Use of healthcare data and AI: The need for collaboration between data scientists and healthcare professionals
In today’s rapidly evolving healthcare landscape, the convergence of big data, machine learning (ML), and epidemiology is paving the way for groundbreaking advancements in patient care and drug safety. This session at the D3A 2.0 conference in Nyborg will explore the transformative potential of integrating these disciplines to foster improvements in healthcare outcomes. In this session we will discuss the pivotal role of data science in modern healthcare, highlighting how machine learning and epidemiological insights can be harnessed to enhance patient care.
We will examine the types of healthcare data available and the potential of AI to analyze and interpret this data for the benefit of patients, healthcare professionals, and healthcare systems. The session will also address the challenges and opportunities associated with big data in healthcare, including data quality, privacy concerns, ethics, and the need for interdisciplinary collaboration.
The session will feature representatives from the faculty of Health Science and the department of Computer Science. Each panelist will present a talk, focusing on the use of healthcare data, collaboration across disciplines, and eagerness to create solutions to real-world problems. Case studies, use-cases, and real-world examples will be shared to demonstrate the practical applications and impact of this multidisciplinary approach.
Program
Leveraging Deep Learning: Insights from EHR and MRI Data in Retrospective Observational Studies
Ph.D. Fellow Kiril Vadimovic Klein and Postdoc Stefano Cerri, Department of Computer Science, University of Copenhagen and Copenhagen Research Centre for Biological and Precision Psychiatry
Using big data and epidemiological science to detect unknown drug effects
Professor Jesper Hallas, The Faculty of Health Sciences, University of Southern Denmark
AI in MRI – generalizability, bias and fairness
Associate Professor Melanie Ganz-Benjaminsen, Department of Computer Science, University of Copenhagen
Post-presentations, the panel will engage in a moderated discussion, open for audience participation, to debate challenges, best practices, and future directions. The session aims to foster a collaborative atmosphere, bringing together experts, academicians, and practitioners from the fields of ML, epidemiology, and pharmacology. This interdisciplinary dialogue will encourage new collaborations and contribute meaningful advancements to each participating domain.
Main activities
The 90-minutes session will feature three presentations, followed by a panel discussion moderated by Associate Professor and Senior Hospital Physician Espen Solem, PI in the PHAIR project (www.phair.dk). There will be dedicated time for audience questions.