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.
For more information, please visit the following link: https://sites.google.com/view/ainurse23/home/
The purpose of XAI-Healthcare 2023 event is to provide a place for intensive discussion on all aspects of eXplainable Artificial Intelligence (XAI) in the medical and healthcare field. This should result in cross-fertilization among research on Machine Learning, Decision Support Systems, Natural Language, Human-Computer Interaction, and Healthcare sciences. This meeting will also provide attendees with an opportunity to learn more on the progress of XAI in healthcare and to share their own perspectives. The panel discussion will provide participants with the insights on current developments and challenges from the researchers working in this fast-developing field. We expect the contributions received to describe explanation methods, AI techniques and a targeted healthcare problem. Some examples are provided below for guidance, but the list of topics is not limited to these specific methods, techniques and problems.
For more information, please visit the following link: https://www.um.es/medailab/events/XAI-Healthcare/
Knowledge representation in health care is a productive area of theories, standards, models, systems, and applications, with an active community of researchers and developers with no dedicated international specific event for this community to meet. Since 2009, the KR4HC workshop has been organized year after year (with the exception of 2020 and 2022) with a great level of participation, excellent quality of the works presented, and top quality publications of the best selected papers as Springer LNCS series books. As it was observed in the previous editions of KR4HC, we expect that this new thirteenth edition of KR4HC could bring together a part of the KR in medicine community to present and discuss new ideas and technologies, as well as, to serve as a platform to discuss the progress of the field. Due to the growing interest of mixing machine learning and knowledge representation technologies to address new biomedical challenges, KR4HC 2023 will also welcome quality works on the challenging combination of machine learning technology and KR systems.
For more information, please visit the following link: https://sites.google.com/view/kr4hc2023/home
The increasing amount of health-related data pose unprecedented opportunities for mining previously unknown knowledge with semantics-powered data mining and analytical methods. However, due to the heterogeneity of different data sources ranging from electronic health records (EHRs) to environmental exposures, from social determinants of health to social media, it is challenging to exploit and integrate multiple sources to solve real-world problems. Biomedical ontologies and semantics-powered analytical methods are promising solutions. The goal of the 7th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2023) is to bring people in the fields of ontologies, knowledge representation, knowledge management, data mining, and data analytics to discuss a wide range of innovative methods and applications that address problems in healthcare, biomedicine, public health, and clinical research with biomedical, clinical, behavioral, and social web data. We are inviting original research submissions as well as work-in-progress to the 7th International Workshop on Semantics-Powered Health Data Analytics (SEPDA 2023).
For more information, please visit the following link: http://semantics-powered.org/sepda2023.html
The International Workshop on Process Mining Applications for Healthcare 2023 (PM4H23) provides a high-quality forum for interdisciplinary researchers and practitioners to exchange research findings and ideas on data-driven process analysis techniques and practices in healthcare. PM4H research includes a variety of topics ranging from process mining techniques adapted for healthcare processes, to practical issues related to the implementation of PM4H methodologies in healthcare organizations. During the 1st edition of our workshop at AIME, we aim to bring together researchers and practitioners in a spirit of collaboration and co-creation. In this way, we have the ambition to move PM4H research and practice forward, taking into account the distinguishing characteristics and challenges of the healthcare domain which were recently published in the Journal of Biomedical Informatics (https://doi.org/10.1016/j.jbi.2022.103994). This workshop is an initiative of the Process-Oriented Data Science for Healthcare Alliance, which is a chapter within the IEEE Task Force on Process Mining.
For more information, please visit the following link: http://personales.upv.es/carferll/AIME23/PM4H-cfp.html
The reliable prediction of outcomes from disease and treatment is becoming increasingly important in the delivery and organization of health care. The learning objective of this tutorial is to understand the elements underlying predictive performance and to show how to quantitatively assess the performance of prediction models. In particular, I address different categories of performance measures (including calibration, sharpness, resolution, and discrimination) and valid methods (including bootstrapping and cross validation) for obtaining performance assessments. I will also address the important difference between prediction and causal models, and how prediction models can still play a part in causality. The focus of the tutorial is on conceptual frameworks. Attention will be paid to the various choices in the design of model evaluation procedures, and the relationship between model evaluation and the purposes for which a model has been built. All methods are illustrated with real-world examples.
For more information, please visit the following link: https://sites.google.com/view/predmod/
pMineR & pMinShiny Tutorial
pMineR is an R library specifically designed to support Process Analysts to work in the clinical domain, providing Process Discovery algorithms, tools for Conformance Checking, Trace and Event Log analysis and methods for representing and working with Clinical Guidelines, Consensus flow, Clinical Protocols, etc.. pMinShiny is a Shiny-based Graphical User Interface designed to create a comfortable environment for quick data exploration using pMineR. This Tutorial is designed to provide a general overview on pMineR, and introduce the participants to the main modules, by short hands-on sessions, to cope with Process Discovery, Conformance Checking and Statistical Analysis of paths, events and timing. The Tutorial will also exploit invited speakers to present their experience on real-world data analysis and open projects based on pMineR. Finally, an open round-table will be the opportunity to share opinions, ideas about the topic or a further evolution of the libraries.
For more information, please visit the following link: http://www.pminer.info/progetti/pMineRTutorialWebsite/pMineRTutorialMainPage.php
Mining and multimodal learning from complex medical data
Recent advances in the areas of artificial intelligence and machine learning and their application to knowledge discovery from medical data sources has been receiving increasing attention over the past several years. At the same time, the adoption of Electronic Health Records
(EHRs) together with the availability of patient self-management and empowerment technologies give rise to new forms of health-related data sources of multi-modality and high complexity requiring novel data representation and analytics workflows. Particular challenges that arise include the representation of the involved complex feature spaces, dealing with data sparsity and missingness, exploiting the temporality nature of the data sources, and maintaining good trade-offs between model performance and explainability.
In this tutorial, we focus on data variables of sequential nature related to heath. We emphasize the importance of mining and knowledge discovery from these data sources in the context of research questions posed by medical researchers and clinicians. We further elaborate on how the integration and fusion of such data sources of heterogeneous nature can result in improved model performance in terms of predictive power, reliability, and scalability. Furthermore, we discuss the utility and usability of time series forecasting methods when dealing with sequential data variables collected in the hospital setting (e.g., in the Intensive Care Unit). Finally, we elaborate on the need for explainable machine learning models and their applicability in medical decision making problems that require trustworthy recommendations.
For more information, please visit the following link: https://www.kmd.ovgu.de/aime_tutorial_2023.html