WideHealth Podcast Series

WideHealth EU Project

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Seminars and discussions on pervasive healthcare, data-driven healthcare, human factors in healthcare, and federated learning in healthcare. Promoted by Eu project WideHealth.

08/18/2022

WideHealth Seminars with Milene Teixeira, " Automating the Generation of Dialogue Managers for Healthcare"

This podcast is part of the "WideHealth Seminars". This project (widehealth.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952279 Speaker: Milene Teixeira Title: Automating the Generation of Dialogue Managers for Healthcare Abstract: Health dialogue systems are required to respect some special requirements such as predictability and reliability. Given the complexities of the health domain, these systems frequently rely on knowledge-based techniques. However, the automated generation of reliable policies is a challenging task and it remains an open problem. This talk will first present the challenges of current techniques for dialogue management of health dialogues. Then, I will present an approach that integrates semantic awareness and AI planning which was proposed with the aim of simplifying and automating the generation of health dialogue managers. Finally, I will discuss some of the results obtained from a living lab that was conducted in the context of the WideHealth project. Short Bio: Milene Santos Teixeira is a Ph.D. candidate in Computer Science at the University of Trento – Italy. Her current research focuses on the integration of AI Planning and information management techniques to address health dialogues. In 2018, she concluded her master’s degree in Computer Science at the Federal University of Santa Maria, having conducted part of her research at Brock University. Milene has also collaborated with the LASIGE group (University of Lisbon) in the context of the European project WideHealth.
06/04/2022

WideHealth Seminars with Stefan Konigorski, "StudyU: A platform for conducting digital N-of-1 trials that link personalized medicine and population health research"

This podcast is part of the "WideHealth Seminars". This project (widehealth.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952279 Speaker: Stefan Konigorski Title: StudyU: A platform for conducting digital N-of-1 trials that link personalized medicine and population health research Abstract: Traditionally, effect estimates of health interventions have been obtained from studies of large groups of individuals. However, the derived average effects do not allow meaningful insights on whether an intervention will help a given individual – which is at the center of personalized medicine. We have developed the StudyU platform (arxiv.org/abs/2012.14201) which allows evaluating the effectiveness of health interventions on an individual level by digitally designing, publishing, and conducting so-called N-of-1 trials. In N-of-1 trials, every participant compares different health interventions of interest over time. The data generated from N-of-1 trials are hence single time series, usually within complex causal graphs, and the goal is to test interpretable effects of the interventions. The power of N-of-1 trials can be further enhanced by including sensor data to measure health outcomes. In this talk, I will introduce N-of-1 trials and the StudyU platform, present some of our work on the statistical methods for the analysis and discuss how the StudyU platform might be helpful in bridging individual-level and population-level studies by aggregating multiple N-of-1 trials. Short Bio: Stefan Konigorski, PhD, is a Senior Researcher in the Digital Health & Machine Learning chair at the Hasso Plattner Institute in Potsdam Germany, where he leads the Health Intervention Analytics lab. He is also Adjunct Assistant Professor in the Genetics and Genomic Sciences Department at the Icahn School of Medicine at Mount Sinai in New York. He develops statistical and machine learning methods to derive causal effects from complex observational and experimental studies, with a specific research focus on investigating personalized health trajectories and digital health interventions by using N-of-1 trials and adaptive trials.
06/04/2022

WideHealth Seminars with Walter Maetzler, "Digital biomarkers for chronic diseases: Lessons learned"

