The selection of algorithms is a crucial step in designing AI services for real-world time series classification use cases. Traditional methods such as neural architecture search, automated machine learning, combined algorithm selection, and hyperparameter optimizations are effective but require considerable computational resources and necessitate access to all data points to run their optimizations. In this work, we introduce a novel data fingerprint that describes any time series classification dataset in a privacy-preserving manner and provides insight into the algorithm selection problem without requiring training on the (unseen) dataset. By decomposing the multi-target regression problem, only our data fingerprints are used to estimate algorithm performance and uncertainty in a scalable and adaptable manner. Our approach is evaluated on the 112 University of California riverside benchmark datasets, demonstrating its effectiveness in predicting the performance of 35 state-of-the-art algorithms and providing valuable insights for effective algorithm selection in time series classification service systems, improving a naive baseline by 7.32% on average in estimating the mean performance and 15.81% in estimating the uncertainty.

Providing a suitable rehabilitation after an acute episode or a chronic disease helps people to live independently and enhance their quality of life. However, the continuity of care is often interrupted in the transition from hospital to home. Virtual coaches (VCs) could help these patients to engage in personalized home rehabilitation programs. These coaching systems need also to be fed with procedural precepts in order to work as intended. This, in turn, relates both to properly represent the clinical knowledge (as the VC somehow replaces the formal caregivers that cannot be fully present) as well guide the patient correctly (in order to follow the medically desired procedures given the need for personalisation according to individual needs). Therefore, we outline our technical approach to deal with this. In particular, clinical pathways in terms of semi-formal procedure models in combination with machine learning components processing and powerful user interfaces providing these pathway information and feeding the VC are presented. The system is currently under testing in a participatory design phase called Living Lab. Thus, initial user feedback for further improvements is about to come.

Clinical pathways describe the treatment procedure for a patient from a medical point of view. Based on the patient’s condition, a decision is made about the next actions to be carried out. Such recurring sequential process decisions could well be outsourced to a reinforcement learning agent, but the patient’s safety should always be the main consideration when suggesting activities. The development of individual pathways is also cost and time intensive, therefore a smart agent could support and relieve physicians. In addition, not every patient reacts in the same way to a clinical intervention, so the personalization of a clinical pathway should be given attention. In this paper we address with the fundamental problem that the use of reinforcement learning agents in the specification of clinical pathways should provide an individual optimal proposal within the limits of safety constraints. Imitating the decisions of physicians can guarantee safety but not optimality. Therefore, we present an approach that ensures compliance with health critical rules without limiting the exploration of the optimum. We evaluate our approach on open source gym environment where we are able to show that our adaptation of behavior cloning not only adheres better to safety regulations, but also manages to better explore the space of the optimum in the collective rewards.