) precision of S was compared with other ratios by computing random fluctuations in the same patients. In comparison to other ratios, S performs better in predicting the cardiac response to fluid bolus and supports more directly the clinical conclusions. ⋯ When the proportion of true responses is high, S is the unique ratio that identifies the categorization that balances the proportion of TP and TN. The precision of S is close to that of CC. In conclusion, S should be considered for creating categories from quantitative variables; especially when matching predictability with balance between TP and TN is a concern.
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Comput Math Methods Med · Jan 2014
Gastroscopic image graph: application to noninvasive multitarget tracking under gastroscopy.
Gastroscopic examination is one of the most common methods for gastric disease diagnosis. In this paper, a multitarget tracking approach is proposed to assist endoscopists in identifying lesions under gastroscopy. This approach analyzes numerous preobserved gastroscopic images and constructs a gastroscopic image graph. ⋯ The clinical experimental results demonstrated that the accuracy at angularis, antral, and stomach body was 6.3 ± 2.4 mm, 7.6 ± 3.1 mm, and 7.9 ± 1.6 mm, respectively. The mean accuracy was 7.31 mm, average targeting time was 56 ms, and the P value was 0.032, which makes it an attractive candidate for clinical practice. Furthermore, this approach provides a significant reference for endoscopic target tracking of other soft tissue organs.
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Comput Math Methods Med · Jan 2014
Methodological framework for estimating the correlation dimension in HRV signals.
This paper presents a methodological framework for robust estimation of the correlation dimension in HRV signals. It includes (i) a fast algorithm for on-line computation of correlation sums; (ii) log-log curves fitting to a sigmoidal function for robust maximum slope estimation discarding the estimation according to fitting requirements; (iii) three different approaches for linear region slope estimation based on latter point; and (iv) exponential fitting for robust estimation of saturation level of slope series with increasing embedded dimension to finally obtain the correlation dimension estimate. ⋯ The three approaches are applied to HRV signals of pregnant women before spinal anesthesia for cesarean delivery in order to identify patients at risk for hypotension. D₂ keeps the 81% of accuracy previously described in the literature while D(2(⊥)) and D(2(max)) approaches reach 91% of accuracy in the same database.
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Comput Math Methods Med · Jan 2014
Optimal installation locations for automated external defibrillators in Taipei 7-Eleven stores: using GIS and a genetic algorithm with a new stirring operator.
Immediate treatment with an automated external defibrillator (AED) increases out-of-hospital cardiac arrest (OHCA) patient survival potential. While considerable attention has been given to determining optimal public AED locations, spatial and temporal factors such as time of day and distance from emergency medical services (EMSs) are understudied. Here we describe a geocomputational genetic algorithm with a new stirring operator (GANSO) that considers spatial and temporal cardiac arrest occurrence factors when assessing the feasibility of using Taipei 7-Eleven stores as installation locations for AEDs. ⋯ Our results suggest different AED allocation strategies involving convenience stores in urban settings. In commercial areas, such installations can compensate for temporal gaps in EMS locations when responding to nighttime OHCA incidents. In residential areas, store installations can compensate for long distances from fire stations, where AEDs are currently held in Taipei.