This podcaste is part of the "WideHealth Seminars". This project (widehealth.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952279 Speaker: Walter Maetzler Title: Digital biomarkers for chronic diseases: Lessons learned Abstract: In recent years, many -wearable- digital devices have conquered the consumer and fitness market, and the medical and health industry also expected an enormous development boost from this advance. However, the results currently available on the detection of disease, its progression and therapy through such digital devices are rather disappointing. The regulatory bodies as well as many clinicians argue that this is mainly due to the fact that the development always starts from the technological, but not from the clinical, or even better, patient level. In this webinar, a large EU research project, IDEA-FAST, will be used as an example to show how informed digital and device-agnostic biomarkers can be developed for quality-of-life-relevant symptoms in various chronic diseases. Short Bio: Walter Maetzler is full professor for neurogeriatrics and deputy director of the neurology department of the University Hospital in Kiel, Germany. His main clinical interest is on Parkinson’s disease and other disorders associated with functionally relevant movement and cognitive disabilities. He leads a research group focusing on the analysis and validation of mobile sensor technology in supervised (“lab- or clinic-based”) and unsupervised (“home-based”) assessments. He is involved as principal investigator, chief clinical investigator and workpackage leader in multiple international projects investigating the potential of mobile sensor technology to improve our understanding of disease progression and treatment response in Parkinson’s disease. Examples at a European level are IDEA-FAST, Mobilise-D, Fair-Park II and Keep Control. Currently, he serves as the co-chair of the Technology task force of the Movement Disorders Society.
06/04/2022

WideHealth Seminars with Kyle Montague, "Democratising Healthcare Technologies: Wearables to cue for drooling in Parkinson’s Disease"

This podcast is part of the "WideHealth Seminars". This project (widehealth.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952279 Speaker: Kyle Montague Title: Democratising Healthcare Technologies: Wearables to cue for drooling in Parkinson’s Disease Abstract: Digital technologies are rapidly transforming the healthcare landscape. Artificial Intelligence and Machine Learning are helping researchers to discover more and more about diseases – leading to new breakthroughs in treatments and cures. In recent years we seen a surge in the use of wearable technologies to track and monitor our physical activity and psychological measurements of daily life, giving healthcare professionals a greater understanding patient of symptoms and behaviours. With much of this innovation making its way into mainstream consumer devices, there is an opportunity for a new generation of self-management technologies that not only support interactions with healthcare professionals and researchers but enable individuals to take greater control of their health. The democratisation of healthcare technologies would not only allow an individual access to their health information, but it also seeks to provide the means by which they leverage that information to enrich and enhance their lives. Providing people living with Parkinson’s the know-how and resources to transform their ideas and desires into interventions and tools is key to enabling new breakthroughs. In this talk, I will discuss an ongoing project where we are designing and developing CueBand, a wearable device specifically for people living with Parkinson’s. CueBand is an open and customisable technology to support symptoms of Parkinson’s, such as cueing for decreased automatic swallowing, while also providing research grade data collection. Together with the Parkinson’s community we want to develop CueBand to create an entirely democratised infrastructure to transform the future of healthcare technologies. Short bio: Kyle Montague is an Associate Professor in Computer and Information Sciences at Northumbria University, where he co-leads the Northumbria Social Computing (NorSC) research group. His research expertise is in Human-Computer Interaction and Digital Civics, with much of his work exploring novel applications and configurations of digital technologies to tackle societal challenges across a broad range of health and social care topics through both largescale approaches and small embedded participatory work with communities.
05/10/2022

WideHealth Seminars with Cátia Pesquita, "Knowledge Science for trust in AI-based biomedical and clinical applications"

This podcast is part of the "WideHealth Seminars". This project (widehealth.eu) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952279 Speaker: Cátia Pesquita Title: Knowledge Science for trust in AI-based biomedical and clinical applications Abstract: Biomedical and clinical applications of artificial intelligence are increasingly popular in the scientific community. However, concerns about potential bias and the lack of explainability of high-performing machine learning methods such as deep learning are limiting their adoption in practice. In this talk I explain what knowledge science is and why it is key to assess the trustworthiness of biomedical data and AI outcomes. In particular, I discuss three contexts, data, domain and user, and draw on specific examples to illustrate pitfalls and how knowledge science can overcome them. Short bio: Catia Pesquita is an Assistant Professor in Computer Science at Faculdade de Ciências da Universidade de Lisboa and a Senior Researcher at LASIGE where she leads the Health and Bioinformatics Research Line of Excellence. She has a multidisciplinary background in Biology and Computer Science, and she develops her research at the intersection between the areas of Knowledge Representation and Data Mining, with a focus on biomedical and healthcare applications. She has made internationally recognized contributions, namely in the areas of ontology-based semantic similarity and ontology alignment, winning multiple awards and competitions. She is deeply interested in how human knowledge can be communicated to computers and vice-versa.
01/25/2022

