2nd Lecture Series - Season 2021

The Lecture Series continues on 27 January as WebEx Online Meetings

............................... WEBEX Access ...............................

We invite you to join this Webinars.

Meeting link:(but please check the invitation as the link may change)

Meeting number (access code): 175 952 5790
Meeting password: SujKPJ5MB52
Host key: 912098


Thanks to the CLAIRE network!

....................................... Contact .......................................

...................................... Schedule ......................................

27 January 2021
16h00 Prof Dr Juan Rafael Orozco-Arroyave

An overview of ML methods to model symptoms in movement disorders (Parkinson’s disease): from classical ML to DL

Host: Dr. Vladimir Despotovic

14 April 2021

Dr. Mauro Dragoni, Fondazione Bruno Kessler, Trento, Italy

Achieving Explainable AI Through Semantic Technologies: Challenges and Future Directions in Digital Health ( slides )

Host: Prof. Dr. Christoph Schommer

21 April 2021

FNR Covid19-projects: Humanities

FNR Covid19-projects: Humanities


Dr. Eugenio Peluso (LISER): Family Response And Well-being Effects Of Covid-19

Prof. Dr. Isabelle Astrid Albert (UL): Correlates Of Resilience In The Context Of Social Isolation In Seniors (CRISIS)

Prof Dr. Frédéric Clavert (C2DH): Ordinary life in extrarodinary times. The #covid19fr project.

Host: Prof. Dr. Christoph Schommer

28 April 2021

FNR Covid19-projects: Humanities

FNR Covid19-projects: Humanities

FNR Covis19-projects: ICT

Prof. Dr. Stefan Krebs (UL): History In The Making: #Covidmemory (COMEM)

Prof. Dr. Robin Samuel (UL): Young People And Covid-19 – Social, Economic, And Health Consequences Of Infection Prevention And Control Measures For Young People In Luxembourg (YAC)

Host: Prof. Dr. Christoph Schommer

Dr. Jorge Augusto Meira (UL):Pocket Rehab: Mhealth-based Rehabilitation Program For Patients With Cardiovascular Disease As Prevention And. Treatment Strategy For Covid-19 Victims: An International Collaborative Multicentre Research Trial

Host: Dr. Vladimir Despotovic, Dr. Jun Pang


05 May 2021
16h00 Dr.-Ing. Aureli Soria-Frisch, Director, Neuroscience BU, Starlab Barcelona

Machine Learning for Brain Health and Understanding at Starlab Neuroscience

Host: Prof. Dr. Christoph Schommer

12 May 2021
16h00 FNR Covid19-projects: ICT

Dr. Muhannad Ismael (LIST): Covid-19 Detection By Cough And Voice Analysis.

Ninghan Chen (UL): Information Diffusion In Twitter During The Covid-19 Pandemic: The Case Of The Greater Region (PandemicGR).

Dr. Joshgun Sirajzade (UL): DeepHouse - Deep Mining With The Covid-19 Data Warehouse

Host: Dr. Jun Pang, Dr. Vladimir Despotovic

19 May 2021
16h00 FNR Covid19-projects: ICT tbd
26 May 2021
16h00 FNR Covid19-projects: Life Sciences

Dr. Francesco Sarracino, STATEC: Preferences Expressed Through Twitter

Dr. Aymeric Fouquie, UL: Phylodynamic Real-time Monitoring Of Sars-cov-2 Genomes In Luxembourg (Co-PhyloDyn) and UCoVis

Lisa Veiber, UL: REBRON : Rescue - From Health Recovery To Economic Revival

02 June 2021
16h00 FNR Covid19-projects: Life Sciences

Dr. Andreas Husch, UL: AI Based Diagnosis Of Covid-19 From Ct/X-ray Imaging (AICovIX+)
Dr. Anupam Sengupta, UL: Virus-surface Interactions In Dynamic Environments (V-SIDE)
Dr. Ulrich Leopold, LIST: Towards An Integrated Geospatial Pandemic Response System.
Dr. Sascha Jung: Leveraging Systems Biology To Target Hyperinflammation In Critically-ill Covid-19 Patients

Host: Dr. Venkata Satagopam


14 July 2021


Dr Sergio Martinez-Cuestra

AstraZeneca R&D and the University of Cambridge, UK

Developing new genomics technologies to map DNA epigenetic modifications and damage in humans, parasites and cancer

Host: Prof. Dr. Thomas Sauter

16 September 2021 16h00 Prof Rudi Balling, Director of the Luxembourg Centre for Systems Biomedicine (LCSB), Lxuembourg.

Wicked Problems: mission impossible or next frontier? (Abstract)  ('Rudi Balling Goodbye' Lecture Series)

Webex event:

23 September 2021  16h00 Prof. David Leigh, Sir Samuel Hall Chair of Chemistry, Department of Chemistry, University of Manchester, UK.

Making the tiniest machines (Abstract)('Rudi Balling Goodbye' Lecture Series)

Webex Event:

30 September 2021  16h00  Prof. Ortwin Renn, Scientific Director at the Institute for Advanced Sustainability Studies (IASS), Potsdam, Germany.

Behavioral adaptations to the COVID-19 crisis: What is here to stay? (Abstract)('Rudi Balling Goodbye' Lecture Series)

Webex Event:

07 October 2021  16h00  Dr. Sara-Jane Dunn, Google DeepMind, London, UK.

Biological Computation in Stem Cells (Abstract)('Rudi Balling Goodbye' Lecture Series)

Webex Event:

14 October 2021  16h30  Prof. Edith Heard, Director General of the European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.

