In this paper, the WIPI-to-iOS automatic mobile game converter was designed to automatically translate mobile game contents from the WIPI platform to the iOS platform for smart phones. Through the WIPI-to-iOS converter, resources such as images and sounds can be converted, APIs can be converted using a platform mapping engine with wrapper functions. These and all other content conversion functions were examined. Test results indicate that the graphics, image output, sound output, and other functions of converted iOS platform games were equivalent to those of the WIPI platform games before conversion.
Background: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury (TBI). Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals. A machine learning model for the elimination of artifactual events shows promising results for improving signal quality.However, the actual impact of the improvements on the performance of the clinical parameters after the elimination of the artifacts is not well studied.
Methods:The arterial blood pressure of 99 subjects with TBI was continuously measured for five consecutive days, beginning on the day of admission. The machine learning deep belief network (DBN) was constructed to automatically identify and remove false incidences of hypotension, hypertension, bradycardia, tachycardia, and alterations in cerebral perfusion pressure (CPP).
Results:The prevalences of hypotension and tachycardia were significantly reduced by 47.5% and 13.1%, respectively, after suppressing false incidents (p = 0.01). Hypotension was particularly effective at predicting outcome favorability and mortality after artifact elimination (p = 0.015 and 0.027, respectively). In addition, increased CPP was also statistically significant in predicting outcomes (p = 0.02).
Conclusions:The prevalence of false incidents due to signal artifacts can be significantly reduced using machine learning. Some clinical events, such as hypotension and alterations in CPP, gain particularly high predictive capacity for patient outcomes after artifacts are eliminated from physiological signals.
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