Lecture Series: Medical Information Sciences
Medical Information Sciences
The future of medical research and healthcare is personalized, digitized, and data-driven. The provision, analysis, and interpretation of this data rely on interdisciplinary collaborations. In this way, the foundations for future medical progress are created at the interface of medicine and computer science.
One response to this development is the establishment of the Medical Information Sciences research and study focus at the Augsburg location. In the winter semester of 22/23, a lecture series of the same name was held for the first time, which addresses current questions from science and provides insights into corresponding research areas of industry.
The events will take place in the summer semester of 2023 on Tuesdays at 5:30 pm in the Large and Small Lecture Halls of the University Hospital, as well as in Lecture Hall N2045 at FAI.
Additionally, the events will be live-streamed at the following remote locations:
- Lectures at the University Hospital in Lecture Hall N2045 at FAI
- Lectures in Lecture Hall N2045 at IDM Meeting Room (Gutenbergstr. 7, 86356 Neusäß - 1st floor, room 01.B001)
The lectures are aimed at an interested professional audience and will be held in English.
At the "Bayerische Landesärztekammer" (BLÄK), 2 credit points within the context of Continuing Medical Education (CME) are requested for each individual appointment. Interested doctors can register for participation in advance by sending a message to office.bioinf@informatik.uni-augsburg.de, which will be confirmed after the respective appointment.
In addition, prior to the lectures, there will be an opportunity to attend a personal consultation with the speaker of the day to discuss scientific questions, topics or cooperation opportunities. If you are interested, please register in advance by sending a message to office.bioinf@informatik.uni-augsburg.de.
Below you will find the schedule for the lecture series with further information on each individual lecture:
Veranstaltungsort: Großer Hörsaal (2.OG, Raum 047, Universitätsklinikum)
Abstract
I will talk about the application of automated, quantitative image analysis in combination with machine learning and artificial intelligence in radiology. These techniques have the potential to revolutionize clinical routine. I will illustrate how machine learning and artificial intelligence can be used in radiology to solve typical problems, using examples from my research group.
However, there are also challenges and difficulties that can arise when implementing these technologies into clinical routines, such as data issues, and data and computing infrastructure. I will also highlight potential solutions and strategies to address these challenges.
I hope that after my talk, you will have a better understanding of how clinical data science can improve radiological diagnostics and thus have a positive impact on patient care.
Referent: Prof. Dr. Michael Ingrisch (Clinical Data Science in Radiology, LMU Klinikum München)
Kurzbiographie
I am leading the group for Clinical Data Science at the Department of Radiology. We employ advanced statistics, machine learning and computer vision techniques in the context of clinical radiology to enable fast and precise AI-supported diagnosis and prognostication. Open science and reproducible research in this field is highly relevant, especially with deep learning or machine learning. While it is easy to share analyses and code, the sensitive nature of medical images and associated clinical data poses challenges with respect of public data sharing. I believe the Open Science Center provides the ideal framework to address these challenges.
Current position: W2 Professor for Clinical Data Science in Radiology, Department of Radiology, University hospital, LMU Munich. A selection of scientific activities and memberships:
Since 2022: Fellow of the Konrad Zuse School of Excellence in Reliable AI (relAI).
Since 2021: PI in the Munich Center for Machine Learning (MCML); Member of the focus area “Next Generation AI”, Center of Advanced Studies; Coordinator of the „Clinical Open Research Engine“(CORE) established as shared, collaborative high performance computing environment at LMU Klinikum (Profs. Ingrisch, Hinske).
Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)
Abstract
Since the early 2010s, (deep) artificial neural networks started to flood the medical publication landscape, in particular in the fields of radiology, nuclear medicine and radiation oncology, by showing promising results in various tasks such as disease detection, outcome prediction, and therapy planning. Despite excellent reported performances, the deployment of such tools in clinical routine has been slower than expected due to the large variability in medical images. Physics-informed machine learning has been proposed to increase robustness and improve the generalization of deep learning models. An extension of this concept in medical informatics is anatomy- and physiology-informed machine learning. In the frame of the "Medical Information Sciences"lecture series, we present here different projects hosted at the Chair for Computer-Aided Medical Procedures at Technical University of Munich, where we use high-level knowledge of anatomy and physiology to constrain and regularize machine learning models, and as such, produce more robust medical image analysis tools.