Launching a new era of biology to understand « life in context (Abstract)('Rudi Balling Goodbye' Lecture Series)

Hybrid event:

Register a seat at MSA via Eventbride

17 November 2021  16h00  Dr. habil. Jürgen Landes, Ludwig-Maximilians-Universität München

 Causal Inference in Medicine in the Real World (Abstract);

Online Event:

24 November 2021  16h00  Prof. Dr. med. Jochen Klucken, LCSB, UL

 "Integration of AI into the life of patients and healtcare providers: how does it work and what does it change? (Abstract);

Online Event:




Prof Dr Rafael Orozco-Arroyave, University of Antioquia + Adjunct researcher at the Pattern Recognition Lab, University of Erlangen

An overview of ML methods to model symptoms in movement disorders (the case of Parkinson’s disease): from classical ML to DL

There exist different movement disorders with different origin. Parkinson’s disease (PD) is one of those disorders and appears due to the progressive death of dopaminergic neurons in the substantia nigra of the mid-brain. Diagnosis and monitoring of PD patients is still highly subjective, time consuming and expensive. Existing medical scales used to evaluate the neurological state of PD patients cover many different aspects, including activities of daily living, motor aspects, speech and depression. This makes the task of automatically reproducing experts’ evaluation very difficult because several bio-signals and modeling methods are required to produce clinically acceptable/practical results. This talk tries to show part of the way that has been traveled since about ten years considering different bio-signals (e.g., speech, gait, and handwriting) and methods of Machine Learning and the relatively new topics of Deep Learning (DL) with the aim to find suitable models for PD diagnosis and monitoring. Results with classical feature extraction and classification methods will be presented and also experiments with CNN and LSTM architectures will be discussed.

Biography: Juan Rafael Orozco-Arroyave was born in Medellín, Colombia in 1981. He is an Electronics Engineer from the University of Antioquia (2004). From 2004 to 2009 he was working for a telco company in Medellín, Colombia. In 2011 he finished the MSc. degree in Telecommunications from the Universidad de Antioquia. In 2015 he finished the PhD in Computer Science in a double degree program between the University of Erlangen (Germany) and the University of Antioquia (Colombia). Currently Juan Rafael Orozco-Arroyave is an Associate Professor at the University of Antioquia and adjunct researcher at the Pattern Recognition Lab at the University of Erlangen.


Prof Dr Sergio Martinez-Cuestra

AstraZeneca R&D and the University of Cambridge

Developing new genomics technologies to map DNA epigenetic modifications and damage in humans, parasites and cancer

DNA is much more than ACGT. I will give a broad overview of the experimental and computational technologies and challenges used to map and understand epigenetic modifications, damage and structures in DNA. We are just beginning to understand how changes to the chemistry of DNA play a key role in the development of diseases (e.g. cancer), life cycle control and development of eukaryotic diseases. Using recent projects from my own collaborative research with talented chemists and biologists I will introduce the state of the art in the areas of DNA epigenetics from a computational perspective. 


Sergio is starting a research group in bioinformatics exploring fundamental mechanisms of protein degradation and DNA epigenetics at the interface between industry and academia. He builds from his early training in laboratory chemistry and biochemistry, through a PhD and postdoc appointments in bioinformatics and cheminformatics developing projects in collaboration with wet-lab colleagues sharing his time between the AstraZeneca headquarters and the University of Cambridge. Outside research, Sergio teaches bioinformatics at university, contributes as an editor of the emerging journal Frontiers in Bioinformatics and supports the growth of the European life science data infrastructure ELIXIR. Beyond science, he has a passion for team sports, languages and culture/gastronomy. 


Dr Mauro Dragoni, Fondazione Bruno Kessler, Trento, Italy

Achieving Explainable AI Through Semantic Technologies: Challenges and Future Directions in Digital Health 

Abstract The interest in Explainable Artificial Intelligence (XAI) research area has dramatically grown during the last few years. The main reason is the need of having systems that beyond being effective are also able to describe how a certain outcome has been reached and to present it in a comprehensive manner with respect to the target users. The Digital Health domain is a pioneering scenario where XAI strategies have been designed and implemented. In this talk, I would go through the milestones of this paradigm and I will discuss the role of semantic technologies. Then, I will present how such strategies have been applied to the Digital Health domain, and which are the challenges that have to be tackled in the near future.

Short Bio Mauro Dragoni is a research scientist at Fondazione Bruno Kessler within the Process and Data Intelligence (PDI) Research Unit. He received his Ph.D. in Computer Science from the University of Milan in 2010. His main research topics concern knowledge management, information retrieval, and machine learning with a focus on the design and development of real-world prototypes for enabling the access of both industries and people to research. Since 2015, he has been involved in activities dedicated to bring AI solutions within the Digital Health area. He has been involved in a number of national and international research projects and he co-authored more than 100 scientific publications in international journals, conferences, and workshops. Beyond AI, he is a motorsport (real and simulated) passionate and a certified personal trainer from the Italian National Olympic Committee.




