Health 4.0: How Digitisation Drives Innovation in the Healthcare Sector

Driven by networked Electronic Health Record systems, Artificial Intelligence, real-time data from wearable devices with an overlay of invisible user interfaces and improved analytics, a revolution is afoot in the healthcare industry. Over the next few years, it is likely to fundamentally change how healthcare is delivered and how the outcomes are measured. The focus on collaboration, coherence, and convergence will make healthcare more predictive and personalised. This revolution is called Health 4.0. Data portability allows patients and their physicians to access it anytime anywhere and enhanced analytics allows for differential diagnosis and medical responses that can be predictive, timely, and innovative. Health 4.0 allows the value of data more consistently and effectively. It can pinpoint areas of improvement and enable decisions that are more informed. What it also does is help move the entire healthcare industry from a system that is reactive and focused on fee-for-service to a system that is value-based, which measures outcomes and ensures proactive prevention (Thuemmler, Bai, 2017). In this paper, the authors discuss how digitisation is paving the way for data-driven innovation in the healthcare systems. They elaborate on the opportunities and challenges for all stakeholders involved and discuss how emerging technologies can help overcome the inherent rigidity of today’s healthcare ecosystem. Following on from this, the authors explain the importance of research on the actual design of smart healthcare products and product service systems of the future and the challenges faced from the viewpoint of design practice

Gradual Weisfeiler-Leman: Slow and Steady Wins the Race

The classical Weisfeiler-Leman algorithm aka color refinement is fundamental for graph learning and central for successful graph kernels and graph neural networks. Originally developed for graph isomorphism testing, the algorithm iteratively refines vertex colors. On many datasets, the stable coloring is reached after a few iterations and the optimal number of iterations for machine learning tasks is typically even lower. This suggests that the colors diverge too fast, defining a similarity that is too coarse. We generalize the concept of color refinement and propose a framework for gradual neighborhood refinement, which allows a slower convergence to the stable coloring and thus provides a more fine-grained refinement hierarchy and vertex similarity. We assign new colors by clustering vertex neighborhoods, replacing the original injective color assignment function. Our approach is used to derive new variants of existing graph kernels and to approximate the graph edit distance via optimal assignments regarding vertex similarity. We show that in both tasks, our method outperforms the original color refinement with only moderate increase in running time advancing the state of the art

Design for Health 4.0: Exploration of a New Area

Driven by networked Electronic Health Record systems, Artificial Intelligence, real-time data from wearable devices with an overlay of invisible user interfaces and improved analytics, Health 4.0 is changing the healthcare industry. The focus on collaboration, coherence, and convergence that will make healthcare more predictive and personalised. Furthermore, Health 4.0 realises the value of data more consistently and effectively. It can pinpoint areas of improvement and enable more informed decisions. What it also does is help move the entire healthcare industry from a system that is reactive and focused on fee-for-service to a system that is value-based, which measures outcomes and ensures proactive prevention. In this paper, the authors will first explore the realm of the emerging area of Health 4.0 and identify its opportunities and challenges. This includes understanding the relevant base technologies as well as the design principles for the realization of smart healthcare product, systems and product-service-systems of the future. Following on from there, the authors focus on the role of design in the specific context of healthcare

Approximating the Graph Edit Distance with Compact Neighborhood Representations

The graph edit distance is used for comparing graphs in various domains. Due to its high computational complexity it is primarily approximated. Widely-used heuristics search for an optimal assignment of vertices based on the distance between local substructures. While faster ones only consider vertices and their incident edges, leading to poor accuracy, other approaches require computationally intense exact distance computations between subgraphs. Our new method abstracts local substructures to neighborhood trees and compares them using efficient tree matching techniques. This results in a ground distance for mapping vertices that yields high quality approximations of the graph edit distance. By limiting the maximum tree height, our method supports steering between more accurate results and faster execution. We thoroughly analyze the running time of the tree matching method and propose several techniques to accelerate computation in practice. We use compressed tree representations, recognize redundancies by tree canonization and exploit them via caching. Experimentally we show that our method provides a significantly improved trade-off between running time and approximation quality compared to existing state-of-the-art approaches

EmbAssi: Embedding Assignment Costs for Similarity Search in Large Graph Databases

The graph edit distance is an intuitive measure to quantify the dissimilarity of graphs, but its computation is NP-hard and challenging in practice. We introduce methods for answering nearest neighbor and range queries regarding this distance efficiently for large databases with up to millions of graphs. We build on the filter-verification paradigm, where lower and upper bounds are used to reduce the number of exact computations of the graph edit distance. Highly effective bounds for this involve solving a linear assignment problem for each graph in the database, which is prohibitive in massive datasets. Index-based approaches typically provide only weak bounds leading to high computational costs verification. In this work, we derive novel lower bounds for efficient filtering from restricted assignment problems, where the cost function is a tree metric. This special case allows embedding the costs of optimal assignments isometrically into $\ell_1$ space, rendering efficient indexing possible. We propose several lower bounds of the graph edit distance obtained from tree metrics reflecting the edit costs, which are combined for effective filtering. Our method termed EmbAssi can be integrated into existing filter-verification pipelines as a fast and effective pre-filtering step. Empirically we show that for many real-world graphs our lower bounds are already close to the exact graph edit distance, while our index construction and search scales to very large databases

Non-Redundant Graph Neural Networks with Improved Expressiveness

Message passing graph neural networks iteratively compute node embeddings by aggregating messages from all neighbors. This procedure can be viewed as a neural variant of the Weisfeiler-Leman method, which limits their expressive power. Moreover, oversmoothing and oversquashing restrict the number of layers these networks can effectively utilize. The repeated exchange and encoding of identical information in message passing amplifies oversquashing. We propose a novel aggregation scheme based on neighborhood trees, which allows for controlling the redundancy by pruning branches of the unfolding trees underlying standard message passing. We prove that reducing redundancy improves expressivity and experimentally show that it alleviates oversquashing. We investigate the interaction between redundancy in message passing and redundancy in computation and propose a compact representation of neighborhood trees, from which we compute node and graph embeddings via a neural tree canonization technique. Our method is provably more expressive than the Weisfeiler-Leman method, less susceptible to oversquashing than message passing neural networks, and provides high classification accuracy on widely-used benchmark datasets