Sessions

Review of the potential of using machine learning for the integration of medical data.

This workshop is designed to provide a comprehensive overview of the EFMI accreditation process, its benefits and importance, key requirements, and criteria, and tips for a successful accreditation application. Aimed at universities and institutions looking to attain EFMI accreditation, this workshop will provide valuable insights and guidance. Our experienced speakers will share their expertise, personal experiences with their master’s accreditation, and answer any questions participants may have about the process.

Patients' remote monitoring platforms can be enhanced with intelligent recommendations and gamification functionalities to support their adherence to care plans. The current paper aims to present a methodology for creating personalized recommendations, which can be used to improve patient remote monitoring and care platforms. The current pilot system design is aimed to support patients by providing recommendations for Sleep, Physical Activity, BMI, Blood sugar, Mental Health, Heart Health, and Chronic Obstructive Pulmonary Disease aspects. The users, through the application, can select the types of recommendations they are interested in. Thus, personalized recommendations based on data obtained by the patients’ records anticipated to be a valuable and a safe approach for patient coaching. The paper discusses the main technical details and provides some initial results. 

Healthcare longitudinal data offers endless opportunities such as diagnostics, and prognostics, to care personalization and improving healthcare quality and efficiency. These can be achieved through Artificial Intelligence (AI), data science, and in specific machine learning (ML). However, lack of inscrutability of the AI/ML-based techniques has hampered pervasive AI in healthcare systems. In this talk, we will introduce graph-based approaches for collecting, storing, visualizing, analyzing, and modeling healthcare data, and discuss how they outperform the other available approaches. We will also discuss challenges around health data and how synthetic data can mitigate them.

With the fast progression in AI, its role in healthcare is bound to be ubiquitous in near future. However, creating AI software that is unbiased and trained ethically remains a challenge. In this session, we will evaluate different aspects of what it means for the AI to be biased and how we can evaluate it as a medical device software. Similarly, it is important to understand the fact that ethical AI is a two-way street. We will also look into how the society and the regulatory bodies should play a role for AI to be constructed in an ethical way. 

Women have been historically excluded from healthcare research and women’s health issues still receive inadequate attention, yet women live more years in poor health and with disability.Digital transformation and a rapidly growing global FemTech market provide new opportunities to transform the lives of millions of women globally and contribute to a substantial social impact and economic gains. However, research on FemTech that provide robust evidence on its value is lacking.In this seminar, based on our ongoing research, we will discuss opportunities and challenges of FemTech and why a rigorous research agenda with targeted research funding is needed. We will also address research methods that include multi-stakeholder partnerships, methods of intersectionality research to address health inequities, and co-creation models to engage women in the co-design of technologies to ensure person-centered outcomes.