Referent: Dr. Thomas Wendler Vidal (Technische Universität München)
Kurzbiographie
Dr. Thomas Wendler is an Electronic Engineer (Technical University Federico Santa María, Valparaíso, 2004) with a Master of Science in Medical Technolgy (Technical University of Munich, 2007) and a Ph.D. in Computer Science (Technical University of Munich, 2010). After 9 years as CTO and CEO of medical device companies (SurgicEye GmbH, OncoBeta GmbH, ScintHealth GmbH), Dr. Wendler rejoined the Chair for Computer Aided Medical Procedures at Technical University of Munich as group leader at the Interdisciplinary Research Lab at Klinikum rechts der Isar. Dr. Wendler coordinates there the activities of the chair in the fields of Medical Image Processing and Robotic Ultrasound, as well as partnerships with the university hospital in the fields of Machine Learning and Computer-Aided Surgery. Dr. Wendler has authored >40 peer reviewed, has written two book chapters and has been granted 10 patent families (EU, US, DE). He is also currently the Vice-Chair of the Working Group for Digitalization and Artificial Intelligence at the German Society of Nuclear Medicine, and Comunication Officer of the Translational Molecular Imaging and Therapy Committee at the European Association of Nuclear Medicine.
Veranstaltungsort: Kleiner Hörsaal (2.OG, Raum 048, Universitätsklinikum)
Abstract
In this lecture the origin and development of the National Intensive Care Evaluation (NICE) registry, a Dutch quality registry including all ICU patients in the Netherlands will be presented. The lecture will explain which data is included, to what extent it is possible to use routinely collected data from the EHR to fill the NICE registry, and which measures are taken to optimize data quality and reduce administrative burden. The primary aim of the NICE registry is to support ICUs in monitoring and improving quality of care. Benchmarking or audit and feedback, i.e. the strategy that intends to encourage professionals to change their clinical practice by providing professional performance based on explicit criteria or standards back to professionals in a structured manner, is an important strategy of quality registries in realising quality improvement. The lecture will include examples on the effectiveness of audit and feedback, among which an RCT on actionable indicators and a toolbox for improvement activities in the domain of pain management. The secondary aim of the NICE registry is to provide an infrastructure to research medical and methodological medical informatics research questions. If time allows, some examples out of ~180 scientific journal papers and 15 PhD theses based on the NICE registry will be presented.
Referentin: Prof. PhD Dr. Nicolette F. de Keizer (Amsterdam Unviersity Medical Center)
Kurzbiographie
Nicolette de Keizer has a master and PhD in Medical Informatics of the University of Amsterdam. She has a special interest in reusing routinely collected data to evaluate quality of care and impact of health care information systems. She is one of the founders of the National Intensive Care Evaluation (NICE) quality registry for Dutch intensive care units and of the post-graduate Master Health Informatics. She is appointed Principle Investigator in AmsterdamUMC and full professor of the University of Amsterdam. She is chair of the department of Medical Informatics, one of the leading Medical Informatics departments in the Netherlands. With her department she provides Medical Informatics training and research at BSc, MSc and PhD level.
Nicolette is internationally recognized as an expert in quality assessment, audit and feedback and standards for data reuse and she acted for years as an expert for the Dutch National ICT institute in health care and the international SNOMED CT quality committee for terminology development and maintenance. She was co-chair of several international medical informatics conferences and workshops. She is chair of the Dutch cooperation of Quality Registries and member of the Dutch data governance committee for quality registries. She supervised 25 graduated and 13 ongoing PhD students and over 40 Bachelor and Master students during scientific research projects. She published over 300 scientific research papers and book chapters, and was co-editor of the book “Applied interdisciplinary theory in health informatics: a knowledge base for practitioners”. Her H-index is 63 (Google Scholar)
Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)
Abstract
This talk gives a short overview on the literature on prognostic and predictive models for patients with Multiple Sclerosis. Besides a methodological overview, I discuss issues on the performance quality of such models and how how they reflect patient interests. Own experience will be reported gained from model development on data available within the DIFUTURE consortium as well as data from the french national MS Registry (OFSEP). At the end, we have to discuss how to cope with a very unsatisfying overall perspective:
Do we need better and more data? Do we need more advanced methods?
Do we need a deeper understanding of the disease?
Referent: Univ. Prof. Dr. Ulrich Mansmann (Direktor des IBE, Medizinische Fakultät LMU)
Kurzbiographie
Veranstaltungsort: Großer Hörsaal (2.OG, Raum 047, Universitätsklinikum)
Abstract
Digital technologies are changing the field of medicine and health. Ubiquitous medical devices can be used as point-of-care tools to measure and timely deliver personalized medical treatments across the whole continuum of care. However, this comes with a number of technical and medical challenges that will guide the research and development of digital health technologies in the coming years. In this talk I will highlight how medical automation and artificial intelligence can open new avenues to enable easy access to medical technology outside specialized clinical centers, with an example in sleep research. Intelligent user-machine interaction, automation, and machine learning approaches will have a huge impact on future medical technologies and will find applications in many medical domains, from prevention to treatment.
Referent: Prof. Dr. Walter Karlen (Institut für Biomedizinische Technik, Universität Ulm)
Kurzbiographie
Prof. Walter Karlen is professor for Biomedical Engineering at Ulm University since May 2021 where he specializes in the research on design and algorithms for medical wearables and their applications.
