<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
</head>
<body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">
<div class="">===================================================</div>
<div class="">Call for Participation</div>
<div class=""><br class="">
</div>
<div class="">AIME 2022: the 20th International Conference on Artificial Intelligence in Medicine</div>
<div class="">Dalhousie University, Nova Scotia, Canada, June 14-17 2022</div>
<div class=""><br class="">
</div>
<div class="">Full program: <a href="https://aime22.aimedicine.info/index.php/program/program-details" class="">https://aime22.aimedicine.info/index.php/program/program-details</a> </div>
<div class="">Registration: <a href="https://aime22.aimedicine.info/index.php/register" class="">https://aime22.aimedicine.info/index.php/register</a> </div>
<div class="">Accommodations: <a href="https://aime22.aimedicine.info/index.php/accommodations" class="">https://aime22.aimedicine.info/index.php/accommodations</a> </div>
<div class="">===================================================</div>
<div class=""><br class="">
</div>
<div class="">We cordially invite you to participate in the AIME 2022 conference, which will be hosted in person at Dalhousie University in Halifax, Canada from June 14-17 2022. </div>
<div class=""><br class="">
</div>
<div class="">The objective of the the AIME 2022 conference is to promote high-quality research on the development of theory, methods, systems, and applications of Artificial Intelligence in biomedicine, including the application of AI approaches in biomedical
informatics, healthcare organisation, and molecular medicine.</div>
<div class=""><br class="">
</div>
<div class="">AIME 2022 will include invited speakers, long and short presentations, poster sessions, demos, tutorials, workshops and a doctoral consortium. </div>
<div class=""><br class="">
</div>
<div class="">The scope of the conference includes the following areas:</div>
<div class=""> Machine learning and big data analytics,</div>
<div class=""> Knowledge discovery and data mining,</div>
<div class=""> Biomedical ontologies and terminologies,</div>
<div class=""> Biomedical knowledge acquisition and representation,</div>
<div class=""> Knowledge-based reasoning in biomedicine,</div>
<div class=""> Natural language processing,</div>
<div class=""> Biomedical image processing,</div>
<div class=""> Document classification and information retrieval,</div>
<div class=""> Bayesian networks, fuzzy logic and reasoning under uncertainty,</div>
<div class=""> Temporal and spatial representation and reasoning,</div>
<div class=""> Healthcare processes and workflow management,</div>
<div class=""> Computerized clinical practice guidelines (CPGs) and protocols,</div>
<div class=""> Signal processing,</div>
<div class=""> Visual analytics in biomedicine,</div>
<div class=""> Clinical decision support systems (CDSSs),</div>
<div class=""> Patient engagement support (personal healthcare record),</div>
<div class=""> Explainable AI (XAI) for health,</div>
<div class=""> Precision medicine and health,</div>
<div class=""> AI solutions for ambient assisted living, telemedicine, and e-health.</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class="">*** Invited Speakers</div>
<div class=""><br class="">
</div>
<div class="">Bo Wang: Opportunities and challenges of artificial intelligence for organ transplantation</div>
<div class=""><br class="">
</div>
<div class="">Organ transplantation is a life-saving procedure for patients with end-stage organ disease. A successful transplant depends on thorough pre-transplant evaluation and preparation, accurate identification of a suitable donor, and detailed follow-ups
with post-transplant monitoring. There is no medical field where more variables are intercalated than in the case of a transplant patient. The embracement and integration of artificial intelligence with big data in such a setting are fundamental to improving
access to transplantation, quality of life, and patient outcomes.</div>
<div class=""><br class="">
</div>
<div class="">In this talk, I will discuss the opportunities and challenges of AI for organ transplantation. Specifically, I will first show a machine-learning model trained on hundreds of ex vivo lung perfusion (EVLP) cases, to accurately predict lung transplantation
outcomes. Second, I will present a novel AI system that can seamlessly track longitudinal follow-up data to identify patients at increased risk for complications after liver transplantation. Last, current challenges to implementing AI in transplantation will
be discussed.</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class="">David L. Buckeridge: Translating AI into Practice in Healthcare – Opportunities, Challenges, and Possible Solutions</div>
<div class=""><br class="">
</div>
<div class="">The potential for AI in healthcare has been evident for decades and the opportunity has grown with increasing volumes of data and advances in AI, particularly in machine learning. Despite this potential, the translation of AI-based innovations
into healthcare practice has faced challenges along the development and implementation pipeline. These include access to data, technology debt in clinical practice, and AI expertise in healthcare systems.</div>
<div class=""><br class="">
</div>
<div class="">In the presentation, I will present this landscape and identify possible solutions, drawing on examples from Canada and other countries.</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class="">*** Tutorials</div>
<div class=""><br class="">
</div>
<div class="">- Using Machine Learning on mHealth-based Data Sources</div>
<div class="">(<a href="https://aime22.aimedicine.info/index.php/tutorials-workshops#ml-mhealth" class="">https://aime22.aimedicine.info/index.php/tutorials-workshops#ml-mhealth</a>)</div>
<div class=""><br class="">
</div>
<div class="">In this tutorial, at first, participants will be provided with relevant aspects and issues of the mobile application engineering side (e.g., offline vs online features), the created data sources (e.g., data collection procedure), and relevant
related technical (e.g., proper APIs) as well as medical aspects (e.g., regulatory aspects) of mHealth apps. In the second part of the tutorial, the participants will get practical insights into conducted machine learning analyses of long-running developed
mHealth apps. This includes a detailed discussion with tangible results on the opportunities as well as the shortcomings when using machine learning on mHealth data.</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class="">- End-user Development of Mobile AI-based Clinical Apps using Punya (PUNYA2022)</div>
<div class="">(<a href="https://aime22.aimedicine.info/index.php/tutorials-workshops#punya" class="">https://aime22.aimedicine.info/index.php/tutorials-workshops#punya</a>)</div>
<div class=""><br class="">
</div>
<div class="">This tutorial provides a gentle introduction for both non-technical and technical attendees to develop AI-enabled mobile apps for healthcare and life sciences, using an intuitive and visual online platform called Punya (based on MIT App Inventor).