Dr Aureli Sonia-Frisch, Director, Neuroscience BU, Starlab Barcelona

Machine Learning for Brain Health and Understanding at Starlab Neuroscience


The convergent development of different technologies is bringing the understanding of the brain, both on healthy and pathological condition, further than ever before. The confluence of Wearables, Neurotechnologies, Augmented and Virtual Reality, Serious Gaming, Data Science, Machine Learning and Artificial Intelligence are a game changer in the way we study brain functionality, make use of it for interacting with the environment, and treat mental and neurological disease. The talk will deal with the combination of Neurotechnologies, Machine Learning and Artificial Intelligence in different Digital Brain Health applications developed at Starlab Neuroscience. Digital markers of brain function will lead in the near future to improved diagnostic, drug discovery, risk analysis, and interactivity. We will show developed methodologies for: stratified performance evaluation of classifiers in operational conditions for Parkinsons’ risk assessment, differential diagnosis in ADHD based on Reservoir Computing, and new treatment outcome prediction in Coma patients. I will go over the technical challenges we faced to develop these applications, but also over some insights that influence the applicability of pure academic data science in the real world.


Dr.-Ing. Aureli Soria-Frisch received the MSc from the Polytechnic University of Catalonia– UPC (1995), and the PhD from the Technical University Berlin. Between 1996 and 2005 he worked at the Fraunhofer IPK (Berlin), as research scientist and project leader. After 3 years as Visiting Professor at the Universitat Pompeu Fabra, he joined Starlab in 2008. He is the Director of the Neuroscience Business Unit since beginning 2017. His research interest is focused on the fields: computational intelligence for data analysis, and machine learning for electrophysiological signal analysis. He has authored 20 journal papers, seven book chapters, and over 60 conference papers. He has been Project Manager of the FP7 HIVE project, where the Starstim early prototype for transcranial electrical stimulation was developed, Project Coordinator of the H2020 FET Open LUMINOUS project on the clinical study of consciousness, and PI of the 2 MJFF grants for the development of Machine Learning PD biomarkers. 


see also the annoncement of the Luxembourg Centre for Systems Biomedicine HERE



Talk 1 :
Prof. Rudi Balling: Wicked problems: Mission impossible or the next frontier?

Today’s problems cannot  be solved any more by a single person, organisation or discipline. Therefore, interdisciplinary cooperation and systems approaches have been widely adopted to cope with the complexity and uncertainty of our world. Big data and artificial intelligence penetrate almost every single aspect of our lives. However, we now realise the limitations of such a data driven toolbox. There are problems that seem to defy a solution. These are sometimes called “wicked problems”, that apparently have many possible, but no real solution. Each wicked problem is unique and involves social or cultural issues, touching upon a diversity of individual or societal values. As a result, a much deeper understanding of the stakeholders involved is necessary. I will give an overview and discuss some of the challenges related to wicked problems.



Talk 2 :
Prof. David Leigh: Making the tinest machines

Abstract: Perhaps the best way to appreciate the technological potential of controlled molecular-level motion is to recognise that molecular machines lie at the heart of every biological process. Nature has not repeatedly chosen this solution for achieving complex task performance without good reason. In stark contrast to biology, none of mankind’s myriad of present day technologies exploit controlled molecular-level motion in any way at all: every catalyst, every material, every pharmaceutical, all function through their static or equilibrium dynamic properties. When we learn how to build artificial structures that can exploit molecular level motion, and interface their effects directly with other molecules and the outside world, it will potentially impact on every aspect of functional molecule and materials design. An improved understanding of physics and biology will surely follow. 



Talk 3:

Prof. Ortwin Renn: Behavioral adaptations to the COVID-19 crisis: What is here to stay?

Abstract: The rise of populism and corresponding political resonance in Europe and the USA is precarious because it is threatening democracy and science. Many political decision-making processes are based on evidence and expert knowledge. This is particularly true for systemic risks such as the recent COVD-19 pandemic. However, many political and social actors discredit wellgrounded knowledge as “fake news” or conspiracy theory; they bring alternative facts and truth into play. The public is often confused and lacks orientation. The paper will stress the importance of robust knowledge stemming from science, expertise and practical experience.



Talk 4:

Dr. Sara-Jane Dunn: Biological Computation in Stem Cells

Abstract: Experimental biology has proven our ability to induce cell identity via differentiation or reprogramming, offering huge promise for medicine and the study of development. Yet despite this wealth of research, an explanation of how cell state conversions arise remains fragmentary. Ideally, we would like to understand the complex, dynamic interplay of genetic components that manifests as cell fate conversions. To address this gap, computational analyses can be combined with mathematical modelling to interrogate experimental data and generate testable hypotheses on how a program of genetic interactions governs cell identity. In this talk, I will demonstrate how interdisciplinary approaches have revealed the biological program governing fate decisions in stem cells, and indeed, could be used in other domains to expose the regulatory programs that drive cellular decision-making more broadly.


Talk 5:

Prof. Edith Heard: Launching a new area of biology to unterstand 'Life in context'

Abstract: coming soon




Dr. habil. Jürgen Landes: Causal Inference in Medicine in the real world

Abstract: In an ideal world, we draw causal inferences based on well-controlled randomised experiments carried out by independent and impartial experts. In reality, we cannot infer whether drugs cause adverse reactions from such data. Randomised clinical trials are too short and study too few patients to observe rare (and possibly severe) adverse reactions. These trials are often carried out by employees of a multi-billion dollar industry incentivised to sell their products. In this talk, I will i) present a Bayesian framework, E-Synthesis, which > facilitates causal inference from (possibly biased) real world evidence and ii) discuss its applicability in the real world.

