[PlanetKR] Call for Participation: 20th International Conference on Artificial Intelligence in Medicine (AIME 2022)

William Van Woensel William.Van.Woensel at Dal.Ca
Thu Jun 2 13:39:23 UTC 2022 

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Call for Participation

AIME 2022: the 20th International Conference on Artificial Intelligence in Medicine
Dalhousie University, Nova Scotia, Canada, June 14-17 2022

Full program: https://aime22.aimedicine.info/index.php/program/program-details
Registration: https://aime22.aimedicine.info/index.php/register
Accommodations: https://aime22.aimedicine.info/index.php/accommodations
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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.

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.

AIME 2022 will include invited speakers, long and short presentations, poster sessions, demos, tutorials, workshops and a doctoral consortium.

The scope of the conference includes the following areas:
    Machine learning and big data analytics,
    Knowledge discovery and data mining,
    Biomedical ontologies and terminologies,
    Biomedical knowledge acquisition and representation,
    Knowledge-based reasoning in biomedicine,
    Natural language processing,
    Biomedical image processing,
    Document classification and information retrieval,
    Bayesian networks, fuzzy logic and reasoning under uncertainty,
    Temporal and spatial representation and reasoning,
    Healthcare processes and workflow management,
    Computerized clinical practice guidelines (CPGs) and protocols,
    Signal processing,
    Visual analytics in biomedicine,
    Clinical decision support systems (CDSSs),
    Patient engagement support (personal healthcare record),
    Explainable AI (XAI) for health,
    Precision medicine and health,
    AI solutions for ambient assisted living, telemedicine, and e-health.


*** Invited Speakers

Bo Wang: Opportunities and challenges of artificial intelligence for organ transplantation

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.

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.


David L. Buckeridge: Translating AI into Practice in Healthcare – Opportunities, Challenges, and Possible Solutions

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.

In the presentation, I will present this landscape and identify possible solutions, drawing on examples from Canada and other countries.


*** Tutorials

- Using Machine Learning on mHealth-based Data Sources
(https://aime22.aimedicine.info/index.php/tutorials-workshops#ml-mhealth)

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.


- End-user Development of Mobile AI-based Clinical Apps using Punya (PUNYA2022)
(https://aime22.aimedicine.info/index.php/tutorials-workshops#punya)

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.


- Machine learning for complex medical temporal sequences
(https://aime22.aimedicine.info/index.php/tutorials-workshops#ml-temp)

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.


- Data Science for Starters: How to Train and be Trained
(https://aime22.aimedicine.info/index.php/tutorials-workshops#data-science)

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.


*** Workshops

1st Workshop on Artificial Intelligence in Nursing: Advances, Methods and Path Forward (AINurse22)
(https://aime22.aimedicine.info/index.php/tutorials-workshops#ainurse)

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.


*** Organization

- General Chairs:
Syed Sibte Raza Abidi, Dalhousie University, Faculty of Computer Science, Halifax NS, Canada
Martin Michalowski, University of Minnesota, Minneapolis MN USA

- Scientific Program Chair:
Wojtek Michalowski, Telfer School of Management, University of Ottawa, Ottawa ON, Canada

- Doctoral Consortium Chair:
Arianna Dagliati, University of Pavia, Italy

- Tutorial Chair:
Enea Parimbelli, University of Pavia, Italy

- Workshop Chair:
Jose M. Juarez, University of Murcia, Spain

- Application Demonstration Chair:
William Van Woensel, Dalhousie University, Faculty of Computer Science, Halifax NS, Canada

- Local Organization Chairs:
Syed Sibte Raza Abidi, Dalhousie University, Faculty of Computer Science, Halifax NS, Canada
Samina Abidi, Dalhousie University, Faculty of Medicine, Halifax NS, Canada
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