We foresee Punya being utilized for the exploratory development and prototyping of mobile health apps, without requiring specialized development skills and thus reducing the effort and cost involved. Punya features components for Knowledge Representation and
Reasoning (KRR) as well as Machine Learning (ML). During the tutorial, the attendees will develop a smart health app outfitted with KRR and ML features.</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class="">- Machine learning for complex medical temporal sequences</div>
<div class="">(<a href="https://aime22.aimedicine.info/index.php/tutorials-workshops#ml-temp" class="">https://aime22.aimedicine.info/index.php/tutorials-workshops#ml-temp</a>)</div>
<div class=""><br class="">
</div>
<div class="">This tutorial introduces the concept of learning on medical temporal sequences and elaborates on challenges and learning methods for clinical data and for mHealth data.</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class="">- Data Science for Starters: How to Train and be Trained</div>
<div class="">(<a href="https://aime22.aimedicine.info/index.php/tutorials-workshops#data-science" class="">https://aime22.aimedicine.info/index.php/tutorials-workshops#data-science</a>)</div>
<div class=""><br class="">
</div>
<div class="">Given the right tool, it may take only a few hours to familiarize anyone with data science. Data science, machine learning, and artificial intelligence are drivers of change in all fields of science, including biomedicine. But only a few professionals
understand the essential concepts behind data science, and even fewer engage in building models using their data. The tutorial will explain how anybody can learn about the crucial mechanics behind data science and machine learning in the workshops that take
only a few hours. After the tutorial, it should be evident that after a short training of this kind, the professionals can gain enough intuition about data science to recognize opportunities that this field can offer and actively engage in data science projects.
Besides good mentors and an encouraging working environment, the right tool is critical for such training.</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class="">*** Workshops</div>
<div class=""><br class="">
</div>
<div class="">1st Workshop on Artificial Intelligence in Nursing: Advances, Methods and Path Forward (AINurse22)</div>
<div class="">(<a href="https://aime22.aimedicine.info/index.php/tutorials-workshops#ainurse" class="">https://aime22.aimedicine.info/index.php/tutorials-workshops#ainurse</a>)</div>
<div class=""><br class="">
</div>
<div class="">This workshop, organized by the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, will focus on AI in nursing and provide a platform for discussions about the recent advances, cutting edge AI methods, and chart a path forward
for nursing AI. These goals will be achieved through a combination of presentation types, including paper presentations, invited talks, panels, demos, and general discussion. Intended workshop participants are individuals involved in developing and applying
AI for nursing, including those with clinical (e.g., nursing, medicine), technical (e.g., machine learning, computer/data science) and human factors (e.g., visualization and UI/UX) backgrounds. AIME participants who are focusing on using AI technologies based
on nursing data or intended to be used by nurses will benefit from this workshop by learning about current AI applications and cutting-edge methods. The workshop will also chart AI research areas that require further development to advance patient outcomes.</div>
<div class=""><br class="">
</div>
<div class=""><br class="">
</div>
<div class="">*** Organization</div>
<div class=""><br class="">
</div>
<div class="">- General Chairs:</div>
<div class="">Syed Sibte Raza Abidi, Dalhousie University, Faculty of Computer Science, Halifax NS, Canada</div>
<div class="">Martin Michalowski, University of Minnesota, Minneapolis MN USA</div>
<div class=""><br class="">
</div>
<div class="">- Scientific Program Chair: </div>
<div class="">Wojtek Michalowski, Telfer School of Management, University of Ottawa, Ottawa ON, Canada</div>
<div class=""><br class="">
</div>
<div class="">- Doctoral Consortium Chair: </div>
<div class="">Arianna Dagliati, University of Pavia, Italy</div>
<div class=""><br class="">
</div>
<div class="">- Tutorial Chair: </div>
<div class="">Enea Parimbelli, University of Pavia, Italy</div>
<div class=""><br class="">
</div>
<div class="">- Workshop Chair: </div>
<div class="">Jose M. Juarez, University of Murcia, Spain</div>
<div class=""><br class="">
</div>
<div class="">- Application Demonstration Chair: </div>
<div class="">William Van Woensel, Dalhousie University, Faculty of Computer Science, Halifax NS, Canada</div>
<div class=""><br class="">
</div>
<div class="">- Local Organization Chairs:</div>
<div class="">Syed Sibte Raza Abidi, Dalhousie University, Faculty of Computer Science, Halifax NS, Canada</div>
<div class="">Samina Abidi, Dalhousie University, Faculty of Medicine, Halifax NS, Canada</div>
</body>
</html>