Bio: After obtaining a PhD in mathematical (probabilistic) logic and two brief postdocs I turned to philosophy in 2012. I have since been interested in evidence and confirmation of (causal) hypotheses. In particular, I'm much interested in how to assess the causal hypothesis that a drug causes an adverse reaction based on real world evidence. I also work on Maximum Entropy inference (finite and infinite domains) and Bayesian epistemology of science.



Prof. Dr. med. Joachim Klucken: Integration of AI into the life of patients and healtcare providers: how does it work and what does it change?

Wearable sensors and smartphone apps are increasingly providing information - usually referred to as "data" - from the real world environment of patients home. Data-driven medicine has generated a profound area of research aiming to provide better diagnostic and therapeutic applications. The anticipated effects for patients as well as the market for new digital healthcare services seems endless. Using the example of wearable sensors on gait analysis in Parkinson's disease the presentation will show how to translate data-driven research into data-driven medicine. The technical, medical and societal aspects of the different development phases of data-driven medicine will be presented, as well as the difference between AI-driven innovations and AI-driven medical applications. The goal is to better understand how to bring smart algorithms and real-world data driven innovations into healthcare applications that in the end are beneficiary for patients, healthcare provider and society.

Digital medicine is a new field Medicine that aims to understand how patient-centered technology can be used in everyday medical practice, and which evidence assessment is needed to not only understand the medical benefits of healthcare technologies, but also their patient- and social acceptance and economical efficacy. Here, the major goal lies in clinical studies for healthcare technologies providing evidence for their medical, social, ethical and legal benefit as well as economic efficiency ultimately generating a concept of “clinical validation of healthcare technologies and services”. Prof. Klucken earned his MD in Laboratory Medicine and specialized in Neurology. He finished his habilitation thesis in 2009 in translational neuroscience in Parkinson’s disease including work at the Massachusetts Institute for Neurodegenerative Diseases, Harvard Medical School, Boston, USA on neurodegenerative processes in Parkinson’s disease. In 2004 he also started translational research projects in the field of medical technology (m/eHealth) applying sensor-based motion detection in movement disorders. Jointly with engineers and data-scientists, he developed novel gait-specific instrumented movement analysis concepts for Parkinson’s disease, multiple sclerosis, osteoarthritis, sarcopenia, oncology and healthy well-being of the elderly. From 2008 until 2021 he was a senior physician and PI at the Movement Disorder Unit (Department of Molecular Neurology, University Hospital Erlangen, Germany) and developed sensor-based gait analysis for patients with movement disorders. From 2018-2021 he also lead a group at Fraunhofer IIS, Erlangen, Germany with the focus on developing digital health pathways that enable technology integration into healthcare workflows. In 2019 he also established a contract research organization (Medical Valley Digital Health Application Center - dmac) supporting personalized healthcare technologies in order to get access to the German healthcare market. Within the scientific community he initiated and leads the task-force “Telehealth Services” of the Germany Parkinson Society (DPG), he is a founding member of the task-force “technology” of the international movement disorder society (MDS), and he is the chairman of the advisory board “e-health, telematics methods) of the Professional Association of German Neurologists (BDN). On political and societal level including patient-support groups he promotes the use of mobile healthcare technologies and innovations for comprehensive digital healthcare services, clinical studies and care. In addition, he participates in spin-offs/start-ups in the field of sensor-based movement analysis, and is advising several pharmaceutical companies and healthcare insurances/services on the topic of wearable derived objective outcomes.




1st Lecture Series - Season 2020

The Lecture Series takes place from 03 March 2020 - 10 March 2020 ON-Site and

                                              from 23 September - 18 November as WebEx Online Meetings

............................... WEBEX Access ...............................

We invite you to join this Webinars.

Meeting link:

Meeting number (access code): 163 267 6786
Meeting password: UL-AIForHCWebs

Host key: 478625 


Thanks to the CLAIRE network!

....................................... Contact .......................................

  • Prof Dr Christoph Schommer (Info)
  • Prof Dr Thomas Sauter (Info)
  • Dr Jun Pang (Info)
  • Prof Dr Daniel Abankwa (Info)

....................................... Support .......................................

...................................... Schedule ......................................

03 MARCH 2020


Learning Centre, Room LH 2.02

Prof Dr Andreas Maier, University Erlangen-Nürnberg, Germany (Info)

Known Operator Learning - An Approach to increase Trust in Deep Learning for Medical Image Processing (Abstract)

Chairman: Christoph Schommer

10 MARCH 2020


MSA, Room 4.510

Prof Dr Jim Torresen, University of Oslo, Norway (Info)

Older People Care and Mental Health Treatment by Adaptive Technology (Abstract)

Chairman: Thomas Sauter

Wed, 23 SEPTEMBER 2020




Dr Julio Saez-Rodriguez, University Heidelberg, Germany (Info)

Helping machine learning to help us in personalized medicine (Abstract)

Chairman: Thomas Sauter

Wed, 28 OCTOBER 2020




Dr Victor Vicente Palacios, Philips Salamanca, Spain (Info)

Artificial Intelligence applied to Cardiology (Abstract)

Chairman: Jun Pang

Wed, 4 NOVEMBER 2020




Prof Dr Ute Schmid, cand.PhD Bettina Finzel, University Bamberg, Germany (Info)

Learning from Mutual Explanations for Cooperative Decision Making in Medicine (Abstract)

Chairman: Christoph Schommer

Wed, 11 NOVEMBER 2020



Dr. med. Markus Lingman (Info), Region Halland and Prof. Dr. Mattias Ohlsson (Info), Halmstad University, Sweden

