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Could artificial intelligence predict the outcomes of patients with TBI in real time?

#artificialintelligence

In a collaboration project between Helsinki University Hospital (HUS), Kuopio University Hospital and Turku University Hospital (all Finland), a team of researchers have presented the first artificial intelligence (AI) based algorithm that has the potential to assist in treating patients with severe TBI in intensive care units (ICUs). Patients with the most severe cases of TBI are usually treated in ICUs, however, despite the high-quality care, recent observational studies have reported mortality rates of approximately 30%. Patients who suffer from severe TBI are unconscious, therefore, it is a challenge to accurately monitor their condition. In ICUs many tens of variables, such as intercranial pressure and mean arterial pressure, are continuously monitored to assess the patient's condition. One variable alone could yield hundreds of thousands of data points per day, making it impossible for ICU staff to fully analyze.


Artificial intelligence-based algorithm for intensive care of traumatic brain injury

#artificialintelligence

A recent Finnish study published in Scientific Reports presents the first artificial intelligence (AI)-based algorithm designed for use in intensive care units for treating patients with severe traumatic brain injury. The project is a collaborative project between three Finnish university hospitals: Helsinki University Hospital, Kuopio University Hospital and Turku University Hospital. Traumatic brain injury (TBI) is a significant global cause of mortality and morbidity with an increasing incidence, especially in low-and-middle income countries. The most severe TBIs are treated in intensive care units (ICU), but in spite of the proper and high-quality care, about one in three patients dies. Patients that suffer from severe TBI are unconscious, which makes it challenging to accurately monitor the condition of the patient during intensive care.


Artificial intelligence-based algorithm for intensive care of traumatic brain injury

#artificialintelligence

A recent Finnish study published in Scientific Reports presents the first artificial intelligence (AI)-based algorithm designed for use in intensive care units for treating patients with severe traumatic brain injury. The project is a collaborative project between three Finnish university hospitals: Helsinki University Hospital, Kuopio University Hospital and Turku University Hospital. Traumatic brain injury (TBI) is a significant global cause of mortality and morbidity with an increasing incidence, especially in low-and-middle income countries. The most severe TBIs are treated in intensive care units (ICU), but in spite of the proper and high-quality care, about one in three patients dies. Patients that suffer from severe TBI are unconscious, which makes it challenging to accurately monitor the condition of the patient during intensive care.


AI and Ownership for Educators OEB

#artificialintelligence

Jari Multisilta is the director of Cicero Learning Network in the University of Helsinki, Finland and the professor of multimedia at the Tampere University of Technology, Information Technology at Pori, Finland. He did his doctoral thesis on hypermedia based learning environments for mathematics. Prof. Multisilta has studied learning and modern communication and information technologies and has taken part in several research projects on this area. Currently, his research interests include games for learning, mobile video storytelling, and mobile social video applications. Professor Multisilta has published over 100 international conference papers and journal articles on his research area.


A Crowdsourcing Framework for On-Device Federated Learning

arXiv.org Machine Learning

Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentive-based interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22 % gain in the offered reward. A preliminary version of this paper has been accepted at IEEE GLOBECOM [1]. Nguyen H. Tran is with the School of Computer Science, The University of Sydney, NSW 2006, Australia, email: nguyen.tran@sydney.edu.au. Mehdi Bennis is with the Center for Wireless Communications, University of Oulu, 90014 Oulu, Finland, email: mehdi.bennis@oulu.fi. I NTRODUCTION A. Background and motivation Recent years have admittedly witnessed a tremendous growth in the use of Machine Learning (ML) techniques and its applications in mobile devices. On one hand, according to International Data Corporation, the shipments of smartphones reached 3 billions in 2018 [2], which implies a large crowd of mobile users generating personalized data via the interaction with mobile applications, or with the use of inbuilt sensors (e.g., cameras, microphones and GPS) exploited efficiently by mobile crowdsensing paradigm (e.g., for indoor localization, traffic monitoring, navigation [3], [4], [5], [6]). On the other hand, mobile devices are getting empowered extensively with specialized hardware architectures and computing engines such as the CPU, GPU and DSP (e.g., energy efficient Qualcomm Hexagon V ector eXtensions on Snapdragon 835 [7]) for solving diverse machine learning problems. Gartner predicts that 80 percent of smartphones will have on-device AI capabilities by 2022.