WideHealth Seminars with Mitja Lustrek, "Activity recognition with a few twists: Experiences from SHL Challenges 2019 and 2020"

Speaker: Mitja Lustrek Title: Activity recognition with a few twists: Experiences from SHL Challenges 2019 and 2020 Abstract: Sussex-Huawei Locomotion (SHL) Dataset was recorded by three people carrying four phones in different locations on their bodies for seven months. It is labelled with eight locomotion activities: still, walking, running, biking, car, bus, train and subway. It was used in three machine-learning competitions organized in collaboration with the HASCA workshop at the Ubicomp conference in 2018–20. While the 2018 challenge presented a relatively standard activity-recognition problem, 2019 and 2020 introduced a few twists. In 2019, the goal was to recognize activities with the phone in the hand location, while most of the training data was provided for the other three locations. In 2020, the goal was to recognize activities with the phone in an unknown location when carried by two different persons, while most of the training data was provided for the third person. The talk will explain how the team from Jozef Stefan Institute tackled these twists with cross-location transfer learning, machine learning to identify the unknown phone location, and trying to separate the persons with clustering. Short bio: Mitja Lustrek received his PhD degree from the Faculty of Computer and Information Science of the University of Ljubljana in 2007. He was a postdoc at the Institute for Biostatistics and Informatics in Medicine and Ageing Research in Rostock, Germany in 2010. He has worked at the Department of Intelligent Systems at Jozef Stefan Institute, Ljubljana, Slovenia ever since. He is currently employed there as a senior research associate and the head of the Ambient Intelligence Group. His main research interest is the analysis of sensor and other data related to human health and behavior using machine learning. He has been the principal investigator in a number of international research projects on this topic. He was a member of the teams scoring highly in several computer-science competition, such as the XPrize Pandemic Response Challenge and Tricorder competition, EvAAL competition and Sussex-Huawei Locomotion Challenge 2018-2020. He also served as the chair of the Slovenian Artificial Intelligence Society.
11/09/2021

WideHealth Seminars with Diogo Branco, "DataPark: Reflections from a Longitudinal Deployment of a Digital Platform for PD Monitoring"

Speaker: Diogo Branco Title: DataPark: Reflections from a Longitudinal Deployment of a Digital Platform for PD Monitoring Abstract: Designing tools that are meaningful for healthcare environments can be difficult. There is the need to take into consideration the idiosyncrasies of dealing directly with clinicians, and patients and their families. This talk will focus on the importance of doing embedded research together with the ones that will use the software designed. For that, the talk will first introduce Datapark, a web platform for continuous monitoring of Parkinson's Disease. Additionally, the motivations and steps for designing the platform will be explained. Then, the most relevant components will also be highlighted. After that, the talk will focus on the challenges, barriers, and learnings of designing a platform and maintaining long-term collaboration with clinicians. Short bio: Diogo Branco is a PhD student in Computer Science at Faculdade de Ciências da Universidade de Lisboa. His research focused on Human-Computer Interaction, particularly in health. For the last four years, he has been designing, developing, and evaluating applications, and platforms for different healthcare domains (e.g. people with Parkinson’s Disease, parents of young children with food disorders). Diogo is currently collaborating on several projects, such as IDEA-FAST (IMI) and FoodParenting.
10/08/2021

WideHealth Seminars with Hristijan Gjoreski, "Wearable Computing and its Machine-Learning Applications"

