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Archiv: SS 2023

Vortragsreihe ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Information Sciences

SCHULUNG
BIOINF ? Universit?t Augsburg

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Veranstaltungsflyer | Veranstaltungsplakat

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Die Veranstaltungen finden im Sommersemester 2023 jeweils dienstags ab 17:30 Uhr wechselnd im Gro?en und Kleinen H?rsaal des Universit?tsklinikums und im H?rsaal N2045 an der FAI in Pr?senz statt. Am Universit?tsklinikum gelangen Sie ¨¹ber die Wegweiser "H?rs?le" ab der Eingangshalle ¨¹ber der Rolltreppe direkt zu den H?rs?len (Gro?er H?rsaal: 2.OG Raum 047 / Kleiner H?rsaal: 2.OG Raum 048).

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Die Veranstaltungen werden au?erdem per Livestream an folgende ?bertragungsorte ¨¹bertragen:

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  • Die Vortr?ge am Universit?tsklinikum werden in H?rsaal N2045 an der FAI ¨¹bertragen.
  • Die Vortr?ge im H?rsaal N2045 werden in den Besprechungsraum des IDM (Gutenbergstr. 7, 86356 Neus?? - 1. OG, Raum 01.B001) ¨¹bertragen.

Die Vortr?ge richten sich an ein interessiertes Fachpublikum und werden in englischer Sprache gehalten.

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Bei der Bayerischen Landes?rztekammer (BL?K) sind au?erdem f¨¹r jeden Einzeltermin 2 Fortbildungspunkte im Rahmen der Continuing ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Education (CME) beantragt. Interessierte ?rztinnen und ?rzte k?nnen sich ¨¹ber eine Nachricht an office.bioinf@informatik.uni-augsburg.de vorab f¨¹r eine Teilnahme registrieren, die im Anschluss an den jeweiligen Termin best?tigt wird.

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Im Vorlauf der Vortr?ge wird zudem die M?glichkeit zur Wahrnehmung einer pers?nlichen Sprechstunde mit der oder dem Vortragenden des Tages angeboten, um sich bspw. ¨¹ber wissenschaftliche Fragen, Themen oder Kooperationsm?glichkeiten auszutauschen. Bei Interesse bitten wir Sie, sich hierf¨¹r rechtzeitig ¨¹ber eine Nachricht an office.bioinf@informatik.uni-augsburg.de anzumelden.

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Im Folgenden finden Sie den aktuellen Ablaufplan der Vortragsreihe mit weiterf¨¹hrenden Informationen zu den einzelnen Vortr?gen:

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Veranstaltungsort: Gro?er H?rsaal (2.OG, Raum 047, Universit?tsklinikum)

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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.

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Referent: Prof. Dr. Michael Ingrisch (Clinical Data Science in Radiology, LMU Klinikum M¨¹nchen)

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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, ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ 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: H?rsaal N2045 (Fakult?t f¨¹r Angewandte Informatik)

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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 "ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Information Sciences"lecture series, we present here different projects hosted at the Chair for Computer-Aided ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Procedures at Technical ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ 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.
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Referent: Dr. Thomas Wendler Vidal (Technische Universit?t M¨¹nchen)

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Kurzbiographie

Dr. Thomas Wendler?is an Electronic Engineer (Technical ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ Federico Santa Mar¨ªa,?Valpara¨ªso, 2004) with a Master of Science in ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Technolgy (Technical ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ of Munich, 2007)?and a Ph.D. in Computer Science (Technical ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ 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 ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Procedures at Technical ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ 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 ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical 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 ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿icine, and Comunication Officer of the Translational Molecular Imaging and Therapy Committee at the European Association of Nuclear ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿icine.

Veranstaltungsort: Kleiner H?rsaal (2.OG, Raum 048, Universit?tsklinikum)

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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.