Information driven healthcare in Halland (Abstract)

Chairman: Daniel Abankwa

Wed, 18 November 2020



Prof Dr Georg Dorffner, Medical University Vienna, Austria (Info)

It's all about the ground truth - how to prove that AI can outperform experts (Abstract)

Chairman: Jun Pang




Dr Mark Wernsdorfer, Research Associate, Laboratory University Hospital Leipzig, Germany (Info)

Machine Learning in the Clinical Decision Support System for Laboratory Medicine AMPEL (Abstract)

Chairman: Christoph Schommer




Prof Dr Jeroen van den Hoven, TU Delft, The Netherlands (Info)

Ethics of AI in Healthcare (Abstract)

Chairman: Daniel Abankwa


..................................... Speakers .....................................

am Prof Dr Andreas Maier, University Erlangen-Nürnberg, Germany

Prof. Dr. Andreas Maier studied Computer Science, graduated in 2005, and received his PhD in 2009. From 2005 to 2009 he was working at the Pattern Recognition Lab at the Computer Science Department of the University of Erlangen-Nuremberg. His major research subject was medical signal processing in speech data. In this period, he developed the first online speech intelligibility assessment tool - PEAKS - that has been used to analyze over 4.000 patient and control subjects so far. From 2009 to 2010, he started working on flat-panel C-arm CT as post-doctoral fellow at the Radiological Sciences Laboratory in the Department of Radiology at the Stanford University. From 2011 to 2012 he joined Siemens Healthcare as innovation project manager and was responsible for reconstruction topics in the Angiography and X-ray business unit. In 2012, he returned the University of Erlangen-Nuremberg as head of the Medical Reconstruction Group at the Pattern Recognition lab. In 2015 he became professor and head of the Pattern Recognition Lab. Since 2016, he is member of the steering committee of the European Time Machine Consortium. In 2018, he was awarded an ERC Synergy Grant "4D nanoscope". Current research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators.

jt Prof Dr Jim Torresen , University of Oslo, Norway

Prof Dr Jim Torresen is a professor at University of Oslo where he leads the Robotics and Intelligent Systems research group. He received his M.Sc. and (Ph.D) degrees in computer architecture and design from the Norwegian University of Science and Technology, University of Trondheim in 1991 and 1996, respectively. He has been employed as a senior hardware designer at NERA Telecommunications (1996-1998) and at Navia Aviation (1998-1999). Since 1999, he has been a professor at the Department of Informatics at the University of Oslo (associate professor 1999-2005). Jim Torresen has been a visiting researcher at Kyoto University, Japan for one year (1993-1994), four months at Electrotechnical laboratory, Tsukuba, Japan (1997 and 2000) and a visiting professor at Cornell University, USA for one year (2010-2011). His research interests at the moment include artificial intelligence, ethical aspects of AI and robotics, machine learning, robotics, and applying this to complex real-world applications. Several novel methods have been proposed. He has published over 200 scientific papers in international journals, books and conference proceedings. 10 tutorials and a number of invited talks have been given at international conferences and research institutes. He is in the program committee of more than ten different international conferences, associate editor of three international scientific journals as well as a regular reviewer of a number of other international journals. He has also acted as an evaluator for proposals in EU FP7 and Horizon2020 and is currently project manager/principal investigator in four externally funded research projects/centres. He is a member of the Norwegian Academy of Technological Sciences (NTVA) and the National Committee for Research Ethics in Science and Technology (NENT) where he is a member of a working group on research ethics for AI.

mark Dr Mark Wernsdorfer, Research Associate, Laboratory Medicine of the University Hospital Leipzig

Dr Mark Wernsdorfer studied philosophy and computer science at the University of Bamberg. After his studies, he did his doctorate at the professorship Cognitive Systems on the question of consciousness in artificial systems. He then provided technical support to the Centre for Heritage Conservation Studies and Technologies. He has been a research associate at the AMPEL project of the Laboratory Medicine of the University Hospital Leipzig since October 2019. The project supports treating physicians by automatically recording laboratory values of patients, recognizing them as critical and, if necessary, reporting them to medical staff. His research interests include the philosophy of mind and artificial intelligence. In the intersection of both, he is particularly concerned with the structural prerequisites that a system must have in order to be considered intelligent, as well as the associated external prerequisites that it must have in order to be perceived as conscious by others.

bf cand.PhD Bettina Finzel, University Bamberg, Germany

Bettina Finzel Bettina Finzel is a research assistant in the BMBF funded project Transparent Medical Expert Companion (TraMeExCo). She has a master as well as a bachelor of science both in Applied Computer Science from the University of Bamberg. She is mainly interested in comprehensible and interactive machine learning approaches for the medical domain. Bettina Finzel is active in measures to engage female high school students in computer science.

us Prof Dr Ute Schmid, University Bamberg, Germany

Ute Schmid is professor for Cognitive Systems at University of Bamberg. She holds a diploma in psychology and a diploma in computer science, both from Technical University Berlin (TUB), Germany. She received both her doctoral degree (Dr. rer.nat.) and her habilitation from the Department of Computer Science of TUB. Her research focus is on interpretable and human-like machine learning, inductive programming, and multimodal explanations. Current research projects are on interpretable and explainable machine learning for medical image diagnosis (BMBF – TraMeExCo), for facial expression analysis (DFG – PainFaceReader), and for detecting irrelevant digital objects (DFG – Dare2Del in the priority program Intentional Forgetting). Ute Schmid is a fortiss resesarch fellow for Inductive Programming. She is engaged to bring AI education to school and holds many outreach talks to give a realistic picture of the benefits and risks of AI applications.