Speaker: Hristijan Gjoreski, UKIM Title: Wearable Computing and its Machine-Learning Applications Abstract: The recent technological advancements in the sensors development in their miniaturization, allowed numerous applications in the areas of wearable computing, pervasive computing, smart systems, mobile health etc. This talk will focus on few important machine-learning applications in with wearable devices such as smartphones, smartwatches, smart wristbands, etc. The first part of the talk will introduce the whole process of development of a machine learning pipeline for human activity recognition with wearable accelerometers. Numerous technical steps in the creation of the classification model will be discussed, such as: sensor data filtering, data segmentation, feature engineering, training a classification model and its evaluation. Additionally, some ideas and recent trends will be presented, including deep learning approaches, transfer learning, and unsupervised learning. The second part of the talk will focus on other ML applications in wearable computing such as: calorie expenditure estimation, fall detection, stress and arousal recognition. Each of these applications will be individually discussed and appropriate ML solutions will be presented. Short bio: Hristijan Gjoreski is Assistant Professor at the Ss. Cyril and Methodius University in Skopje, Macedonia. He finished his PhD at the Jozef Stefan Institute in Slovenia, and was postdoctoral researcher at University of Sussex, UK. His research experience is in the domains of applied Artificial Intelligence and Machine Learning. He has specialized in development of machine-learning algorithms in the areas of e-health, wearable computing, activity recognition and affective computing. He has participated more than 12 international projects, and currently is a coordinator of the European Horizon 2020 Twinning project - WideHealth. He has 3 international patent applications, has more than 100 scientific publications, and 1700 citations with h-index of 23. He has received award "Best Young Scientist" for 2016 from the President of Republic of Macedonia. He established and is organizer of the Data Science Macedonia group, with more than 1000 members. He has won 3 international machine learning competitions for human activity recognition with wearable sensors, which experience will be presented during this talk.
08/18/2021

WideHealth Seminars with Venet Osmani, "Predicting deterioration of critically ill patients"

Speaker: Venet Osmani, Fondazione Bruno Kessler Title: Predicting deterioration of critically ill patients Abstract: Intensive care units generate large quantities of data from various patient monitoring and intervention systems. Limited ability of humans to process and act on complex information also extends to intensive care clinicians, at times resulting in information overload, and consequently hindering recognition of early signs of patient deterioration. Machine learning has been touted as a possible approach to address this problem. However, enormous challenges remain despite the significant work carried out in this domain. In this talk I will provide an overview of the challenges of applying machine learning in medicine in general, and in critical care in particular, especially in comparison to traditional applications such as computer vision. Then, I will present our approach in tackling the challenge of prediction of deterioration of critically ill patients, starting from the definition of the problem, up to the methodology we employed and the results we obtained. Finally, I will also discuss this work in the context of the specific challenges identified in applying machine learning in medicine. Short bio: Venet Osmani, PhD is a senior researcher at Fondazione Bruno Kessler Research Institute. Previously, he was a lecturer at the department of Psychology and Cognitive Science at University of Trento, Italy and a visiting researcher at Georgia Institute of Technology, USA. His earlier research focused primarily on monitoring and analysing human behaviour. Specifically, using personal and environmental sensing applied to healthcare, including predicting depressive and manic episodes of bipolar patients and detecting occupational stress from smartphone sensors. Currently, the focus of his research is on analysis of clinical data (EHR) using machine learning methods to model disease and patient trajectories both for chronic conditions as well as in critical care. In this work he collaborates with some of the leading healthcare institutions in the US, including Cleveland Clinic, Mayo Clinic, MIT, as well as several leading European research institutions. He is an Expert Evaluator for European Commission (Horizon 2020 Programme), UK Medical Research Council (MRC), Swiss National Science Foundation (SNSF) and several other scientific funding institutions. Further information can be found in: http://venetosmani.com
08/18/2021

WideHealth Seminars with Orhan Konak, "IMU-Based Trajectory Image Classification for Human Activity Recognition"

