The date for the LAST ATTEMPT exam in ITO is:
December 12, 14:00 and 15:00
NOTE: Last attempt exams are offered exclusively for degrees that prescribe a last attempt, and state that this attempt must be an oral exam.
Important announcement regarding registration process:
In order to stay more compliant to the social distancing guidelines, the registration for the exam will be conducted slightly differently.
Download, print, and fill out the examination registration form from the examination office. Fill all fields except the date and time.
Submit the document to Mr. Knopke electronically Deadline 25.11.2022.
The next available time slot will be assigned to you, and Mr. Knopke forwards the updated form to the examination office. You will be informed about the date and time for your exam.
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.
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'.
Nehmen Sie an unseren Laborexperimenten teil! Participate in our Lab experiments!
Wir führen am Samstag, 11. Juni ab 18Uhr, Mitmachexperiment im Labor 021 durch.
Anne Rother's paper, "Virtual Reality for Medical Annotation Tasks – A Systematic Review", has been accepted in Frontiers.
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.
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.