Mobile, collaborative augmented reality using cloudlets

The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment

Positive blood culture detection in time series data using a BiLSTM network

The presence of bacteria or fungi in the bloodstream of patients is abnormal and can lead to life-threatening conditions. A computational model based on a bidirectional long short-term memory artificial neural network, is explored to assist doctors in the intensive care unit to predict whether examination of blood cultures of patients will return positive. As input it uses nine monitored clinical parameters, presented as time series data, collected from 2177 ICU admissions at the Ghent University Hospital. Our main goal is to determine if general machine learning methods and more specific, temporal models, can be used to create an early detection system. This preliminary research obtains an area of 71.95% under the precision recall curve, proving the potential of temporal neural networks in this context

Random survival forests for predicting the bed occupancy in the intensive care unit

Predicting the bed occupancy of an intensive care unit (ICU) is a daunting task. The uncertainty associated with the prognosis of critically ill patients and the random arrival of new patients can lead to capacity problems and the need for reactive measures. In this paper, we work towards a predictive model based on Random Survival Forests which can assist physicians in estimating the bed occupancy. As input data, we make use of the Sequential Organ Failure Assessment (SOFA) score collected and calculated from 4098 patients at two ICU units of Ghent University Hospital over a time period of four years. We compare the performance of our system with a baseline performance and a standard Random Forest regression approach. Our results indicate that Random Survival Forests can effectively be used to assist in the occupancy prediction problem. Furthermore, we show that a group based approach, such as Random Survival Forests, performs better compared to a setting in which the length of stay of a patient is individually assessed

Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks

Introduction: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsis, a severe immune response associated with an increased risk of organ failure and death. Problem statement The early clinical detection of a bloodstream infection is challenging but rapid targeted treatment, within the first place antimicrobials, substantially increases survival chances. As blood cultures require time to incubate, early clinical detection using physiological signals combined with indicative lab values is pivotal. Objective: In this work, a novel method is constructed and explored for the potential prediction of the outcome of a blood culture test. The approach is based on a temporal computational model which uses nine clinical parameters measured over time. Methodology: We use a bidirectional long short-term memory neural network, a type of recurrent neural network well suited for tasks where the time lag between a predictive event and outcome is unknown. Evaluation is performed using a novel high-quality database consisting of 2177 ICU admissions at the Ghent University Hospital located in Belgium. Results: The network achieves, on average, an area under the receiver operating characteristic curve of 0.99 and an area under the precision-recall curve of 0.82. In addition, our results show that predicting several hours upfront is possible with only a small decrease in predictive power. In this setting, it outperforms traditional non-temporal, machine learning models. Conclusion: Our proposed computational model accurately predicts the outcome of blood culture tests using nine clinical parameters. Moreover, it can be used in the ICU as an early warning system to detect patients at risk of blood stream infection