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      AI to enhance interactive simulation-based training in resuscitation medicine

      1 , 2 , 2 , 3 , 3 , 1

      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Digital simulation, Resuscitation medicine, Artificial intelligence, Reinforcement learning

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          When patients become acutely unwell, the ability of frontline healthcare professionals to act quickly and effectively can mean the difference between life and death. High-fidelity simulation is the gold standard by which medics acquire and maintain key resuscitation skills, but the resource-intensive nature of current, face-to-face training limits access to training and allows “skills fade” to creep in. We propose that human computer interaction-based simulations augmented by artificial intelligence could provide a cost-effective alternative to traditional training and allow clinicians much greater access to training. This paper is mostly an in-depth discussion; however, we also present a 3D simulator for resuscitation skills training which we developed using the Unity games physics engine.

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          Most cited references 22

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          Machine learning and radiology.

          In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.
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            Prospective controlled trial of effect of medical emergency team on postoperative morbidity and mortality rates.

            To determine whether the introduction of an intensive care unit-based medical emergency team, responding to hospital-wide preset criteria of physiologic instability, would decrease the rate of predefined adverse outcomes in patients having major surgery. Prospective, controlled before-and-after trial. University-affiliated hospital. Consecutive patients admitted to hospital for major surgery during a 4-month control phase and during a 4-month intervention phase. Introduction of a hospital-wide intensive care unit-based medical emergency team to evaluate and treat in-patients deemed at risk of developing an adverse outcome by nursing, paramedical, and/or medical staff. We measured incidence of serious adverse events, mortality after major surgery, and mean duration of hospital stay. There were 1,369 operations in 1,116 patients during the control period and 1,313 in 1,067 patients during the medical emergency team intervention period. In the control period, there were 336 adverse outcomes in 190 patients (301 outcomes/1,000 surgical admissions), which decreased to 136 in 105 patients (127 outcomes/1,000 surgical admissions) during the intervention period (relative risk reduction, 57.8%; p <.0001). These changes were due to significant decreases in the number of cases of respiratory failure (relative risk reduction, 79.1%; p <.0001), stroke (relative risk reduction, 78.2%; p =.0026), severe sepsis (relative risk reduction, 74.3%; p =.0044), and acute renal failure requiring renal replacement therapy (relative risk reduction, 88.5%; p <.0001). Emergency intensive care unit admissions were also reduced (relative risk reduction, 44.4%; p =.001). The introduction of the medical emergency team was also associated with a significant decrease in the number of postoperative deaths (relative risk reduction, 36.6%; p =.0178). Duration of hospital stay after major surgery decreased from a mean of 23.8 days to 19.8 days (p =.0092). The introduction of an intensive care unit-based medical emergency team in a teaching hospital was associated with a reduced incidence of postoperative adverse outcomes, postoperative mortality rate, and mean duration of hospital stay.
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              Part 4: Advanced Life Support: 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations.

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                Author and article information

                Contributors
                Conference
                July 2018
                July 2018
                : 1-4
                Affiliations
                [1 ]Cardiovascular Research Unit, Craigavon Area Hospital, 68 Lurgan Rd, Portadown, BT63 5QQ
                [2 ]Computer Science Research Institute, Ulster University, Shore Road, Newtonabbey, BT37 0QB
                [3 ]Nanotechnology and Integrated BioEngineering Centre, Ulster University, Shore Road, Newtonabbey, BT37 0QB
                Article
                10.14236/ewic/HCI2018.64
                © Brisk et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                HCI
                32
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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