gd Prof Dr Georg Dorffner, Medical University Vienna, Austria

Georg Dorffner is associated professor at the Section for Artificial Intelligence & Decision Support at the Medical University of Vienna, of which he is currently also the head. At the same university he also holds the position of the curriculum director of the university's master's programme in Medical Informatics. He received Master's degrees in Computer Science and Communication Engineering from the Vienna University of Technology in 1985 and a PhD in Computer Science from Indiana University in 1989. In 1994 he received tenure ("Habilitation") in Artificial Intelligence in Medicine for his work in novel tyspes of neural networks in clinical applications. His research has included machine learning, in particular neural networks, since the beginning of his career in the 1980s with a particular focus on time series and signal processing. From 2002 to 2014 he was the founding managing director of the company The Siesta Group, which - as a spin-off from an EU-funded project - among others successfully developed algorithms for the automated analysis of sleep signals (polysomnogrpahy) with human-level performance, which eventually was taken over by Philips for clinical commercialisation. From 2010 to 2014, Georg Dorffner was also a part-time senior managament at Philips Home Helathcare Solutions. Since the late 1990s he has frequently been advisor and/or evaluator for various EU funding programmes, in particular in FET (Future Emerging Technologies).

Markus Dr. med. Markus Lingman, Region Halland, Sweden

Dr Markus Lingman is a specialist physician, has a background in Industrial Engineering and Management, and a PhD in medical sciences. He has done postdoctoral research in collaboration with Harvard. He is a consulting cardiologist and member of the board of directors for Halland hospital group. He is the Chief Strategy Officer and leads Region Halland’s Centre for Information Driven Care (CIDD).

Mattias Prof. Dr. Mattias Ohlsson, Halmstad University, Sweden

Prof Dr Mattias Ohlsson has a PhD in theoretical physics and is professor of Information Technology at Halmstad University. He is also professor in Theoretical Physics at Lund University, with a specialization in machine learning for medical diagnostics. He has done a postdoctoral research stay at the Technical University of Copenhagen. Today, he heads the information driven healthcare research within Halmstad University’s Centre for Applied Intelligent Systems Research (CAISR).

victor Dr Victor Vicente Palacios, Philips Salamanca, Spain

Dr Victor Vicente Palacios is currently Clinical Data Scientist at Philips Healthcare (Salamanca, Spain); he is part of an artificial intelligence research group based on the University Hospital of Salamanca (Spain) that focuses its investigations on applying machine learning and deep learning to the clinical practice, mainly applied to cardiology. He has a master's degree in mechanical engineering from the Technical University of Madrid, and a Ph.D. in multivariate statistics from the University of Salamanca. He is also a former alumnus of the “Data Science for Social Good” program (University of Chicago) and coordinator of Datalab USAL (Data Science student research group at the University of Salamanca).

julio Dr Julio Saez-Rodriguez, University Heidelberg, Germany

Julio Saez-Rodriguez is Professor of Medical Bioinformatics and Data Analysis at the Faculty of Medicine of the University of Heidelberg, and director of the institute of computational biomedicine. He is also a group leader of the EMBL-Heidelberg University Molecular Medicine Partnership Unit, and a co-director of the DREAM challenges ( to crowdsource computational systems biology. He obtained his M.S. in Chemical Engineering in 2001, and a PhD in 2007 at the University of Magdeburg and the Max-Planck-Institute. He was a postdoctoral fellow at Harvard Medical School and M.I.T., and a Scientific Coordinator of the NIH-NIGMS Cell Decision Process Center from 2007 to 2010. From 2010 until 2015 he was a group leader at EMBL-EBI with a joint appointment in the EMBL Genome Biology Unit in Heidelberg, as well as a senior fellow at Wolfson College (Cambridge). From 2015 to 2018 he was professor of Computational Biomedicine at the RWTH University Medical Hospital in Aachen, Germany. He is interested in developing and applying computational methods to acquire a functional understanding of signaling networks and their deregulation in disease, and to apply this knowledge to develop novel therapeutics. Current emphasis in his group is on use of single-cell technologies, multi-omics integration, and understanding multi-cellular communication. More information at

vh Prof Dr Jeroen van den Hoven, TU Delft, The Netherlands

Prof Dr Jeroen van den Hoven is university professor and full professor of Ethics and Technology at Delft University of Technology and editor in chief of Ethics and Information Technology. He is currently the scientific director of the Delft Design for Values Institute. He was the founding scientific director of 4TU.Centre for Ethics and Technology (2007-2013). In 2009, he won the World Technology Award for Ethics as well as the IFIP prize for ICT and Society for his work in Ethics and ICT. Jeroen van den Hoven was founder, and until 2016 Programme Chair, of the program of the Dutch Research Council on Responsible Innovation. He published Designing in Ethics (Van den Hoven, Miller & Pogge eds., Cambridge University Press, 2017) and Evil Online (Cocking & Van den Hoven, Blackwell, 2018) He is a permanent member of the European Group on Ethics (EGE) to the European Commission. In 2017 he was knighted in the Order of the Lion of The Netherlands.

..................................... Abstracts .....................................