He was a Swiss National Science Foundation professor at the Eidgenössische Technische Hochschule Zürich (ETH Zürich) from 2014 to 2020 where he founded and directed the Mobile Health Systems Lab. Between 2005 and 2014, he held research positions at the University of Stellenbosch, South Africa, BC Children's Hospital and Child and Family Research Institute (CFRI), Vancouver, Canada; the University of British Columbia (UBC) in Vancouver, Canada; and Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Walter Karlen holds a Master degree in micro-engineering from EPFL and a Docteur ès sciences (PhD) in Computer, Communication and Information Sciences (also EPFL).
Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)
Abstract
Referent: Prof. Dr. Ulrich Sax (Institut für Medizinische Informatik, Universitätsmedizin Göttingen)
Kurzbiographie
Ulrich Sax is a medical informatician. After studying medical informatics at the University of Heidelberg, he led the department of IT and organization at St. Josef Hospital, an academic teaching hospital of the University of Regensburg.
He then worked as a research associate at the Medical Data Center of the Georg-August-University in Göttingen, where he earned his PhD in Medical Informatics in 2002, as well as a certificate in "Medical Informatics" from GI, GMDS. From 2003 to 2005, he was a postdoctoral research fellow at the Children's Hospital Informatics Program, Harvard-MIT Division of Health Sciences & Technology, and Harvard Medical School in Boston, MA, USA.
From 2005 to 2008, Sax was the head of the CIOffice Medical Research Networks in Göttingen, deputy speaker of the MediGRID project within D-Grid (BMBF), consortium leader of Services@MediGRID (BMBF), and responsible for the IT infrastructure of several medical research networks (BMBF, DFG). In 2005, he was appointed junior professor of Medical Informatics, and in 2011 he became a full professor of Medical Informatics. From 2009 to 2014, he was the head of the IT department of the University Medical Center Göttingen.
Prof. Sax is a longstanding member of the German Society for Medical Informatics, Biometry, and Epidemiology (gmds), currently active in the specialist committee "Medical Informatics (FAMI)", the management committee of the Gesellschaft für Informatik (GI) e.V., and the GMDS as well as the AG KAS of the GMDS. For many years, Prof. Sax has also been active in the TMF - Technology and Methods Platform for Networked Medical Research e.V., including as the spokesperson for the IT and Quality Management working group (AG ITQM).
As a university lecturer, Sax is committed to training the next generation of biomedical informatics professionals, as evidenced by his work with the TMF School, a joint program of the TMF, GMDS, and BVMI.
Veranstaltungsort: Großer Hörsaal (2.OG, Raum 047, Universitätsklinikum)
Abstract
Referent: Prof. Dr. med. Falk von Dincklage (Klinik für Anästhesie, Intensiv-, Notfall- und Schmerzmedizin, Universitätsmedizin Greifswald)
Kurzbiographie
Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)
Abstract
Referent: Prof. Dr. Henner Hanssen (Department für Sport, Bewegung und Gesundheit, Universität Basel)
Kurzbiographie
Veranstaltungsort: Kleiner Hörsaal (2.OG, Raum 048, Universitätsklinikum)
Abstract
Referent: Prof. Dr. med. Peter Krawitz (Institut für Genomische Statistik und Bioinformatik, Universitätsklinikum Bonn)
Kurzbiographie
Peter Krawitz studied Medicine and Physics in Munich. He continued his specialization in Medical Genetics at Charité Berlin and did a postdoc in Bioinformatics. In 2017 he was appointed a full professor at university Bonn and established the Institute for Genomic Statistics and Bioinformatics.
Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)
Abstract
Leiden University Medical Center (LUMC) is dedicated to getting AI from code to clinic. To achieve this goal, the Clinical AI Implementation and Research Lab (CAIRELab) was launched almost four years ago. During this talk, Marieke van Buchem will discuss CAIRELab’s journey from code to clinic at LUMC, using real-world examples from the past few years to illustrate the process. She will address the essential conditions for AI implementation, highlighting how they are often lacking in hospital settings and the strategies employed by CAIRELab to overcome these challenges.
Referentin: Marieke van Buchem (Leiden University Medical Center)
Kurzbiographie
Marieke van Buchem is an innovation manager at CAIRELab LUMC, where she is dedicated to identifying opportunities, launching new AI projects, and cultivating partnerships with external organizations. Furthermore, she is finishing her PhD in natural language processing (NLP) applications in healthcare. With a background in medicine and medical informatics, her work focuses on developing, validating, and implementing NLP models, both at LUMC and Stanford University.
Veranstaltungsort: Hörsaal N2045 (Fakultät für Angewandte Informatik)
Abstract
Referentin: Prof. Dr. Dr. Melanie Börries (Institut für Medizinische Bioinformatik und Systemmedizin, Universitätsklinikum Freiburg)
Kurzbiographie
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