News
'Best paper award' at AIME 2022
Miro Schleicher has received the Marco Ramoni best paper award at the 20th Artificial Intelligence in Medicine (AIME) conference for his paper 'When can I expect the mHealth user to return? Prediction meets time series with gaps' (Miro Schleicher, Rüdiger Pryss, Winfried Schlee and Myra Spiliopoulou).
This work is within the frame of the UNITI project that encompasses machine learning methods for choosing the best treatment for each tinnitus patient. Treatments have an mHealth component, which assists the users towards self-empowerment and daily management of their disease. However, mHealth apps demand self-discipline; some users give up or interact very irregularly. The proposed method learns from the data of each user and from the absence of data, and it predicts if and when a user will start interacting again with the app.
20th International Conference on Artificial Intelligence in Medicine (AIME 2022)
20th International Conference on Artificial Intelligence in Medicine (AIME 2022), Halifax
The KMD team was present with two scientific contributions:
- 'When can I expect the mHealth user to return? Prediction meets time series with gaps' by Miro Schleicher, Rüdiger Pryss, Winfried Schlee and Myra Spiliopoulou: the new machine learning method analyses the behaviour of users towards an mHealth app, learns from the absence of data and predicts if and when a user will start using the app again.
- 'Discovering Instantaneous Granger Causalities in Non-stationary Categorical Time Series Data' by Noor Jamaludeen, Vishnu Unnikrishnan, André Brechmann, and Myra Spiliopoulou: the new machine learning method analyses the data of an auditory category learning experiment, and it identifies characteristic patterns that distinguish between learners and non-learners.
Myra Spiliopoulou, together with Panos Papapetrou (Univ Stockholm) also presented a tutorial on 'Machine learning for complex medical temporal sequences'. Her part was on 'Dealing with Missingness/Gaps'.
Lange Nacht der Wissenschaft 2022
Nehmen Sie an unseren Laborexperimenten teil! Participate in our Lab experiments!
Wir führen am Samstag, 11. Juni ab 18Uhr, Mitmachexperiment im Labor 021 durch.
Mehr Informationen:
https://wissenschaft.magdeburg.de/programm/ovgu/2596-forschung-trifft-crowdsourcing
Paper accepted in Frontiers in Virtual Reality
Anne Rother's paper, "Virtual Reality for Medical Annotation Tasks – A Systematic Review", has been accepted in Frontiers.
https://www.frontiersin.org/articles/10.3389/frvir.2022.717383/full
Presentation of Topics for the summer Term 2022
On 12.04.2022 at 13:00 s.t. in room G10-337.
We will present topics for:
- Scientific Team Projects (Master)
- IT-Softwareprojects (Bachelor)
UPDATE 12 April:
Slides of topics:
- Myra Spiliopoulou (includes administrative information)
- Uli Niemann
- Christian Beyer (password-protected)
Application deadline: 21 April 2022 12:00.
Tutorial Machine learning for complex medical temporal sequences
In this tutorial, we focus on sequential forms of health-related data – spatial trajectories, panel data from longitudinal studies, time series signals (such as ECGs), event sequences (such as sequences containing EHR events) and mHealth data. We elaborate on the questions that medical researchers and clinicians pose on those data, and on the instruments they use. We elaborate on what questions are asked with those instruments, on what questions can be answered from those data, on ML advances and achievements on such data, and on ways of responding to the medical experts’ questions about the derived models. Furthermore, we emphasize the need for interpretable and explainable models that can inspire trust and facilitate informed decision making. Towards this goal we elaborate on actionable models and counterfactual explanations for sequential medical data, and discuss how to apply them for the interpretation of black-box models, such as deep learning architectures.
Best student research award "Rudolf Kruse Award"
Anne Rother received the "Rudolf Kruse Award" (student research award) 2021. Congratulations!
Anne Rother, Uli Niemann, Tommy Hielscher, Henry Völzke, Till Ittermann, and Myra Spiliopoulou (2021). Assessing the difficulty of annotating medical data in crowdworking with help of experiments. PLOS ONE 16(7): e0254764. https://doi.org/10.1371/journal.pone.0254764