Prof Dr Andreas Maier, University Erlangen-Nürnberg, Germany

Known Operator Learning - An Approach to increase Trust in Deep Learning for Medical Image Processing

We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We demonstrate a reduced a maximal error bound for deep nets by inclusion of prior knowledge. Furthermore, we also show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from CT image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such the concept is widely applicable for many researchers in physics, imaging, and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, medical imaging, and signal processing. 

Prof Dr Jim Torresen, University Oslo, Norway

Older People Care and Mental Health Treatment by Adaptive Technology

Our mental state to a large extent impacts our well-being. Unfortunately, for many people, it fluctuates during lifetime and results in various degree of mental disorders. The most common one is depression but there exist a number of other ones as well like social anxiety, Attention Deficit Hyperactivity Disorder (AHDH), and bipolar disorders. The treatment is on the other hand often long-lasting and much dependent on therapeutic follow-up. Little technology is available to measure mental state and provide any automatic support and treatment. This is what we at the University of Oslo together with clinical collaborators in Bergen in Norway are addressing in the project INtroducing personalized TReatment Of Mental health problems using Adaptive Technology (INTROMAT).

We work in the project with sensor-data collected from mental health patients and controls using mobile phones and sensor watches. We apply state-of-the-art machine learning methods to train models that can classify and foresee how the mental state is changing through time. Data comes in many formats like motion, speech, mobile phone usage, and more. Having some indication of the future development of the brain´s mental state is helpful for providing a user with self-help as well as support to the therapist. In parallel, we are implementing technology to increase health safety for older people living by themselves at home. This is by using ambient sensor technology on a mobile robot platform to remotely sense the state of a person to be able to warn the caregiver when any abnormal or emergence situation has occurred. This has relevance to mental health since the mental state can impact the well-being and risk of emergency situations. In this talk, three important aspects of these projects will be presented including the user design perspective, sensing technology and possible treatments that together target to contribute improved mental health. The talk will also address many of the ethical issues like privacy, security and safety that should be considered when developing such technology.

Dr Mark Wernsdorfer, Research Associate, Laboratory Medicine of the University Hospital Leipzig

Machine Learning in the Clinical Decision Support System for Laboratory Medicine AMPEL

Laboratory medicine is essential for the diagnosis, therapy, and management of patients. The timely and appropriate consideration and interpretation of laboratory results is critical to, for example, review the choice of treatment or respond quickly to sudden changes in the patient's condition. Laboratory diagnostics, in general, provide relevant and high-quality information about the condition of the patient. Clinical Decision Support Systems (CDSS) assist in the digital collection of large amounts of such information, its automated delivery to the appropriate medical staff, and the development of treatment methods. This helps to avoid medical errors due to mistakes or misinterpretations. The aim of the AMPEL project at the University Hospital Leipzig is to implement and evaluate a CDSS for laboratory diagnostics.
At the core of the system is the conversion of laboratory results into more condensed information, enabling better and faster treatment. From the data available to this system, machine learning methods can deduce critical patient states, biomarkers that are difficult or expensive to collect, or medical diagnoses from similar cases in the past. Medical staff can then be automatically alerted to critical constellations of biomarkers that require medical intervention, costly and time-consuming analyses can be performed in a more targeted manner, and diagnoses of rare diseases can be proposed that could otherwise have been overlooked.
A transparent machine learning system has been developed that can predict diagnoses with high precision and recall. The generated models are validated by specialists and checked for medical plausibility. This ensures that the tests for models are representative and practice-oriented. A connection to the productive patient database of the University Hospital Leipzig enables the reactive adaptation of models to specific changes of the local patient population.
The current status of the AMPEL project is presented. The methods used and the problems resulting from their application to certain data sets are described by means of an exemplary case. Methods of result analysis, as well as means to improve the system and to extend its applicability in the future, are described.

Prof Dr Ute Schmid, University Bamberg, Germany, cand.PhD Bettina Finzel, University Bamberg, Germany

Learning from Mutual Explanations for Cooperative Decision Making in Medicine

Medical decision making is one of the most relevant real world domains where intelligent support is necessary to help human experts master the ever growing complexity. At the same time, standard approaches of data driven black box machine learning are not recommendable since medicine is a highly sensitive domain where errors may have fatal consequences. In the talk, we will advocate interactive machine learning from mutual explanations to overcome typical problems of purely data driven approaches to machine learning. Mutual explanations, realised with the help of an interpretable machine learning approach, allow to incorporate expert knowledge in the learning process and support the correction of erroneous labels as well as dealing with noise. Mutual explanations therefore constitute a framework for explainable, comprehensible and correctable classification. Specifically, we present an extension of the inductive logic programming system Aleph which allows for interactive learning. We introduce our application LearnWithME which is based on this extension. LearnWithME gets input from a classifier such as a Convolutional Neural Net‘s prediction on medical images. Medical experts can ask for verbal explanations in order to evaluate the prediction. Through interaction with the verbal statements they can correct classification decisions and in addition can also correct the explanations. Thereby, expert knowledge is taken into account in form of constraints for model adaptation.

