WASHINGTON, DC (March 8, 2017)--Interventional radiologists at the University of California at Los Angeles (UCLA) are using technology found in self-driving cars to power a machine learning application that helps guide patients' interventional radiology care, according to research presented today at the Society of Interventional Radiology's 2017 Annual Scientific Meeting. The researchers used cutting-edge artificial intelligence to create a "chatbot" interventional radiologist that can automatically communicate with referring clinicians and quickly provide evidence-based answers to frequently asked questions. This allows the referring physician to provide real-time information to the patient about the next phase of treatment, or basic information about an interventional radiology treatment. "We theorized that artificial intelligence could be used in a low-cost, automated way in interventional radiology as a way to improve patient care," said Edward W. Lee, M.D., Ph.D., assistant professor of radiology at UCLA's David Geffen School of Medicine and one of the authors of the study. "Because artificial intelligence has already begun transforming many industries, it has great potential to also transform health care."
As one of the newest members in Artificial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been applied to a range of problems. These applications mainly belong to the field of anomaly detection. However, real-time detection, a new challenge to anomaly detection, requires improvement on the real-time capability of the DCA. To assess such capability, formal methods in the research of rea-time systems can be employed. The findings of the assessment can provide guideline for the future development of the algorithm. Therefore, in this paper we use an interval logic based method, named the Duration Calculus (DC), to specify a simplified single-cell model of the DCA. Based on the DC specifications with further induction, we find that each individual cell in the DCA can perform its function as a detector in real-time. Since the DCA can be seen as many such cells operating in parallel, it is potentially capable of performing real-time detection. However, the analysis process of the standard DCA constricts its real-time capability. As a result, we conclude that the analysis process of the standard DCA should be replaced by a real-time analysis component, which can perform periodic analysis for the purpose of real-time detection.
Merck plans to use a cloud-based software platform to better predict and prevent drug shortages, according to The Wall Street Journal. The platform, developed by healthcare software company TraceLink, will analyze in real time data from pharmacies, hospitals and wholesale distributors. By using analytics and machine learning, the software can improve predictions and help drugmakers better match drug demand. The software could also save drugmakers hundreds of millions of dollars annually by reducing waste and avoiding costs like expedited shipments, because it can track a drug's status at every step in the supply chain. The platform currently holds data on more than 6 billion drugs.
Princeton University neuroscientists joined forces with Intel computer scientists to map the human mind in real time, developing the next generation in brain imaging analysis. In a lab at the Princeton Neuroscience Institute, test subjects look at pictures, watch movies and listen to The Moth Radio Hour as scientists track their brain activity via functional magnetic resonance imaging (fMRI). The researchers' goal: to read their subjects' minds in real time, as they are thinking, feeling and reacting to the stimuli. This task would have been impossible just a few years ago. Reading a single scan was time consuming -- reading a multitude of scans meant blowing out a system's storage and processing capabilities.
Cellnovo Group (Paris:CLNV), a medical technology company marketing the first mobile, connected, all-in-one diabetes management system, announces that it has been selected to participate in a project, funded by the European Commission's Horizon 2020 programme, aimed at investigating new technologies to help improve the lives of people with Type 1 diabetes. The project, named PEPPER (Patient Empowerment through Predictive Personalised decision support), has a budget of nearly EUR 4 million and brings together leading UK and European universities and companies to research and develop technology that will help to improve the self-management of people with Type 1 diabetes. Researchers working on the project will use Cellnovo's diabetes management system to create a personalised decision support system that will make predictions based on real-time data in order to empower individuals to self-manage their condition. The design of the system will involve patients, clinicians and carers at every stage to ensure that it meets user needs. Sophie Baratte, Chief Executive Officer of Cellnovo, commented: "We are delighted to be participating in PEPPER, which we believe is a strong endorsement of our proprietary technology.