Speaker: Orhan Konak, Hasso-Plattner Institute Title: IMU-Based Trajectory Image Classification for Human Activity Recognition Abstract: Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. In this talk, we will evaluate how transforming inertial sensor data into movement trajectories and further 2D heatmap images can be advantageous for HAR when data are scarce. We will briefly discuss how a performance advantage can be achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns. Short bio: Orhan Konak graduated in Computational Engineering – Mathematics at the University of Applied Science Berlin in 2010. After working as a software engineer and forecast manager for eight years, he joined HPI in 2018 as a research assistant/PhD student. His research focuses on human activity recognition, through which classification of activities contributes to lower the documentation time for nurses. He is also very passionate about football.
08/18/2021

WideHealth Seminars with André Rodrigues: "WildKey: A Privacy-Aware Keyboard Toolkit for In-The-Wild Data Collection"

Speaker: André Rodrigues, LASIGE, Universidade de Lisboa Title: WildKey: A Privacy-Aware Keyboard Toolkit for In-The-Wild Data Collection Abstract: Touch data, and in particular text-entry data, has been mostly collected in the laboratory, under controlled conditions. While touch and text-entry data has consistently shown its potential for monitoring and detecting a variety of conditions and impairments, its deployment in-the-wild remains a challenge. In this talk, we will present WildKey, an Android keyboard toolkit that allows for the usable deployment of in-the-wild user studies. WildKey is able to analyse text-entry behaviours through implicit and explicit text-entry data collection while ensuring user privacy. We will briefly present ongoing studies showcasing the wide potential of text-entry as a digital end-point for chronic and neurodegenerative diseases. Short bio: André Rodrigues is a computer science researcher focused on HCI, with a particular interest in how technology can and is leveraged in accessibility, health, and gaming. He completed his PhD with distinction and honour in January 2020 in Computer Science from Faculdade de Ciências da Universidade de Lisboa. For the last seven years, he has dedicated himself to learn, design, develop and evaluate mobile services, applications and platforms for a variety of contexts (e.g. people with visual/motor impairments, people with Parkinson's Disease), always working closely with and/or within the communities. André is currently a PostDoc Researcher at LASIGE where he is the local technical lead for IDEA-FAST (IMI), is the local Co-PI for the projects INPLAY and WideHealth. He is a long time member of SIGACCESS where he serves as the Newsletter Editor.
08/18/2021

WideHealth Seminars with Nina Reščič: XPrize Pandemic Response Challenge

Speaker: Nina Reščič, Jožef Stefan International Postgraduate School Title: XPrize Pandemic Response Challenge Short bio: Nina Reščič graduated in Applied Mathematics from the University of Ljubljana, Faculty of Mathematics and Physics in 2012. After working in the industry (Aviation and Aerospace Engineering) she began working at the Jožef Stefan Institute in 2017. She is working as a researcher and is a PhD student at the Jožef Stefan International Postgraduate School. Her research interests involve activity recognition, nutrition monitoring and mathematical modelling. She was a member of SHL Activity Recognition competition winning team in 2018, 2019 and 2020, member of the Cooking recognition challenge competition winning team and a member of the XPRIZE Response Challenge 2nd place winning team JSIvsCovid, where she was responsible for epidemiological modelling. She is a musician, receiving her BA in jazz flute at the Gustav Mahler Private Universität Klagenfurt in 2020. Abstract: XPrize Foundation organizes high-profile competitions to develop technologies that solve the world's grand challenges. The competitors of the XPrize Pandemic Response Challenge were tasked with predicting how COVID-19 infections respond to various interventions (such as lockdowns and mask usage), and to propose effective plans of such interventions for different epidemiological situations. In this talk, we will describe the solution developed by the team from the Department of Intelligent Systems at Jožef Stefan Institute, which placed second in the competition. The solution combined a classical epidemiological model with machine learning to predict future infections. Then it used algorithms inspired by biological evolution to find intervention plans with optimal trade-offs between the impact on the infections and the socio-economic cost.

12 Episodes

Seminars and discussions on pervasive healthcare, data-driven healthcare, human factors in healthcare, and federated learning in healthcare. Promoted by Eu project WideHealth.

Creator

WideHealth EU Project
Years Active

2021 - 2022
Episodes

12
Rating

Clean
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© WideHealth EU Project
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WideHealth Podcast Series