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Referentin: Prof. PhD Dr. Nicolette F. de Keizer (Amsterdam Unviersity ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Center)

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Kurzbiographie

Nicolette de Keizer has a master and PhD in ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Informatics of the ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ 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 ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ of Amsterdam. She is chair of the department of ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Informatics, one of the leading ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Informatics departments in the Netherlands. With her department she provides ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Informatics training and research at BSc, MSc and PhD level.

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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: H?rsaal N2045 (Fakult?t f¨¹r Angewandte Informatik)

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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?

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Referent: Univ. Prof. Dr. Ulrich Mansmann (Direktor des IBE, ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿izinische Fakult?t LMU)

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Kurzbiographie

Prof. Dr. Ulrich Mansmann is head of the Department of ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Information Sciences, Biostatistics, and Epidemiology at the Ludwig-Maximilian¡¯s ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ Munich. His background is a PhD in Mathematics (1990, Technical ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ Berlin). In 1991 he started to work as assistant professor at the ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Center of the Free ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ in Berlin working in Clinical Epidemiology with main focus on clinical trials and prognostic studies. In 2000 he moved to the ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ of Heidelberg and cooperated with colleagues from the German Cancer Center (also located in Heidelberg). He was member of the German National Genome Research Network (NGFN). In January 2005, Prof. Mansmann was invited as head of Department at the Institute of ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Information Science, Biometrics, and Epidemiology (IBE) at the ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ of Munich (LMU). The IBE represents methodological research on a wide spectrum of ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Sciences: Public Health, Epidemiology, Clinical Epidemiology, as well as Molecular ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿icine. This position allowed deepening and extending activities in the fields of research which are at the heart of his interest. Especially, his experiences in prognostic oncological research combined with activities in the field of Bioinformatics as well as clinical trials were helpful to enter the fields of translational medicine as well as personalized medicine. Prof. Mansmann is also spokesman of the LMU¡¯s program in Public Health and Epidemiology.? From 2012 to 2020 he served as a DFG collegiate. Since 2017, Professor Mansmann joins the federal ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ical Informatics Initiative (MII).

Veranstaltungsort: Gro?er H?rsaal (2.OG, Raum 047, Universit?tsklinikum)

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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.

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Referent: Prof. Dr. Walter Karlen (Institut f¨¹r Biomedizinische Technik, Universit?t Ulm)

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Kurzbiographie

Prof. Walter Karlen is professor for Biomedical Engineering at Ulm ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ 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 Eidgen?ssische Technische Hochschule Z¨¹rich (ETH Z¨¹rich) 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 ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ of British Columbia (UBC) in Vancouver, Canada;? and Ecole Polytechnique F¨¦d¨¦rale de Lausanne (EPFL), Switzerland. Walter Karlen holds a Master degree in micro-engineering from EPFL and a Docteur ¨¨s sciences (PhD) in Computer, Communication and Information Sciences (also EPFL).

Veranstaltungsort: H?rsaal N2045 (Fakult?t f¨¹r Angewandte Informatik)

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Abstract

The research project ?GenoPerspektiv¡® of the UMG (ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿ ÍþÄá˹¶Ä²©ÓÎÏ·_ÍþÄá˹¶Ä²©app-¡¾¹ÙÍø¡¿icine G?ttingen) focuses on the handling of genomic high-throughput data, taking into account perspectives from the fields of clinical practice, ethics, law, and biomedical information technology. The project aims to address the various challenges and opportunities associated with the use of genomic data in healthcare. Within the realm of medical informatics, Professor Sax is specifically engaged in a subproject that delves into the application of information and health technology in medicine. This entails studying the integration of digital systems and tools to enhance healthcare delivery, data management, and decision support. Therefore, he conducts research on data privacy and security in telematics, with a particular focus on healthcare settings, where he explores the implementation and utilization of electronic patient records and personal health records in translational research. His work aims to optimize the functionality and interoperability of these digital platforms, facilitating seamless information exchange a