Prof Dr Georg Dorffner, Medical University Vienna, Austria

Recent successful applications of AI - in particular of deep learning in imaging, signal processing and related fields - have demonstrated that machines are often able to achieve human-level performance or even outperform human experts in classification tasks for diagnostic support. Such proofs are also at the heart of regulatory decisions of whether AI systems are fit for clinical use as a medical device. In this talk I will present several applications from our own work, as well as comparable approaches from literature, highlighting the different ways of how expert performance can be measured as the benchmark for AI validation. The central question in each application is whether a "ground truth" exists and in what form, e.g. through a gold standard comparative method or through expert opinion itself. This poses different challenges on validation that are often isufficiently addressed in literature. The talk will conclude with a more general discussion on whether and how AI sytsems can replace or, as it is often feared, replace medical personnel based on their proven performance level.

Dr. med. Markus Lingman Prof. Dr. Mattias Ohlsson

Information driven healthcare in Halland

Region Halland in Sweden is the main healthcare provider for the county of Halland (about 330 000 inhabitants). Region Halland realized early on the potential impact of information driven healthcare; using data and data analytics to improve the healthcare system. Region Halland have during the last ten years developed and maintained a comprehensive healthcare data infrastructure covering clinical and administrative information pertaining to every consumer in Halland of healthcare with public funding. This means approximately 500 000 patients treated in Halland now and in the past and includes all the Region’s care delivery units and also the pharmacies. Work is ongoing to include the municipalities, who have responsibility for e.g. elderly care.
Halmstad (Regional Capital of Halland) University, and in particular CAISR (Centre for Applied Intelligent Systems Research), have had a longstanding and seamless collaboration with Region Halland with the focus on applying AI and machine learning towards information driven healthcare solutions. This work also includes collaborations with international partners (e.g. Harvard Medical School and Brigham Women’s Hospital in Boston).
Over the last years, Region Halland has been able to cut costs in the healthcare service at the same time as the population has grown and there has been a substantial increase in patient arrivals to the emergency departments. Also medical quality of care has improved. The efficiency improvement has been achieved e.g. by reducing hospital bed days without affecting occupancy levels, by decreasing the admission rates to the hospital, and increasing the fraction patients that can be discharged early. Many of these achievements were enabled by using information driven healthcare, by introducing data analytics and better prognostics for the management of the healthcare system. For 2019, Region Halland is one of only two regions in Sweden that do not show a large economical deficit in the healthcare service. Several regions are flagging for large staff layoffs in their healthcare systems for 2020.
Detailed and comprehensive care data, together with modern AI and analysis tools, play an important role in delivering effective care by facilitating healthcare providers to to create actionable insights and take better informed decisions. What is also required is a methodology and organization on how to systematically work with information driven improvement work around quality and productivity where the goal is to understand how the patient is affected in the healthcare system. Region Halland have developed a model for organization, working methods and a nine-step process on how to go from idea, to follow-up of an implementation of a change in the health care system. The model gives the decision maker a powerful tool to choose the initiatives that give the best results at the system level. This includes creating agile multidisciplinary teams around system issues and use the nine-step process for a data-driven improvement work that considers all the necessary aspects including production, quality and economy with the highest possible degree of detail.
We will present how Region Halland works with information driven healthcare, both how to find insights and to get them implemented, and show research projects and results that have emerged through the collaboration between Region Halland and CAISR at Halmstad University. We also present ongoing work in developing methods and infrastructure for distributed machine learning, such that medical databases located at different healthcare provides can be utilized when creating AI and machine learning solutions related to information driven healthcare.

Dr Victor Vicente Palacios

Artificial Intelligence applied to Cardiology

Artificial Intelligence (AI) is becoming highly advanced in different disciplines and our current challenge is to transfer all of this development to the medical field and specifically to cardiology. Examples of AI using Machine Learning (ML) or Deep Learning (DL) are becoming more and more common in cardiology. In this talk, I will present the evolution of the contributions of AI to the different application areas of cardiology such as cardiac arrhythmias, ischemic heart disease, cardiac imaging, and others. Furthermore, I will introduce the projects we have developed and the ones we are currently developing in our research group at the Hospital of Salamanca (Spain), as well as show some applications.

Dr Julio Saez-Rodriguez

Helping machine learning to help us in personalized medicine

One area where artificial intelligence is expected to have a major impact in the health area is by developing algorithms that help us provide the right drug for each patient, that is, for personalized medicine. In this talk I will discuss our work applying machine learning on large pharmaco-genomic screenings in cell lines to build predictive models. Integration of this data with prior knowledge on signaling pathways and transcription factors provides biomarkers and offer hypotheses for novel combination therapies. Our own analysis as well as the results of a crowdsourcing effort (as part of a DREAM challenge) reveals that prediction of drug efficacy is far from accurate, implying important limitations for personalised medicine. An important aspect that deserves further attention is the dynamics of signaling networks and how they response to perturbations such as drug treatment. I will present how cell-specific logic models, trained with measurements upon perturbations, can provides new biomarkers and treatment opportunities not noticeable by static molecular characterisation. In summary, I will advocate that combining the right data with biological knowledge will be important to build predictive models for personalized medicine.

Prof Dr Jeroen van den Hoven

Ethics of AI in Healthcare

The attention for ethical problems of AI has rapidly increased in the last two years. At the same time the geo-political debates about digital sovereignty have become very prominent. Different parts of the world have different different images of man and models of society, and have therefore different views on how AI can and should be used and applied, which ethical issues are serious and which ones trivial. I will provide an overview of the ethical debates about AI, discuss the main ideas of Designing AI applications for moral Values, Responsible Innovation in Health Care with AI, proposals for Trustworthy AI as proposed by the High Level Group of the European Commission and indicate what some of the fundamental ethical problems of AI are that will be with us for quite some time.