News
Tutorial @ PAKDD 2013
Myra Spiliopoulou and Georg Krempl will present a Tutorial on Mining Multiple Threads of streaming Data at PAKDD 2013, April 14-17, Gold Coast, Australia.
Stream mining is a mature area of research. However, several applications that require adaptive learning from evolving data do not seem to fit to the conventional stream mining paradigm. For example, a bank grants loans to customers and uses their data for model learning; the label (loan-payed-back YES or NO) arrives some years later, though, during which years the market may have changed drastically. Is this a stream mining problem? How many streams are there? We can distinguish between the stream of customers and the stream of their labels, which arrive with a time lag of years.
As another example, a hospital monitors patients with chronical diseases that come (ir)regularly to the hospital and undergo different tests; the streams of medical recordings and of signals (EEG, fMRI) can be used for learning. The hospital wants to learn a model on how the patients' health evolves in response to the disease and to medications. This problem seems completely different from the previous one, albeit streams of data are there in both cases.
In this tutorial, Myra Spiliopoulou and Georg Krempl bring together research advances on model learning and adaption for dynamic applications that collect and analyze different sources of dynamic data. In the introductory part of the tutorial, they present the classic stream mining paradigm and summarize the challenges being investigated in the state-of-the-art research.
'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.
Tutorial Mining and multimodal learning from complex medical data
The proliferation of medical data and applications has increased the need for extracting useful knowledge that can be effectively used by the healthcare domain experts. The motivation of this tutorial is to address the complexity of medical data with specific focus on their temporal nature. While earlier tutorials in both AIME as well as other related venues such as KDD and ECML/PKDD have explored the application and utility of machine learning on medical data, there has yet been limited focus on the challenges emerging from the sequential and temporal nature of such data, as well as on the need for trust by the medical practitioners.